THE IMPACT OF HIGH-FREQUENCY

TRADING REGULATORY REGIMES

ON EUROPEAN MARKET QUALITY

BY

Francis Joseph Musella

Submitted to Princeton University

Department of Economics

In Partial Fulfillment of the Requirements for the A.B. Degree

April 15, 2014

i

Abstract

I examine the impact of three different national regimes for regulating high-frequency

trading: a licensure regime in Germany, establishment of an HFT order cancellation tax

in Italy, and a combined order cancellation tax and general financial transactions tax in

France. Using GARCH and EGARCH models, I find that the German regime

significantly reduces the persistence of volatility shocks. The French regime significantly

reduces long-run volatility, reduces the size of bid-ask spreads, and increases intraday

volatility. It also weakly reduces volatility persistence and the sensitivity of bid-ask

spreads to volatility. The Italian regime significantly reduces long-run volatility,

increases the persistence of volatility shocks, increases intraday volatility, and reduces

the sensitivity of bid-ask spreads to volatility. It weakly increases the size of bid-ask

spreads. The French and German regimes were associated with a significant reduction in

trade volume, which was not the case with the Italian regime. Overall, I find that the three

regimes improve market quality more often than they detract from it.

ii

Acknowledgements

I want to thank my advisor, Professor Stephen Redding, for helping shape my

ideas into a concrete thesis. My JP advisor, Professor Valentin Haddad, taught me to take

a rigorous approach to econometrics, and I owe much of my knowledge of ARCH models

and demonstrating exogeneity to his teachings. Professor Harrison Hong gave me my

first exposure to theoretical finance, which proved intensely useful when combing the

existing literature. I also want to thank Professor Hank Farber, who inspired my passion

for econometrics while giving me the tools to pursue significant original research.

Additionally, Bobray Bordelon and Todd Hines proved invaluable in helping me gather

data for this thesis, which would have been an impossible undertaking otherwise.

My experience as a Princeton senior would not have been the same without the

support of my friends. I owe thanks to a wide variety of people. To Tierney Kuhn, my

intellectual partner in crime, who inspired me to push through every obstacle life could

throw my way. To Bryton Shang, whose experience in High-Frequency Trading at

Eladian Partners helped inspire this thesis. To Anthony Paranzino, whose endless games

of billiards kept me sane in the face of the abyss. To James di Palma-Grisi, who reviewed

a draft of my thesis over WaWa hoagies and coffee. To Christiana Lloyd-Kirk, whose

keen eye for the English language helped me extract meaning from the complex. And to

all the members of Colonial Club, who made the experience of writing my thesis far less

painful than imagined.

Finally, I owe the ultimate debt of gratitude to my parents, Marianne Musella and

Joseph Whittick, for giving me their love, support, and gametes. They have given me a

joyous life for the past twenty-two years, and I wouldnt be here without them.

iii

Contents

Abstract i

Acknowledgements ii

1. Introduction 1

2. Literature review: Benefits of HFT 4

2.1 Volatility reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Improved price discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Liquidity improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3. Literature review: Drawbacks of HFT 8

3.1 Volatility increases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Quote stuffing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3 Risk events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4. Existing and proposed regulatory mechanisms 13

4.1 Transactions tax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.1.1 2012 French implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.1.2 2013 Italian implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.1.3 Planned 2015 Eurozone FTT . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.2 Licensure regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.2.1 German HFT Act of 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3 Price limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.4 Trading halts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.5 Maximum order-to-trade ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.6 Proposed novel ideas yet to be implemented . . . . . . . . . . . . . . . . . . . . . 20

4.6.1 Minimum order durations/holding periods . . . . . . . . . . . . . . . 20

4.6.2 Quoting obligations for market makers . . . . . . . . . . . . . . . . . . 20

iv

5. Methodology 21

5.1 Impact on daily trade volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2 Impact on long-run volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5.3 Models of intraday volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.4 Models of liquidity provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.5 Potential threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6. Data 31

7. Hypothesis 33

8. Results and analysis 34

8.1 Change in trade volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

8.2 Change in long-run volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

8.3 Change in intraday volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

8.4 Change in liquidity provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

9. Conclusion 42

References 46

Appendix 1: Results of EGARCH model regressions 51

Appendix 2: Results of GARCH model regressions 53

Appendix 3: Results of AR(1) model regressions 55

Appendix 4: A day in the life of duopolistic market-making HFTs 56

Pledge 59

1

1. Introduction

High-Frequency Trading (HFT) is a form of algorithmic trading in which orders are

rapidly placed, modified, or cancelled in accordance with market changes occurring at the

millisecond or microsecond level. HFT has seen widespread adoption in the early twenty-

first century. While HFT trade volumes were minimal at the start of the century, by 2008,

HFT accounted for a majority of all daily trading volume in the United States. Though

HFT volumes peaked in 2009, HFT still comprises roughly half of daily trading volume

in the US, and 30-40% of trading volume in Europe and Canada.1

Multiple different trading strategies can be classified as HFT. The first strategy,

market making, is as old as financial markets themselves. A trader simultaneously places

offers at the best bid and ask price, updating the offers several times per second. The

trader effectively sells liquidity: he takes on inventory risk and is compensated by

capturing the bid-ask spread. Depending on exchange policies, the trader may also

receive a small rebate from the exchange for providing liquidity via limit orders.

Another major HFT strategy is arbitrage between exchanges. Arbitrage consists of

strategies designed to profit from different asset prices across different exchanges. This

might consist of buying assets on exchanges where the price is low and reselling those

assets on exchanges where the price is high. Arbitrage can also take advantage of the

expected relationship between two related assets: buying an Exchange Traded Fund while

shorting its components would produce risk-free profits if the ETF traded at a discount.

These price disparities tend to be short-lived, meaning that HFTs must have access to the

1 Kumar et al, 2011, p. 2

2

fastest available data feeds to profit from them. Arbitrage strategies help ensure the

convergence of prices across different exchanges.

More sophisticated HFTs may try to predict price movement, instead of making

money on spreads or arbitrage. The simplest price prediction strategy is event-based

trading. HFTs will attempt to be the first to trade after important economic events for

example, the release of the monthly unemployment report. Since economic events drive

prices, there are tremendous profits to be made by being the first to trade. Event-driven

strategies gained attention in September 2013, when an unknown trader placed more than

a billion dollars of buy orders on a major gold ETF, coinciding with the release of the

Federal Reserves minutes. The order was placed at exactly 2 PM, suggesting that the

report was leaked early: it would take 7 milliseconds for information to travel at the

speed of light from Washington, D.C. to Chicago.2 As long as event-driven strategies are

profitable, there will be incentives to leak information and commit insider trading.

Another strategy to predict price movements is analyzing order book depth. A

relative abundance of sell orders may imply that prices will move down, and a flood of

buy offers suggests that prices will move up. This is a form of momentum trading, since

it relies solely on past price movements to predict future price movements. However,

strategies rely on order book depth are vulnerable to exploitation by other HFTs. The

practice of spoofing involves placing phantom orders away from the best bid and offer

to create artificial depth in the order book. The phantom orders encourage other traders to

place real orders on the other side of the book, which the abusive HFT can trade against

at a profit. The abuses are considered serious enough that the Securities and Exchange

2 Foxman, 2013

3

Commission has begun acting to shut down trading shops that engage in spoofing, most

recently Visionary Trading in 2014.3

By far the most controversial form of HFT consists of ultra-low latency trading.

Ultra-low latency strategies depend on accessing data faster than other market

participants. This might involve colocation, where traders place their computers in the

same data centers as the exchanges computers to reduce communication time to

practically zero. Low latency strategies also involve the use of proprietary

communication technology, such as private fiber optic cables or microwave

communication networks. These traders generally spend significant amounts of money on

direct data feeds, instead of relying on the slower Securities Information Processor (SIP)

feed, which is publicly available. The difference in speed between the two data feeds

about 25 milliseconds creates frequent price dislocations that HFTs can trade on and

consistently profit from.

The two-tiered structure of information is troubling from an efficiency standpoint.

