Lucjan T. OrlowskiProfessor of Economics and Finance,

J.F. Welch College of Business, Sacred Heart Universityg f , y

Seminar Presentation to the Luxembourg CFA Society, Luxembourg, November 10, 2010

Key Objectives

Examine extreme tail risks in EU financial markets and proposals for policy coping mechanisms

Review effective means of identifying, measuring and controlling extreme market risksA l B l III t l i d t iti ti h i k Analyze Basel III tools aimed at mitigating such risks

Suggest specific strategies and actions for the European Systemic Risk Board and other new EU regulatory institutions y g y

Understanding Extreme Tail Risks

Definition: Tail risks stem from extreme outcomes and suggest that the data distribution of financial market variables is not normal but leptokurtic, which indicates high concentration of data around the mean at normal times gand wide dispersion at turbulent periods

Theoretical Explanations of Tail Risks:1 Mi k Ki dl b Fi i l F ilit Th1. Minsky-Kindleberger Financial Fragility Theory2. Herding Behaviour Theory (Froot/Scharfstein/Stein, JF, 1992)

3 Asset-Price Bubble Models (bursting of a “wandering asset-price3. Asset Price Bubble Models (bursting of a wandering asset-price bubble” Orlowski, Economics E-journal DP #43/2008)

Tail Risk and Relevant Financial Crisis Theories

Minsky-Kindleberger ‘Financial Fragility’: transition from normal fragility to a (leptokurtic) debt deflation process, build-up of debt financed boom (Minsky’s ‘ h i ’ A S ith ‘ t di ’ f ll d b ‘Mi k t’ ‘di l t’‘euphoria’, A. Smith ‘over-trading’, followed by a ‘Minsky moment’ = a ‘displacement’ triggered by a systemic interruption; hence, tranquillity is followed by Minsky’s ‘revulsion’ or, as one can label it, leptokurtic vicissitudes

Herd Behaviour of Investors (Froot/Scharfstein /Stein, 1992; ( , ;Akerlof/Shiller, 2009): Investors as a group act together without planned direction, on the basis of ‘adaptive learning’ i.e., learning from each other. The ‘herd morality’ concept comes from Friedrich Nietzsche. It was introduced to behavioural economics by Thorstein Veblen (‘The Theory of Leisure Class’) as ‘emulation’ = some group members mimic others’ actions( The Theory of Leisure Class ) as emulation some group members mimic others actions. Investors form price expectations by following the herd. They are imperfectly rational (acting on presumed information). In behavioural finance, herd behaviour exacerbates market risk, leads to asset-price bubbles (Cipriani and Guarino, 2009)

A t P i B bbl Asset-Price Bubbles: Global imbalaces, savings glut (USD 72 trillion in 2007), investors acting on presumed price expectations, capital ‘wanders’ between various asset classes = ‘wandering asset-price bubble’ (Orlowski, 2008)

Prevalence of Tail Risks in EU Financial Markets(Key Findings of Orlowski, European Commission ECFIN, European Economy ( y g , p , p y

Economic Paper #416, 2010)

Tail risks in EU equity, interbank credit and foreign exchange markets prior to and during the recent financial crisis

Methodology: volatility series of market indicators based on GARCH-M with GED parameterization to account for leptokurtic data distribution (see Appendix A)

Examination of conditional volatility patterns in financial markets of the Czech Republic, France, Germany, Hungary, Poland, Sweden, and the United Kingdom in comparisonFrance, Germany, Hungary, Poland, Sweden, and the United Kingdom in comparison with the U.S.

Extreme tail risks detected for all examined financial markets (see Appendix B for details)

Tail risks in interbank credit markets more pronounced than those in equity and foreign Tail risks in interbank credit markets more pronounced than those in equity and foreign exchange markets

Volatility outbursts of exchange rates lag behind the respective volatility shocks for equity market indexes and interbank ratesT il i k f i t b k t t i ifi t f H P l d ll th U it d Tail risks of interbank rates most significant for Hungary, Poland as well as the United States, due to their weaker macroeconomic policy discipline; implying the need to reinforce resiliency of their banking sectors against episodes of global credit crunch

Pronounced, destabilizing tail risks in equity markets in the Czech Republic, Poland and th U it d Ki d ll f f l ti f th i i fl ti t ti t t ithe United Kingdom call for reformulation of their inflation targeting strategies