In order to be competitive, firms must spend millions of dollars on data and

infrastructure, raising significant barriers to entry for smaller firms. In Flash Boys,

Michael Lewis relates the story of Spread Networks, a company that charged firms

upwards of $10 million for access to a single fiber optic cable connecting New York and

Chicago. Due to the winner-take-all nature of HFT, firms were forced to buy access to

the line, which was the fastest in existence at the time. By reducing the number of firms

that can afford to be competitive, proprietary data feeds and communication networks

may harm market efficiency. After all, why would Low-Frequency Trading firms with

imperfect information attempt to compete against HFT firms with perfect information?

3 Lynch, 2014

4

In addition to efficiency concerns, it is possible that ultra-low latency strategies

violate existing US securities law. At issue is whether colocation and high-speed data

feeds give HFT firms selective access to material nonpublic information. If so, these

practices would be considered insider trading. In 2013, the FBI, SEC, and New York

Attorney Generals office launched a joint investigation into the practice of selling direct

feeds to firms engaging in HFT.4 This investigation has the potential to dramatically alter

the trading landscape in the United States.

Designing an optimal HFT regulatory policy is challenging. An ideal policy must

mitigate the harms of HFT while preserving any social benefits HFT provides. This thesis

will examine the effectiveness of HFT regulatory regimes recently imposed in three

countries: Germany, Italy, and France. Policy efficacy will be measured by the

preservation or improvement of market quality, specifically volatility and bid-ask

spreads. I will attempt to answer two research questions. First, what effect did the

regulations have on market quality? Second, what is the optimal HFT regulatory policy?

2. Literature review: Benefits of HFT

2.1 Volatility reduction

Several studies have found that HFT activity reduces intraday volatility. Hasbrouck and

Saar examine quotes at the millisecond level, using strategic runs, or sequences of

consecutive order modifications, as a proxy for HFT activity. They find that increased

HFT activity is associated with a decrease in short-term volatility. Of course, the

4 Geiger & Mamudi, 2014

5

direction of causality is uncertain: it might be the case that volatility chases HFTs away

instead of HFTs reducing volatility with their presence. Hasbrouck and Saar acknowledge

this problem and attempt to address it by using trading activity on other exchanges as

instrumental variables. Their results are robust to their IV specification, though they

hinge on the validity of their instruments.

Brogaard employs a different methodology, which more closely matches the

methodology of my own paper. He conducts an event study, examining the impact of the

SECs temporary ban on short sales imposed immediately after the collapse of Lehman

Brothers. Using a policy change helps eliminate the problem of endogeneity, because the

change in HFT activity during the sample period can be attributed to the short sale ban

and not to a rise in volatility. Brogaard finds that decreases in HFT activity caused

increases in short-run volatility, though the effect was only significant at the 10% level

due to a small sample size.

Brogaard also tests the volatility hypothesis by examining a counterfactual: how

would market quality change if there were no HFTs? He constructs a hypothetical price

path by removing all trades in which HFTs participated, then compares the volatility of

prices on the real price path to the volatility on the hypothetical one using one-minute

intervals. He finds that the HFT price path has significantly less volatility than the non-

HFT price path. However, the size of the effect was less than 1% of total volatility.

Furthermore, when examining the volatility of individual stocks instead of the aggregate

market, only one of the 120 analyzed stocks saw a significant decrease in volatility

attributable to the presence of HFTs.5

5 Brogaard, 2010, p. 75

6

2.2 Improved price discovery

Hendershott and Riordan study the impact of Algorithmic Trading (AT) on price

discovery6, using data from the 30 DAX stocks trading on the Deutsche Borse during

January 2008. They find that AT initiated trades have a more than 20% larger permanent

price impact than human trades. In other words, AT is more likely to lead to successful

price discovery than human-generated trades. However, their dataset does not distinguish

between high-frequency and low-frequency AT strategies. It may be the case that low-

frequency strategies, such as an institutional investor placing a large order, contribute

more to price discovery than high-frequency or market-making strategies.

Brogaard replicates the results of Hendershott and Riordan with high-frequency

data taken from the NASDAQ during 2008-2010. Using a Vector Autoregression (VAR)

model, Brogaard finds that an innovation in HFT tends to lead to a 34% greater

permanent price change than does a trade by a non-HFT.7 This effect is present in both

short-run prices and long-run prices. Unlike Hendershott and Riordan, Brogaards dataset

includes information about whether orders were placed by HFTs, which increases the

validity of his results.

Aitken, Cumming, and Zhan examine the impact of HFT on end-of-day price

dislocations. There exist strong financial incentives to manipulate the closing price on

certain days because closing prices are used at the option expiry dates to determine the

value of options, and can strongly influence portfolio allocations at the end of each fiscal

quarter. The question is whether HFTs reduce or increase these dislocations. The authors

find that the presence of HFTs reduces the probability of an EOD price dislocation by

6 Roughly speaking, price discovery is the ability of traders to incorporate new information in asset pricing.

7 Ibid, p. 33

7

about 21%. To address possible concerns about endogeneity, the authors use press

releases announcing new colocation agreements as a proxy for HFT activity.

2.3 Liquidity improvements

Hasbrouck and Saar examine the impact of HFT on two measures of liquidity: bid-ask

spreads and order book depth. They find that an increase in HFT activity significantly

reduces bid-ask spreads while increasing order book depth, both of which can be

considered improvements in liquidity and market quality. As in their analysis of

volatility, the effect is only significant over the aggregate market, and not for any

individual stocks.

Hendershott, Jones, and Menkveld analyze the impact of Algorithmic Trading on

bid-ask spreads on the New York Stock Exchange. They use the 2003 introduction of

automated quote dissemination (autoquote) as an exogenous instrument that should

increase AT. They find a significant negative relationship between AT volumes and bid-

ask spreads, suggesting that ATs increase the provision of liquidity.

Hendershott and Riordan examine the quoting behavior of ATs versus human

traders on the Deutsche Borse. Although algorithmic and human traders each supply 50%

of liquidity in realized trades, they find that ATs are significantly more likely than

humans to quote at the best bid and ask prices. The effect size is roughly one additional

hour per day with the best offer. However, the authors do not attribute this difference to

improved liquidity provision. Instead, they cite two factors relating to the nature of ATs.

First, ATs tend to place much smaller offers than human traders, due to inventory

aversion. Second, human quotes are more likely to be stale and adversely selected

8

against. The quoting disparity is particularly strong when spreads are high, suggesting

that ATs only supply liquidity when it is expensive.

At-Sahalia and Saglam develop a theoretical model of HFTs. In the model, an

HFT has a modified mean-variance utility function, with his profit increasing in the

expected bid-ask spread and decreasing in his inventory and inventory aversion. Latency

is modeled with a Poisson process: the trader has a constant probability of receiving a

signal about the future direction of prices, and quotes based on his signal. As latency

decreases, the profitability of quoting increases, as does the fill rate of the LFTs market

orders. Speed thus increases the provision of liquidity by reducing risk aversion in market

makers.

3. Literature review: Drawbacks of HFT

Although many authors have written about the successes of HFT, their findings are not

unanimous. Several studies find that HFT actually increases market volatility, and other

authors allege that HFTs withdraw liquidity in times of high volatility, thereby increasing

bid-ask spreads and exacerbating market crashes. The latter is a particularly strong

concern, since sound markets require robustness to negative shocks.

3.1 Volatility increases

Zhang studies the impact of HFTs on long-run volatility. Using the same methodology as

Hendershott, Jones, and Menkveld, Zhang uses the 2003 introduction of autoquote on the

NYSE as an exogenous instrument. He studies all equities covered by the Thomsen

9

Reuters Institutional Holdings database over the period from 1985 to 2009. Zhang finds

that volatility significantly increases as the market share of HFTs increases. He also

analyzes the price discovery process by using analyst forecasts and earnings surprises,

finding that prices are significantly more likely to overreact to news when HFT is

widespread. He attributes this result to the interplay between two different types of HFTs:

event-driven and momentum-driven investors. Event-driven HFTs respond first to new

information. Momentum traders, who disregard fundamentals, then respond to the price

change generated by the first HFT group, magnifying the original price movement. This

implies that new information is effectively double-counted, as both types of traders

respond to it for different reasons.

Jarrow and Protter develop a theoretical model of latency arbitraging HFTs. The

model is highly stylized and uses several strong assumptions such as continuous time,

zero bid-ask spreads, and zero-latency trading. Nevertheless, they find that the

introduction of high-frequency trades both increases market volatility and generates

abnormal profit opportunities for the high-frequency traders at the expense of the

ordinary traders.8 These abnormal profits may represent a pure arbitrage, which would

contradict the efficient markets hypothesis. The authors describe latency arbitrage as a

transfer of wealth from mutual funds, pension funds, and other financial institutions, to

the firm doing high-frequency trading.9 It is unclear whether their results would hold if

the model were generalized to include bid-ask spreads or imperfect liquidity, or if a

similar general equilibrium model were used.

8 Jarrow & Protter, 2012, p. 14

9 Ibid, p. 5

10

3.2 Quote stuffing

Egginton, Van Ness, and Van Ness examine the practice of quote stuffing, which

occurs when HFTs submit excessive orders with the goal of causing latency to confound

competitors algorithms. Using data from the NYSEs Trade and Quote database, they

define an episode of quote stuffing as a one-minute period that includes quoting activity

exceeding the mean level of activity over the trailing 20 days by at least 20 standard

deviations. After accounting for major events like corporate earnings releases, they find

that 24,733 instances of quote stuffing occurred during 2010.10

They find that these

episodes of quote stuffing caused decreased liquidity, increased volatility, and increased

trading costs. Once again, however, there is the problem of identifying causality: HFTs

might initiate quote stuffing as a response to changes in market volatility and liquidity,

instead of quote stuffing causing those changes. The results would also be less robust if

the authors failed to remove every instance of news-driven quote stuffing.

3.3 R isk events

Another common criticism is that HFT is a strategy that works until it doesnt. In other

words, while HFT may improve market quality under normal conditions, it will

occasionally create or exacerbate major risk events. The classic example of such an event

is the May 6, 2010 Flash Crash. Over the course of 15 minutes, the Dow Jones and

S&P 500 both lost and recovered over 5% of their value, with several stocks briefly

trading for one cent per share.11

There was no apparent fundamental cause for the sudden

10

Egginton, Van Ness, and Van Ness, 2012, p. 12 11

Trades against these one-cent stub quotes were generally broken after the fact.

11

drop. A joint report by the SEC and Commodities Futures Trading Commission (CFTC)

attempted to uncover the trigger that started the decline. They find that at 2:32 PM, the

beginning of the crash, the hedge fund Waddell & Reed placed a $4.1 billion sell order on

the S&P 500 mini contract. What followed was a complex interplay between HFTs

employing different strategies, each of which contributed to the precipitous price

declines.

The sudden influx of supply in the S&P mini contract overwhelmed market

making HFTs, who sought to quickly unload the positions they had acquired. While

HFTs traded over 1 million contracts on the day of the Flash Crash, they never held net

positions of greater than 3000 contracts long or short, due to inventory aversion.12

When

market makers hit their inventory constraint, they began selling off their contracts to

other HFTs. At this point, momentum-driven HFTs sensed a preponderance of selling

activity, and began to short the contract, driving the price down even further. Of course,

for every seller there must be a buyer. On May 6, the buyers were arbitrage-driven HFTs,

who bought the S&P mini contract while shorting the underlying stocks. While

arbitrageurs generally improve market efficiency, this time they had the effect of

spreading the original mispricing like wildfire, creating a perfect storm.

Compounding the issue was the general withdrawal of liquidity from the market.

The SEC interviewed employees at over 30 HFT firms in the wake of the crash. Many of

them noted that their algorithms had built-in data-integrity pauses, which means the

HFT algorithms were designed to stop trading when large price movements occurred, due

to concerns over potentially erroneous price data.13

These trading halts resulted in the

12

Kirilenko et al, 2011, p. 3 13

SEC, 2011, p. 35

12

withdrawal of more than 80% of liquidity in stocks, and more than 90% of liquidity in

ETFs.14

Since HFTs tend to trade in the most liquid assets possible, this drop in liquidity

significantly affected the 100 largest stocks by market capitalization.

The withdrawal of liquidity caused trades to occur far outside of the range of

sanity. Exchanges often require registered market makers to continuously quote on both

sides of the market. Instead of completely discontinuing their quoting practices, these

HFTs converted their quotes to stub quotes quotes so far from the market price that

they are never expected to be executed. These quotes were generally one cent for bid

offers, and $100,000 for ask offers.15

Due to the extreme withdrawal of liquidity, in a few

cases, these stub quotes were the only active quotes for a stock. When incoming market

orders hit the stub quotes, trades were executed. As a result of the Flash Crash, the SEC

broke more than 20,000 trades, and later formally banned the practice of stub quoting.

Although not rising to the level of the Flash Crash, the August 1, 2012 meltdown

of the HFT firm Knight Capital had the potential to create systemic contagion.

Immediately after the start of trading, a faulty algorithm caused Knight to purchase

billions of dollars worth of unwanted positions. The error caused dramatic mispricings in

over 140 equities; one company, Wizzard Software Corp, saw prices shoot from $3.50 to

over $14.70.16

While some of the trades were broken, Knight ultimately lost roughly

$440 million after unwinding its positions. The incident led Knight to be acquired by

Getco to remain in business. To add insult to injury, the SEC fined Knight $12 million for

violating its standards for firms with direct market access standards that were

14

Ibid, p. 40 15

Ibid, p. 63 16

Valetkevitch & Mikolajczak, 2012

13

implemented as a result of the 2010 Flash Crash.17

Clearly, these examples demonstrate

that HFTs can create or exacerbate extreme market conditions, and should be regulated in

a responsible manner.

4. Existing and proposed regulatory mechanisms

4.1 Transactions tax

The simplest possible way to regulate trades by HFTs would be to tax them. This tax

could take the form of a general Financial Transactions Tax (FTT), sometimes referred to

as a Tobin tax after the economist James Tobin. The tax would consist of a small levy,

generally in the range of 0.1%-1%, on the purchase of stocks or other securities. A

general FTT would impact all securities traders, not just HFTs. FTTs are highly

controversial, and a comprehensive literature review would span hundreds of pages.

One of the most famous advocates of an FTT is Lawrence Summers, former

Secretary of the Treasury. He and his wife authored a paper advocating for a limited FTT.

They found that an increase in stock speculation was driving a corresponding increase in

market volatility, and that an FTT would throw sand into the gears of the market

structure that facilitated that speculation.18

They cite the examples of Britain and Japan,

two nations that successfully implemented their own FTTs. Though the paper does not

specifically address HFT, which did not exist at the time, it can be seen as a response to

program trading the early form of algorithmic trading which was heavily blamed for

the market crash in 1987.

17

Stevenson, 2013 18

Summers & Summers, 1989, p. 1

14

At-Sahalia and Saglam model the Tobin tax as an exogenous shock to bid-ask

spreads. They find that a FTT would make HFTs less likely to supply liquidity,

particularly in times of low volatility. However, their analysis primarily applies to low-

latency arbitrageurs, and would likely not apply to market makers, who are often

exempted from the tax in practice.

A more novel and more targeted concept is a tax specifically applying to order

cancellations or modifications. Such a tax would dramatically reduce the incidence of

quote stuffing and latency arbitraging, while leaving market makers relatively unscathed.

At-Sahalia and Saglam examine the theoretical impact of an order cancellation tax, and

find it much less disruptive than a general FTT. In fact, they find that the HFTs optimal

response to an order cancellation tax is to increase his inventory limits, leaving him more

likely to quote and leaving market orders more likely to be filled.

4.1.1 2012 French implementation

In August 2012, France became the first country in the world to specifically tax high-

frequency trading. The regulations called for a 0.01% tax on the value of orders cancelled

or modified within 0.5 seconds of being placed. It also stipulates a maximum order

cancellation/modification rate of 80%; trades modified or cancelled below this threshold

are not subject to the tax. The regulations only apply to principal traders, and not to

brokers acting on behalf of their clients.19

The law also creates an exemption for market-

making activities.