Mitigating Asset-Price Bubbles and Tail Risks:Analytical Assumptionsy p

Preponderance of “wandering” asset-price bubble (APB) stemming from global economic imbalances and savings glut, fueled by financial innovations

APBs contribute to tail risks in the behavior of financial market variables Bursting of a major APB entails severe spillover effects to the real economy Capital inflows to emerging economies trigger appreciation of their

currencies and give rise to competitive devaluations - “currency wars” Unregulated markets attract APBs Resiliency against bubble bursting effects depends on the ability of policy-

makers to control macroeconomic imbalances, specifically “twin deficits” The deepening US twin deficits may cast doubt on risk-free status of US

TreasuriesS d f i b d i ld OIS t d Lib t i ld Spreads of sovereign bond yields, OIS rates and Libor rates over yields on US Treasuries may not adequately reflect sovereign-, liquidity-, credit- and counterparty risks

Needed new approaches to risk modeling Needed new approaches to risk modeling

Risk Transmission Channels and Adverse Feedback Loops

Th   di   h l The credit channel The exchange rate channel The inflation expectations channel

Suggested Precepts for Policies Aimed at Abating Tail RisksSuggested Precepts for Policies Aimed at Abating Tail Risks

Desirable characteristics of such policies: counter cyclicality and flexibility Desirable characteristics of such policies: counter-cyclicality and flexibility

Necessary (albeit not sufficient) components of policies for abating tail risks: Flexible monetary policy based on a dual mandate of price stability and

financial stability, coupled with macroprudential regulations Monetary policy framework based on flexible, forward looking inflation

targeting that entails ‘leaning’ against anticipated credit bubbles Flexible treatment of capital adequacy ratios for financial institutions as

well as adopting contingent capital requirements Regulating derivative instruments, specifically, subjecting trades in complex

derivatives to central clearing Managing tail risks should become a part of a comprehensive policy

framework aimed at abating systemic risk

Financial Crisis and Obfuscation of Monetary Policy Instrument Rules

Simple Taylor rules are counterproductive, inadequately addressing tail risks

All f ti di ti t t il i k hil Allow for active, discretionary responses to tail risks while following more complex instrument rules

Therefore, consider rules for suspension of instrument (Taylor) , p ( y )rules at times of financial distress

Incorporate interest rate-, exchange rate-, counter-party- and th i k i i fl ti t ti ti f tiother risk measures in inflation targeting reaction functions,

without compromising simplicity (thus also transparency and credibility) of official inflation targeting policies

A Proposal for New Monetary Policy Framework

Target formula: (a forward-looking) flexible inflation with the sovereign risk premium

Supplementary models: sovereign risk premium as an outcome of an interplay between other risks faced by the financial system

Instrument function: combination of the inflation gap, output gap, neutral f g p, p g p,interest rate or a minimum liquidity balance, and the sovereign risk premium

Monitoring devices: risk spreads, exchange rates, market interest rates, g p , g , ,liquidity balances,

Quantitative easing does not comply with the proposed policy framework, shall be viewed as a temporary measurep y

QE2 by the Federal Reserve is questionable, may trigger inflation expectations, contribute to liquidity buildup, rising market interest rates, aggravate APBs and tail risksgg

Risk Mitigating Mechanism in Basel III

Elevated minimum capital adequacy The counter‐cyclical contingency buffer – milestone f h l kfor cushioning tail risks

Nevertheless, tighter capital requirements may be too excessive  inciting by passing financial innovationexcessive, inciting by‐passing financial innovation

Basel III vs. II (BIS, 2010)

Recommendations for the European Systemic Risk Board

ESRB (its General Board, Steering Committee and administration) too heavily influenced by central banks, lacking necessary independence

Needs external advise, technical expertise and independent monitoring Should work closely with national regulatory authorities and Basel

Committee on risk monitoring and early warning signals of systemic risk Its strategies shall include:g1. Pioneering research on all categories of financial risk 2. Developing cutting-edge methodologies of risk assessment that account

for tail risks (extreme value modeling GED parameterization etc )for tail risks (extreme value modeling, GED parameterization, etc.)3. Analysis of risk transmission channels leading to proliferation of systemic

risk4 Ex ante assessment of all major risk categories (liquidity credit default4. Ex-ante assessment of all major risk categories (liquidity, credit, default,

exchange rate and sovereign risks) - signals of systemic risk outbreaks

Concluding RemarksConcluding Remarks

Current financial crisis underscores importance of tail risks d i fi i l d li i blendemic to financial and monetary policy variables