19

France, FTT and HFT, 2013

15

In addition to the HFT tax, France implemented a general tax on financial

transactions. The tax was set at the rate of 0.2% of the value of the transaction. The tax

only applies to securities of companies with market capitalizations over 1 billion.

Interestingly, the FTT only applies to traditional traders, not high-frequency traders.

Euroclear, the company responsible for settling transactions and assessing the tax, only

settles transactions at the end of the day. If a trader maintains zero net inventory at

market open and close, as the majority of HFTs do, then no general FTT can be assessed.

In 2012, the French government raised 199 million from the FTT, far less than the 530

million they had anticipated.20

4.1.2 2013 Italian implementation

In 2013, Italy followed Frances lead by implementing its own FTT and HFT tax. The

provisions of the Italian version are somewhat stricter than those for the French version.

The Italian regime calls for a 0.02% tax on order modifications or cancellations taking

place within 0.5 seconds of the original order. The general FTT was assessed at the rate

of 0.22% until January 1, 2014, when it was lowered to 0.2%. Exemptions were made for

designated market makers and securities of companies with a market capitalization under

500 million.21

Unlike the French implementation, the Italian regime was introduced in two steps.

Italy first implemented the FTT on March 1, 2013, and then introduced the HFT tax on

September 2, 2013. My analysis focuses on the second phase, since this more directly

impacts HFT.

20

Bisserbe, 2013 21

Salvadori di Wiesenhoff & Egori, 2013

16

4.1.3 Planned 2015 Eurozone FTT

A group of 11 EU nations, led by France and Germany, are attempting to implement a

general financial transactions tax across Europe. The original proposal to cover the entire

European Union was defeated, but the nations have agreed to implement the policy

multilaterally through the process of enhanced cooperation. Similar to the notion of an

interstate compact in the United States, enhanced cooperation states that the FTT will

only become law upon the unanimous agreement of all 11 interested countries.

The most recent draft of the proposal calls for a 0.1% tax on equity and bond

transactions and a 0.01% tax on derivatives. There is no specific tax implemented on

high-frequency transactions or cancellations. Several points of contention, including the

lack of exemption for pension investors and the questionable legality of collecting the tax

from non-EU citizens, have prevented the speedy implementation of the proposal. While

the European FTT was scheduled to take effect on January 1, 2014, it now appears that it

will be implemented in 2015 at the earliest.22

4.2 Licensure regime

Another proposed means of regulating HFTs is through a licensure regime. Instead of

leaving market entry relatively unregulated, this regime would require firms to seek

government approval before engaging in HFT. A licensure regime would have the

advantage of being able to distinguish between beneficial and harmful HFT algorithms

as long as the regulators in charge of licensure are competent enough to understand the

difference. The main drawback of a licensure regime is the difficulty of implementing it,

22

Fairless, 2013

17

including defining terms, hiring experts, and analyzing firm strategies. By raising barriers

to market entry, licensure may also increase the risk of market oligopolies.

4.2.1 German HFT Act of 2013

On May 15, 2013, Germany implemented a series of reforms designed to regulate high-

frequency trading. The core reform was a requirement for firms engaging in HFT to

obtain a license. The act defines HFTs as proprietary traders who access markets by a

high frequency algorithmic trading technique characterized by the use of infrastructure

intended to minimize latencies, by automated order initiation, generating, routing or

execution without human intervention for individual trades or orders and by a high

volume of intraday messages which constitute orders, quotes or cancellations.23

Licensure in Germany requires a capital base of at least 730,000. The German

government can compel firms to disclose their algorithms, and suspend trading in the

event of market abuse. Market abuse is defined as the placing of orders without a

trading intention, but (a) to disrupt or delay the functioning of the trading system, (b) to

make it more difficult for a third party to identify genuine purchase or sale orders in the

trading system, or (c) to create a false or misleading signal about the supply of or demand

for a financial instrument.24 This provision is intended to prevent phantom quoting from

occurring. The exchange may also fine people for excessive use, or for exceeding a fixed

order-to-trade ratio. Unlike the other regimes, the German regime allows this ratio to

differ between different equities.

23

Schuster & Dreibus, 2013, p. 2 24

Ibid, p. 4

18

4.3 Price limits

One potential remedy for systemic risk events like the 2010 Flash Crash is the notion of

price limits. Price limits would prevent market participants from quoting more than a

given percentage away from the previous days closing price. Kim, Liu, and Yang

analyze the effectiveness of the price limit regime imposed by the Shenzhen Stock

Exchange in December 1996. The exchange imposed a 10% price limit for normal

stocks, and a 5% price limit for Special Treatment (i.e. underperforming) stocks.

Using a single-difference methodology, the authors find a significant reduction in

transitory volatility under the price limit regime. Price limits also helped volatility return

to normal levels more quickly after volatility shocks.

The United States implements a de facto price limit regime. In the wake of the

2010 Flash Crash, the SEC imposed a blanket ban on stub quotes. Market makers must

place limit orders within 8% of the National Best Bid and Offer (NBBO). The exchanges

and FINRA also make it a policy to break clearly erroneous trade executions.

Depending on several factors including stock price and the presence of circuit breakers,

trades may be broken if they occur anywhere from 3% to 30% away from a stocks

reference price.25

4.4 Trading halts

Many exchanges, including all of the major exchanges in the United States, impose

mandatory trading halts (Circuit Breakers) on stocks after large price movements. Circuit

breakers were instituted in the wake of the October 1987 stock market crash. The original

25

For a more comprehensive summary, see SEC, 2010, p. 7

19

regime called for all trading to be halted for 15 minutes after a 10% or 20% decline in the

DJIA, and for trading to end for the day after a 30% decline in the index. In 2013, the

system received a dramatic overhaul. Under the current system, known as Limit

Up/Limit Down (LULD), trading may be halted for five minutes if the price of a stock

moves up or down by more than 5% in five minutes. This system would have stopped the

vast majority of erroneous trades that took place on May 6, 2010. Unfortunately, single-

stock circuit breakers were not yet in effect, and exchanges only broke trades occurring

more than 60% away from the reference price.

Santoni and Liu analyze the effectiveness of the original circuit breakers

implemented in the wake of the 1987 crash. Similar to my own methodology, they

construct a GARCH model of volatility in the S&P 500 from 1962 to 1991. They find no

evidence that the circuit breakers moderated long-run volatility, both on normal days and

on days with 50+ point moves in the index.

4.5 M aximum order-to-trade ratios

Yet another idea to regulate HFTs is to impose a cap on a firms order-to-trade ratio. In

2012, the Borsa Italiana implemented such a restriction. Firms placing more than 100

orders for every trade were subjected to a tax of up to 1000 per day. Friedrich and Payne

analyze the effects of the order-to-trade ratio (OTR), using a difference-in-difference

methodology matching my own. They find that bid-ask spreads in Italy significantly

increased after the implementation of the OTR. They also find a negative impact on order

book depths. They conclude that an OTR fails to distinguish between efficiency-

generating HFTs and rent-seeking HFTs, harming liquidity in the process. However, their

20

results are somewhat weakened by the fact that spreads rose on competing Italian

exchanges that did not implement an OTR at the same time.

4.6 Proposed novel ideas yet to be implemented

4.6.1 M inimum order durations/holding periods

One potential method of curbing abusive HFT practices would be to require traders to

leave their orders unmodified for a minimum length of time usually on the order of one

second or less. These minimum order durations would effectively ban the practices of

quote stuffing and phantom quoting. At-Sahalia and Saglam address the idea of

minimum order durations in their theoretical paper on HFT. They find that imposing a

minimum time limit before allowing order cancellation would have two key benefits.