Tail risks of stock market indexes, interest rates and exchange rates are magnified by speculation and institutional g y pvulnerability

My tests show their omnipresence in the U.S. and European stock markets interbank credit market and foreign exchangestock markets, interbank credit market and foreign exchange markets

Value at risk (VaR) models should also account for tail risksB h l di fi i l i i i d l Both leading financial institutions and government regulators should devise better policies for mitigating tail risks

Monetary policies cannot be based on simple instrument rules; y p pthey ought to take into consideration prevalence of tail risks

Appendix A: GARCH-M-GED Model

Market Risk Volatility Dynamics GARCH(1 1) M GEDMarket Risk - Volatility Dynamics GARCH(1,1)–M-GED Analysis: Capturing Risk Premium (M) and Tail Risk (GED)

The conditional mean equation: change in the domestic stock market index (the return process ) with the log of the GARCH variance as a regressor:variance as a regressor:

ttrttE 2

110 log

The conditional variance specification:222 2

112

1102

ttt hh

Appendix B: Empirical Testing Results

Table 2: GARCH(1,1)-M-GED estimation of percentage changes (Δlogs) in stock market indexes

Czech R. France Germany Hungary Poland Sweden U.K. U.S.

Cond. Mean Eq. (coeff.x100)

Constant term

-0.774**

-0.111

-0.348***

0.042

2.901

-0.313

-0.037

0.469*** Log(GARCH)

-0.096*** -0.018 -0.033***

-0.001 0.026 -0.041 -0.012 0.037***

Cond. Variance Eq.

Constant term ARCH(1)

GARCH(1)

0.000*** 0.118*** 0.867***

0.000*** 0.090*** 0.905***

0.000*** 1.002*** 0.444***

0.000*** 0.087*** 0.889***

0.000*** 0.109*** 0.879***

0.000*** 0.072*** 0.926***

0.000*** 0.148*** 0.845***

0.000*** 0.387*** 0.698***

GED parameter 1.380*** 1.457*** 1.239*** 1.411*** 1.251*** 1.431*** 1.395*** 0.861***GED parameter 1.380 1.457 1.239 1.411 1.251 1.431 1.395 0.861GED parameter crisis sub-period

1.405***

1.560***

0.301***

1.393***

1.178***

1.526***

1.885***

0.924***

Log likelihood

Schwartz Info Crit

7501 5 911

7446 5 867

12421 9 800

7176 5 654

8165 6 436

7149 5 632

8575 6 760

9355 7 377Schwartz Info. Crit. -5.911 -5.867 -9.800 -5.654 -6.436 -5.632 -6.760 -7.377

Notes: January 3, 2000 – September 14, 2009 sample period (2540 observations); the financial crisis sub-period is August 17, 2007 – March 31, 2009 (423 observations); *** denotes significance at 1% ** at 5% and * at 10%denotes significance at 1%, at 5%, and at 10%.Source: Author’s own estimation based on Datastream data.

Table 2 Results

Risk premium factor (in-mean logGARCH) significantly negative for German and Czech markets, while significantly

i i (hi h ) f U S k hi h i i li i h ipositive (higher) for U.S. market, which is in line with its largest tail risk – underscoring the role of speculative trading

Risk convergence (the sum of ARCH and GARCH coefficients g (<1) detected only for the Czech Market

Leptokurtosis (GED<2) is ubiquitous, most pronounced in U S followed by German Polish Czech UK and HungarianU.S., followed by German, Polish, Czech, UK and Hungarian markets, and the least apparent for French market

Tail risks dramatically increased in analyzed financial crisis sub period for German market less for Polish and Hungariansub-period for German market, less for Polish and Hungarian, but not for the remaining markets

Figures 1a-h: GARCH one standard deviation residuals series for individual stock markets. Notes: The left vertical line (observation #1989) coincides with the outbreak of the US ( )subprime mortgage crisis on August 17, 2007 and the right line (observation # 2289) corresponds with the crisis peak on October 10, 2008. Figure 1a: Czech Republicg p

.07

.08

.05

.06

.03

.04

00

.01

.02

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009.00500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 1b: France

.05

.06

.04

05

.03

.01

.02

.00500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 1c: GermanyFigure 1c: Germany