First, it would improve liquidity by increasing the probability of the HFT quoting at any

given time. Second, it would eliminate the HFTs sensitivity to volatility. Instead of

withdrawing liquidity when the market needs it most, the HFT would continue quoting at

normal levels. A speed bump of as little as 20 milliseconds could achieve this desirable

impact.26

4.6.2 Quoting obligations for market makers

Another proposal to mitigate risk events is to impose quoting obligations on HFT market

makers. In a speech before the Economic Club of New York, SEC Chairwoman Mary

Schapiro questioned whether the firms that effectively act as market makers during

normal times should have any obligation to support the market in reasonable ways in

26

At-Sahalia and Saglam, 2013, p. 44

21

tough times.27 Forcing market makers to quote during the Flash Crash might have

alleviated the problems caused by the withdrawal of liquidity. Schapiro also implied that

HFTs should be banned from short-selling during periods of crisis, analogous to the

uptick rule which prevented firms from placing short positions on equities with falling

prices until 2007.

However, Rijper, Sprenkeler, and Kip argue that quoting obligations would be

unreasonably strict. Noting the extreme financial stress these obligations would cause

market makers during times of crisis, they claim that No company can simply be asked

to commit suicide voluntarily. They point out that existing quoting obligations failed to

stop market crashes in 1987, 1998, and 2007, and that no existing obligations require

designated market makers to quote 100% of the time.

5. M ethodology

My analysis will attempt to determine the impact of recent HFT regulations on several

different measures of market quality. A significant portion of this analysis focuses on

volatility. I measure the impact of regulations on both intraday and long-run volatility, as

well as the persistence of volatility shocks. I also measure how regulation affected

liquidity by examining the change in bid-ask spreads. Finally, I examine the relationship

between volatility and bid-ask spreads.

27

Schapiro, 2010

22

5.1 Impact on daily trade volume

Before beginning my analysis of market quality, I must determine whether the European

HFT regulations had any impact on the market at all. Presumably, any law designed to

make HFT more difficult will monotonically decrease trading volume. I will thus

determine whether the expected decrease in overall volume took place. I model trading

volume with the following specification:

(1) ln(Vt) = + * D + * Mt +

Alpha is a baseline trading volume. Beta is the percentage change in volume

created by D, a dummy variable to indicate observations after regulations were

introduced. I also include 12 different dummy variables (Mt) for months to account for

seasonality. I will then perform a difference-in-difference test on the model on the date of

the implementation of the regulatory regime. Finding a significant difference in betas

between the experimental sample and the control sample implies that the regulatory

regime changed the level of trading volume. The specification is as follows:

(2) Et = -

(3) SEt =

Et denotes the effect size of treatment t, where and are regression

coefficients derived from equation 1. SEt, the standard error of the effect size, is a pooled

variance estimator constructed from the sample standard deviations and sample sizes of

the treatment and control groups. My methodology follows Zhang, who constructs

difference estimates in the same manner. I use the same methodology to construct

difference-in-difference estimators for the remainder of my analysis.

23

5.2 Impact on long-run volatility

Volatility in financial markets is a property undesirable to all market participants, save

for a few options traders and short sellers. One of the oft-claimed benefits of HFT is a

reduction in overall volatility. By this logic, any HFT regulations should cause an

increase in volatility. Lanne and Vesala examined the theoretical effects of a Tobin tax,

using transaction cost data as a proxy, and found that transaction costs have a significant

positive effect on volatility. Their reasoning is as follows: transaction taxes push

informed participants (i.e. HFT) out of the market, leaving uninformed participants

in charge of price discovery. This adverse selection of parties impedes the price discovery

process, creating more price volatility.

However, HFT detractors are quick to point out that HFT exacerbates volatility

clustering. At-Sahalia and Saglam model HFT as a Poisson process. They find that in

periods of high volatility, HFT are less likely to quote, effectively withdrawing liquidity

from the market when it needs it the most. This amplifies volatility by making further

price movements more likely.28

When HFT do choose to quote in periods of high

volatility, they decrease the size of their orders below normal levels. The authors also

examined the theoretical effects of a Tobin tax, the form of regulation at the heart of the

French and Italian regimes. They found that The HFTs quoting has the same sensitivity

to volatility when compared to the scenario in the absence of a Tobin tax.29 In other

words, regulation should not impact the autoregressive component of volatility, though it

may change the baseline level.

28

At-Sahalia and Saglam, p. 31 29

Ibid, p. 40

24

There is much discussion in the financial literature about which types of models

should be used to fit volatility. Poon and Granger analyzed no fewer than 93 different

papers on volatility forecasting in their 2003 literature review. They concluded that the

best models incorporated implied volatility extracted by using the Black-Scholes model

on option prices. However, since the HFT regulations under consideration specifically

target equity markets, interpolating equity volatility from option prices seems

inappropriate.

The simplest way to model volatility is with an autoregressive process. An AR(p)

model regresses volatility on p lags and a constant. The specification is as follows:

(4) ti2 = i +

+

AR processes have a few convenient properties. They are generally stationary,

with the beta coefficients having values between zero and one. In the long run, AR

processes mean revert to some natural level. They model the phenomenon of volatility

clustering fairly well: a shock to volatility at time t will impact volatility for a long time.

The AR regression specification also allows for the use of entity-fixed effects, which can

account for different levels of unconditional volatility in different stocks.

However, simple AR processes have a few drawbacks. The first is the assumption

of homoscedasticity: by definition, errors are i.i.d. and drawn from a Gaussian

distribution. This assumption is generally unrealistic, since errors are neither Gaussian

nor independent in most financial data. AR processes also depend heavily on the

selection of the correct number of lags. I use an AR(1) process to describe the long-run

volatility change, since

converges to the true value of long-run volatility regardless

25

of the number of lags selected, but omit the AR process from my discussion on volatility

persistence, which relies heavily on lag selection.

Autoregressive Conditional Heteroscedasticity (ARCH) models are conceptually

related to AR(p) models. Instead of regressing volatility on a constant term and lagged

values of volatility, ARCH models regress on a constant term and lagged values of the

squared error term. The ARCH(q) specification is:

(5) t2 = +

Generalized ARCH, or GARCH, models improve on ARCH by including lagged

values of volatility. A GARCH(p, q) model is simply an ARCH(q) model with a set of

AR(p) autoregressive terms. The GARCH(p, q) specification is:

(6) t2 = +

+

The GARCH(1, 1) model is by far the most popular model in modeling financial

volatility.30

I will include a GARCH(1, 1) model in my analysis of long-run volatility and

the persistence of volatility shocks.

Exponential GARCH provides several more improvements to volatility modeling.

First, it removes the constraint that coefficients must be positive. Second, it more closely

resembles a normal distribution by accounting for outliers. Third, it reflects the fact that

the autocorrelation of volatility is asymmetric: negative shocks create more volatility than

positive shocks.31

Poon and Granger find that In general, models that incorporate

volatility asymmetry such as EGARCH and GJR-GARCH perform better than

GARCH.32

30

Tersvirta, p. 4 31

Poon and Granger, p. 484 32

Ibid, p. 507

26

However, there are drawbacks to using complex models like EGARCH. The

largest difficulty is in ensuring convergence of the parameters. When estimating GARCH

family models, STATA uses two different algorithms: the Berndt-Hall-Hall-Hausman

(BHHH) algorithm, and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Both

algorithms are quasi-Newtonian iterative methods designed to maximize the log

likelihood function. Unfortunately, there is no guarantee of convergence: the algorithms

may encounter a flat log likelihood function and fail to converge. Failure to converge

would result in estimated parameters that were not global maxima.

The second major difficulty comes in applying univariate econometric techniques

to data with multiple panels. In order to fit multiple equities into a single model, I must

constrain the coefficients to be the same for every stock within a country. This may be a

suboptimal constraint, particularly if different industries are governed by different

volatility processes. However, by analyzing only the stocks on each nations benchmark

index, I hope to correct for industry effects by ensuring a diversity of industries are well-

represented in my sample. Multivariate GARCH techniques do exist, but are

inappropriate for my purposes; since the number of coefficients increases in proportion to

N2, each model would require estimating at least 900 parameters, which is effectively

impossible with iterative maximum likelihood estimation.

I include an EGARCH(1, 1) process as one of my volatility estimating techniques.

The EGARCH(1, 1) specification is as follows:

(7) Ln(RVit2) = + * Ln(RVi,t-1

2) + * |

| + *

The debate over the best method of modeling volatility will, to borrow a

colloquialism if you please, continue to rage until the end of time. I include multiple

27

models of volatility as a means of determining whether model specification significantly

impacts the results. Note that I do not include models of Stochastic Volatility, which lack

a closed form expression and are thus even less likely to achieve convergence than

EGARCH.

I will fit EGARCH models for each of the different countries, with separate

fittings for the period before and after regulations were implemented. There are 16

EGARCH models in total after accounting for control models. For Italy, the hypothesized

break date is September 2, 2013. For Germany, there are two hypothesized break dates:

the passage of the law, on May 15, 2013, and the deadline for obtaining a license to

perform HFT, on November 14, 2013. In France, the hypothesized break date is August

1, 2012.

After fitting the models, I use pooled variance to construct difference estimators,

allowing for a difference-in-difference between sample and control to be derived. The

difference-in-difference methodology should ensure exogeneity of the results, since no

major changes in regulatory policy occurred on the London Stock Exchange in 2012 or

2013.

A significant difference-in-difference would imply that the HFT regulations had a

significant impact on volatility. There are two possible impacts I will consider: a change

in

, the baseline level of volatility, and a change in beta, the volatility persistence

coefficient. To simplify my analysis, I will consider a reduction in either value to be an

improvement in market quality.

28

5.3 M odels of intraday volatility

The characteristics of intraday data make the use of GARCH family models intractable in

modelling volatility. Ghalanos describes the main problem with the data: volatility is not

mean-reverting. Instead, it exhibits strong time-of-day effects, peaking at the daily open

and close of the market. This trait means that convergence of the parameters in any

maximum likelihood estimation is nearly impossible. Therefore, I use a simple AR(1)

model of volatility. My specification is as follows:

(8) = + *

+ * D + * D *

My volatility measure is the standard deviation in midprice movements over the

course of one minute, per Brogaard. Midprice is the average of the best bid and offer. is

a baseline volatility coefficient, and is the change in that coefficient after the

introduction of regulations. is an autoregressive coefficient, and is the change in

that coefficient caused by regulations. D is a dummy variable for observations after the

imposition of regulations. Average volatility can be recovered as

in the period

before regulations, and

in the period after. To account for excess volatility at the

open and close of trading, I omit observations from the first and last five minutes of

trading each day.

5.4 M odels of liquidity provision

There are two main ways to measure the provision of liquidity: bid-ask spreads and order

book depth. Ironically, data on depth is generally proprietary and reserved for HFT

clients, so the former will be my metric of choice. Using high-frequency data, I will

29

measure bid-ask spreads at one minute intervals. My objective is to determine to what

extent volatility impacts the size of spreads, and whether HFT regulations increased or

decreased the size of the effect. My specification is as follows:

(9) ln(St) = + * ln(St-1) + * + + * D * ln(St-1) + * D *

St is the quoted spread of the benchmark ETF at time t, measured as the natural

log of the percentage spread. Taking the log of spreads helps account for situations where

baseline spreads are dramatically different in different countries; in particular, the FTSE

spread typically hovered around 3 basis points, while the MIB spread was in the

neighborhood of 17 basis points. is the constant before regulations are imposed. is

an autoregressive coefficient, since spreads are highly autocorrelated. is the coefficient

of interest: the change in spread caused by a unit change in volatility. D is a dummy

variable equal to 1 for observations after the implementation of regulations. After fitting

the model, I will perform a Chow test to see if the relationship between bid-ask spreads

and volatility changed. An increase in gamma implies that the regulations make spreads

more sensitive to volatility, and thus hurt the price discovery process. A decrease in

gamma suggests that bid-ask spreads become less correlated with volatility, which would

vindicate the regulations.

I will also measure the long-run value of spreads before and after the change. This

is denoted by

+ *

in period 1 and

+ ( + ) *

in period 2, with

volatility defined using the long-run values from section 5.2. (The potential difference

between average and realized volatility is relatively unimportant in this application, since

volatility contributes less than 1% of the value of the spread.)

30

5.5 Potential threats to validity

In spite of my confidence about ensuring exogeneity, my study has a few limitations. The

most obvious potential pitfall is the use of univariate volatility modelling techniques on

panel data. Constraining multiple different assets to have the same coefficients may

introduce substantial upward bias in estimates for volatility persistence. This could be

corrected by creating a separate model for each asset, or by simply testing the benchmark

ETF instead of the indexs individual components. However, the decision to use multiple

assets was an effort to address the small T, large N problem of having too few

observations.

Another study design decision that might have impacted my results is the decision

to use the London Stock Exchange as a control group. My rationale for doing so was

fairly straightforward: it is the largest stock exchange in Europe, yet did not implement

any regulations on high-frequency trading during the sample period. However, the LSE

also trades in Pounds Sterling instead of Euros, since Britain is not a Eurozone member.

This raises the complication of accounting for the Euro crisis. I excluded data points

before 2012 in an effort to keep the Euro crisis from biasing my long-run volatility

estimates. However, it is possible I failed to truncate enough data for example, Mario

Draghis whatever it takes speech, which definitively secured the future of the

Eurozone, did not occur until July 26, 2012. The model might interpret the economic

recovery in early 2012 as a period of high volatility, thereby exaggerating the extent to

which regulations decreased volatility.

Yet another potential issue is the relative lack of datapoints in some cases. The

most egregious case is the German HFT licensure deadline: there are only 30 trading days

31

represented in the period after the deadline passed. This makes it difficult to draw strong

inferences about the effects of the policy, and explains the relative lack of significant

results. I would strongly encourage academics to continue to study the empirical impacts

of HFT regulations over the course of the next few years.

A further complication is the introduction of multiple regulatory changes

simultaneously. This is not a major issue in the case of Germany, which implemented

HFT-specific regulations, or Italy, which delayed the implementation of an HFT tax until

several months after the introduction of a financial transactions tax. However, France

implemented their HFT tax and FTT at the same time. It is thus difficult to determine

which effects were caused by the HFT tax, and which were caused by the FTT. It would

be convenient if a country adopted an HFT tax without having an FTT in place, but alas,

countries dont design their regulatory policy around the dreams of econometricians.

6. Data

Data on long-run equity volatility will be drawn from Thomson Reuters Datastream

International and the CompuStat Global database. The data will span from January 1,

2012 to December 31, 2013. Data from the French, Italian, and German stock exchanges

will form the experimental groups, and British equities will form the control group. Per

Poon and Granger, I will use squared returns to calculate volatility, instead of squared

deviation from the mean return.

My sampling frequency will be daily, due to the relatively short timeframe of my

analysis. I follow the methodology of Guo, Kassa, and Ferguson, who recommend using

a two year rolling window of daily returns to ensure robustness and make the model less

32

sensitive to the initial values. There are 512 trading days in the sample period. Our

desired dependent variable, Realized Volatility, is defined as the following:

(10) RVit =

For each country, I will analyze the stocks included in the primary exchanges

benchmark index. The exchanges are summarized below.

Country Index # Stocks Trading days

Germany DAX 30 16,380

France CAC-40 40 21,840

Italy FTSE MIB 40 21,840

Britain FTSE 100 100 54,600

Total trading days = 114,660

Data on bid-ask spreads and intraday volatility will be drawn from TickWrite.

Experimental data are available for France and Italy, and Britain will again serve as the

control sample.33

Data will be drawn at the tick level and aggregated into one-minute

intervals. The sample period will consist of a 23-day window around the implementation

of new regulations in Italy, and a 25-day window in France. Due to the large volume of

data generated at the tick level, I will only analyze the ETF representation of each

benchmark index, in lieu of the actual components. These data are summarized below:

33

German data exists, but is proprietary. Unfortunately, Princeton didnt want to spend $120,000 to help me write my thesis. Cest la vie.

33

Country Index (ETF) Minutes Tick Observations

France CAC-40 (FP) 12,750 1,379,735

Italy FTSE MIB (IMIB) 11,730 119,182

Britain FTSE 100 (ISF) 24,480 1,824,754

Total trading minutes = 48,960

# Observations at tick intervals = 3,323,671

7. Hypothesis

All things considered, any form of HFT regulation should reduce the daily trading

volume of assets. I hypothesize that all of the implemented regulations will cause a

significant decrease in the equilibrium trading volume.

In terms of volatility, different empirical papers imply different outcomes. Those

who believe HFT moderate volatility would predict an increase in omega following

regulation. Those who think HFT withdraw liquidity during times of high volatility

believe regulation would cause a decrease in beta. These two theoretical results are not

incompatible. To accommodate both, I make the following hypothesis: beta will decrease,

but overall volatility (in terms of

) will increase.

The results should be largely the same when looking at bid-ask spreads. I predict

a decrease in gamma, the parameter representing sensitivity to volatility. This reflects the

intuition that HFT are less likely to quote in periods of high volatility, a behavior which

increases the size of spreads. I also anticipate an increase in the bid-ask spread size.

In terms of magnitude, I predict the largest effects in Italy, which imposed the

strictest regulations on HFT. A slightly smaller effect should be seen in France, which

34

implemented similar restrictions to Italy. In Germany, I predict a very small effect size,

perhaps not statistically significant. This is due to the relatively lax nature of a licensure

regime, combined with the fact that the Deutsche Borse already monitored algorithmic

trading as early as 2007. Since Britain forms the control group, and adopted no new

regulations on HFT or other trading since 2012, there should be no effect during the

introduction of regulations in other countries.

8. Results and analysis

8.1 Change in trade volume

I begin by analyzing the change in trade volume caused by the implementation of

regulations. Our coefficient of interest is from regression 1. My results are summarized

below.

Table 1: Change in trade volume after imposition of regulations

Country % Change in sample

volume

% Change in control

volume

Difference-in-

difference

Germany (5/13) -18.06%***

(2.29%)

-10.26%***

(1.33%)

-7.8%***

(2.48%)

Germany (11/13) -14.37%***

(5.12%)

-4.82%*

(2.91%)

-9.55%*

(5.67%)

France -19.6%***

(1.89%)

-15.67%***

(1.37%)

-3.93%*

(2.12%)

Italy -9.78%

(6.13%)

-13.15%***

(1.83%)

3.37%

(5.97%)

Standard errors in parentheses

* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p

35

In Germany, both the May 15 announcement and the November 14 licensure

deadline were associated with a significant decline in trading volume. The results

remained significant when compared to the change in British trading volume during the

same time period. The effect size was 7.8% for the announcement, and 9.55% for the

licensure deadline a modest decline, to be sure, but still significant.

In France, the implementation of a combined financial transactions tax and HFT

cancellation tax caused a 19.6% decline in trading volume. After accounting for the

British control group, the relative decline in trading volume was 3.93%. This effect was

significant at the 10% level, though not as large as the German effect.

In Italy, trading volume declined by 9.78% after the implementation of an FTT

and HFT tax. The decline in volume barely misses significance at the 10% confidence

level, with a P value of 0.1108. Surprisingly, the decline in trade volume in the British

control group during the same time frame was larger. The relative effect was a 3.37%

increase in trade volume, though this effect is not statistically distinguishable from zero.

The difference between France and Italy is easy to explain: while France implemented a

generic FTT and HFT tax simultaneously, Italy introduced its FTT six months before

implementing an HFT tax. The HFT tax may have encouraged LFTs to reenter the market

after being driven away by perceived unfairness.

8.2 Change in long-run volatility

36

The change in regulatory policy generally resulted in a decline in long-run volatility.

Model selection had an impact on the effect size and direction, with two models

producing an increase in volatility. A summary of the different models is offered below34

:

Table 2: Summary of long-run volatility across different models

Country EGARCH GARCH AR(1)

Vol? Significant? Vol? Significant? Vol? Significant?

Germany

(5/13) Increase Yes Decrease Yes Decrease No

Germany

(11/13) Decrease No Increase Yes Decrease No

France Decrease No Decrease Yes Decrease Yes

Italy Decrease Yes Decrease Yes Decrease Yes

In France and Italy, regulation caused a decline in volatility relative to the

benchmark, regardless of the model used. In almost all cases, this decline was statistically

significant at the 1% confidence level. This finding strongly vindicates Larry Summers

and other advocates of transaction taxes, who claim that excess speculation increases

market volatility.

The case for the German licensure regime is much less clear. The announcement

of the regime in May 2013 caused a significant increase in long-run volatility in the

EGARCH model, a significant decrease in the GARCH model, and an insignificant

decrease in the AR(1) model. Of note is the nonstationarity of volatility in the GARCH

model in the period after the announcement. The alpha and beta coefficients sum to more

than 1, making volatility a random walk process instead of a mean reverting process. In

the long run, volatility is always mean reverting, but nonstationarity may persist for some

34

Coefficient values are reported in appendices 1 - 3

37

time. I attribute this to the relatively short timeframe (7.5 months) of the regime. This

makes it somewhat difficult to draw strong inferences about regime effectiveness.

The German licensure deadline causes an insignificant decline in volatility in the

EGARCH and AR(1) specifications, and a significant increase in volatility in the

GARCH specification. Again, this difference may be due to the sample size, which is

even smaller (1.5 months) in the post-licensing regime. It may also be caused by the

different natures of GARCH and EGARCH with respect to shocks. EGARCH tends to

magnify large shocks, and magnifies negative shocks more than positive shocks.

Depending on the number, sign, and magnitude of shocks, different models may produce

significantly different results. Given the different sign changes produced by different

models, and the statistical insignificance of three of them, I conclude that the licensure

regime had no impact on long-run volatility.

Figure 1: Conditional volatility as a function of past shocks35

35

Chart from Bollerslev, 2011, p. 24

38

Interestingly, the impact of HFT regulations on the persistence of volatility shocks

is generally the opposite of the impact on long-run volatility. The following table

summarizes changes in , or volatility persistence, relative to the British benchmark:

Table 3: Relative change in volatility persistence

Country EGARCH GARCH

Persistence? Significant? Persistence? Significant?

Germany (5/13) Decrease No Decrease No

Germany (11/13) Decrease Yes Decrease No

France Increase No Decrease Yes

Italy Increase Yes Increase No

Both the announcement of the German licensure regime and the licensure

deadline were associated with a decrease in volatility persistence, regardless of model

selection. Unfortunately, only the EGARCH model of the licensure deadline found a

result significant at the 1% confidence level.

In the case of France, the EGARCH model found a moderate but insignificant

increase in the persistence of volatility shocks, while the plain GARCH model produces a

significant decrease in persistence. For Italy, both models produce an increase in

volatility persistence, though this is only significant in the EGARCH model. In fact, the

EGARCH model gives a beta of greater than 1 for Italy, implying nonstationarity of

volatility. Also of note is that both French and Italian regulations produced an increase in

the absolute length of volatility persistence, but the French regulations produced a

decrease in the relative length, due to a substantial increase in persistence in the control

group.

39

Table 4: Half-life of volatility shocks, measured in days, GARCH model

Country

Sample group British control group

in Pre Post Pre Post

Germany(1) 16.64 15.71 -0.93 18.15 18.29 0.14 -1.07

Germany(2) 20.36 7.78 -12.58 18.59 9.51 -9.08 -3.5

France 18.45 20.91 2.46 11.37 15.68 4.31 -1.85

Italy 19.24 19.45 0.21 18.96 17.56 -1.4 1.61

8.3 Change in intraday volatility

In both France and Italy, the implementation of regulations was associated with a

significant increase in intraday volatility. This is in line with the expectations of HFT

advocates, who argue that HFT acts as a stabilizing force in the markets. In France, the

persistence of volatility shocks significantly increased, while persistence declined in

Italy.

Table 5: Impact of regulations on intraday volatility, France

Constant Vol Chow(P) R2

France Baseline 4.19***

(0.255)

0.24***

(0.048)

5.51***

(0.063) 71.94

(0) 0.2009

Change -0.551

(0.546)

0.235***

(0.088)

1.41***

(0.094)

Britain Baseline 2.25***

(0.781)

0.451*

(0.194)

4.09***

(0.053) 7.01

(0.0009) 0.1635

Change 0.266

(0.838)

-0.109

(0.211)

-0.27***

(0.091)

Difference-in-

difference

-0.817

(0.683)

0.343**

(0.152)

1.68***

(0.093)

Standard errors in parentheses

* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p

Constant and Vol multiplied by 100,000

40

Table 6: Impact of regulations on intraday volatility, Italy

Constant Vol Chow(P) R2

Italy Baseline 9.23***

(2.31)

0.366**

(0.177)

14.55***

(0.921) 2.91

(0.0543) 0.0529

Change 5.35**

(2.53)

-0.291

(0.186)

1.21

(1.27)

Britain Baseline 3.62***

(0.31)

0.109

(0.075)

4.06***

(0.039) 68.70

(0) 0.0726

Change -1.47***

(0.34)

0.223**

(0.088)

-0.84***

(0.423)

Difference-in-

difference

6.82***

(1.78)

-0.513***

(0.144)

2.05**

(0.889)

Standard errors in parentheses

* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p

Constant and Vol multiplied by 100,000

8.4 Change in liquidity provision

The impact of regulations on bid-ask spreads is not as clear cut as I had anticipated. In

Italy, as predicted, spreads increased relative to the control sample, though the effect

barely misses significance at the 10% confidence level. Surprisingly, spreads

significantly decreased in France, in both absolute and relative terms. The specific

implementation of the French law may hold the key. In France, HFTs are only taxed for

order cancellations, and not for completed transactions, as long as they clear their

inventory by the end of the day. It is possible that HFTs only altered their order

cancellation behavior in response to the regulations, and not their quoting behavior. This

would leave an HFT offer at best bid or ask more frequently, and, in theory, reduce

spreads. Indeed, At-Sahalia and Saglam acknowledge that the probability of an HFT

quoting at a given time is increasing in the level of a cancellation tax.

41

Table 7: Impact of regulations on bid-ask spreads and their responsiveness to

volatility, France, August 1, 2012

Constant Spread36 Chow(P) R2

France Baseline -2.832***

(0.332)

0.617***

(0.045)

1715***

(304)

6.76***

(0.199) 51.19

(0) 0.6441

Change 0.514

(0.366)

0.076

(0.05)

-392

(392)

-15.8%***

(4.58%)

Britain Baseline -2.36***

(0.163)

0.705***

(0.021)

1268***

(111)

3.47***

(0.094) 34.28

(0) 0.5395

Change -0.541**

(0.216)

-0.075***

(0.028)

-162

(133)

14.1%***

(3.24%)

Difference-in-

difference

1.056**

(0.425)

0.151***

(0.057)

-230

(414)

-29.9%***

(5.61%)

Standard errors in parentheses

* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p

Table 8: Impact of regulations on bid-ask spreads and their responsiveness to

volatility, Italy, September 2, 2013

Constant Spread36 Chow(P) R2

Italy Baseline -1.009***

(0.148)

0.842***

(0.023)

90**

(42.3)

17.32***

(0.537) 19.92

(0) 0.6549

Change -2.148***

(0.347)

-0.332***

(0.054)

78.5

(60.5)

-5.35%*

(3.15%)

Britain Baseline -3.027***

(0.154)

0.621***

(0.019)

110

(756)

3.42***

(0.168) 14.82

(0) 0.4313

Change -0.731***

(0.234)

-0.073***

(0.028)

3142***

(893)

-21%*

(11.3%)

Difference-in-

difference

-1.417***

(0.418)

-0.259***

(0.061)

-3064***

(895)

15.63%

(11.73%)

Standard errors in parentheses

* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p

My empirical findings with respect to gamma, the volatility sensitivity parameter,

exactly match my hypothesis. In both Italy and France, sensitivity to volatility decreased

relative to the control sample. In France, sensitivity decreased in both the absolute and

relative senses, though neither change was significant at the 10% confidence level. In

36

Baseline spread is reported in basis points, while change is measured as a percentage of the baseline

42

Italy, sensitivity only declined relative to the British control sample, though the relative

decline was significant at the 1% confidence level.

In every case, there is a positive correlation between volatility and bid-ask

spreads. The findings confirm the theoretical results of At-Sahalia and Saglam, who state

that HFTs are less likely to quote during periods of high volatility, widening spreads in

the process. Cancellation taxes directly incentivize HFTs not to withdraw liquidity during

periods of high volatility, when liquidity is most needed.

9. Conclusion

The problem of regulating high-frequency trading is especially difficult to solve. There

are many different stakeholders with dramatically different interests to satisfy. A central

regulator must balance the demands of traders, exchanges, and institutional investors, all

while ensuring the stability and robustness of markets.

Additionally, the rapidly-evolving nature of HFT means that regulators lack any

meaningful precedent to turn to for informing their policymaking. European governments

appear to be entering a test and learn phase, in which new policies are implemented and

tweaked until they appear to work.

My analysis underscores the difficulty of finding an optimal regulatory policy. In

several cases, the significance and even direction of the change in market quality

measures differed between different models. Even when the models do agree, the policies

themselves generally produce different results. And no form of regulation is perfect

each regulation improved some measures of market quality while worsening others.

Nevertheless, there are some useful pieces of information we can extract.

43

The German licensure regime appears to have modestly improved market quality.

The impact on long-run volatility is ambiguous, though a majority of models found some

decrease in the unconditional variance of returns. However, all models tested found a

decrease in volatility persistence. Licensure may reduce conditional heteroscedasticity by

discouraging HFTs from withdrawing liquidity at key moments or exacerbating volatility

with phantom orders designed to move the market. Indeed, the true success of licensure is

its ability to distinguish between abusive practices and legitimate market-making

activities.

Both the French and Italian regulatory regimes appear to have substantially

decreased long-run volatility. Model selection had little to no impact on this result. Since

a reduction in volatility means an improvement in market quality, the policies were

dramatically successful on this front. However, there appears to be a tradeoff between

long-run volatility and persistence. The persistence of volatility shocks significantly

increased in Italy; in France, the models disagreed on the direction of the change. This

inverse relationship is surprising: if we held constant, then long-run volatility and

volatility persistence would move in tandem by definition.

There are more tradeoffs to consider when analyzing market microstructure. The

French and Italian regulations both substantially increased intraday volatility measured at

one-minute intervals. However, both regulations decreased the sensitivity of bid-ask

spreads to volatility. This tradeoff might strike different stakeholders very differently. An

institutional investor, for example, might not care about intraday volatility if his holding

period is measured in months or years, but might care a lot about preventing potential

flash crash situations. Meanwhile, a market maker who ends each day with zero net

44

inventory would be tremendously harmed by an increase in intraday volatility, but

wouldnt particularly care that HFTs withdraw liquidity in times of high volatility

indeed, he would be one of the first traders to cancel his orders.

My final analysis considered how bid-ask spreads responded to regulatory

changes. I was surprised to find that spreads in France significantly contracted after

regulations were introduced. This might simply be a result of HFTs leaving limit orders

in place that they would have cancelled in the absence of a tax. However, I am faced with

the classic joint-causality problem, due to the FTT introduced at the same time. In Italy,

spreads expanded, but not significantly so. This hints that the FTT was responsible for the

contraction of spreads. It may be the case that the FTT, which only applies to stocks held

at the end of the day, increases inventory aversion, leading HFTs to quote more

frequently to clear their inventories and avoid the tax.

In light of all this information, what is the optimal HFT regulatory policy? At the

risk of sounding indecisive, I would recommend an all-of-the-above strategy. The

licensure regime, while failing to change long-run volatility, decreased volatility

persistence, ensuring that shocks to volatility dissipate more quickly. Moreover, licensure

would force HFTs to disclose their algorithms to the government, allowing efficiency-

generating algorithms like market-making to proliferate, while curtailing rent-seeking

algorithms like low-latency frontrunning.

Cancellation taxes appear to decrease unconditional volatility, decrease bid-ask

spreads, and decrease the sensitivity of spreads to volatility. The latter property is

particularly important: robustness in the face of high volatility helps prevent flash crash

style events from occurring. These benefits do come at the cost of increased intraday

45

volatility and increased volatility persistence, but the benefits appear to outweigh the

harms. Cancellation taxes would primarily hamper algorithms designed to artificially