.352000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.25

.30

15

.20

.10

.15

.00

.05

500 1000 1500 2000 2500

Figure 1d: Hungary

06

.07

.05

.06

.03

.04

.02

.00

.01

500 1000 1500 2000 25002000 2001 2002 2003 2004 2005 2006 2007 2008 2009

500 1000 1500 2000 2500

Figure 1e: Poland g

.040

.032

.036

020

.024

.028

.012

.016

.020

.004

.008

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

500 1000 1500 2000 2500

Figure 1f: Sweden

040

.045

030

.035

.040

.025

.030

.015

.020

.005

.010

500 1000 1500 2000 25002000 2001 2002 2003 2004 2005 2006 2007 2008 2009

500 1000 1500 2000 2500

Figure 1g: United Kingdom

.040

.030

.035

.020

.025

.010

.015

.000

.005

500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 1h: United States (S&P500)

04

.05

.03

.04

.02

.01

.00500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Source: Author’s own estimation based on Datastream data.

Table 3: GARCH(1,1)-M-GED estimation of changes in interbank rates

Czech R. Eurozone Hungary Poland Sweden U.K. 1) U.S. Czech R. Eurozone Hungary Poland Sweden U.K. 1) U.S.Cond. Mean Eq.

(coeff.x100) Constant term Log(GARCH)

+1.615*** +1.690***

-0.030 -0.003

-2.250 -0.003*

-109.63*** -0.017***

-116.55*** -13.46***

-24.05*** -343.1***

-8.39*** -10.31***

Cond. Variance Eq. Constant term

ARCH(1) GARCH(1)

0.000*** 0.543*** 0.153***

0.000*** 0.737*** 0.544***

0.000*** 2.021*** 0.133***

0.000*** 0.149*** 0.413***

0.000*** 0.003*** 0.650***

0.000*** 0.122*** 0.099***

0.000*** 0.061*** 0.399***

GED t 0 718*** 0 712*** 0 386*** 0 402*** 0 693*** 0 636*** 0 479***GED parameter 0.718*** 0.712*** 0.386*** 0.402*** 0.693*** 0.636*** 0.479***GED parameter crisis sub-period

0.517***

0.746***

0.687***

0.118***

0.431***

0.416***

0.646***

Log likelihood

8083

8456

6340

5363

7694

6242

7818

Schwartz Info. Criterion

-6.371 -6.666 -4.993 -4.221 -6.063 -4.906 -6.161

Notes: Three-month interbank rates: Prague Pribor, Euribor, Budapest Bibor, Warsaw Wibor, Stockholm Stibor London Libor USD Libor January 3 2000-September 14 2009 sampleStockholm Stibor, London Libor, USD Libor. January 3, 2000-September 14, 2009 sample period (2540 observations); the financial crisis sub-period is August 17, 2007 – March 31, 2009 (423 observations); *** denotes significance at 1%, ** at 5%, and * at 10%. 1) Due to significant second-order asymmetric effects, the U.K. Libor series is specified with threshold terms as TGARCH(2,2,1)-M-GED; the ARCH(2) coefficient = -0.122***,threshold terms as TGARCH(2,2,1) M GED; the ARCH(2) coefficient 0.122 , TARCH(1) coefficient = 0.069***, TARCH(2) coefficient = -0.069***. Source: Author’s own estimation based on Datastream data.

Table 3 Results All GED parameters are extremely low, indicating extreme

leptokurtosis (severe fat tails), the most pronounced in Hungary and Polandand Poland

This underscores significant systemic or institutional vulnerability to financial distress in the banking sectors

There is volatility increase in the case of Hungarian interbank rates (sum of ARCH(1) and GARCH(1) exceeds unity)

I t b k k t i C h R bli P l d d S d ( ll Interbank markets in Czech Republic, Poland and Sweden (all inflation targeting countries with flexible exchange rates) show overall diminishing volatility (declining interest rate risk)

Figures 3a-g: GARCH one standard deviation residuals series for changes in interbank rates. Note: The left vertical line (observation #1989) coincides with the outbreak of the USNote: The left vertical line (observation #1989) coincides with the outbreak of the US subprime mortgage crisis on August 17, 2007 and the right line (observation # 2289) corresponds with the crisis peak on October 10, 2008. Figure 3a: Czech Republicg p

.32

.362000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.20

.24

.28

08

.12

.16

.00

.04

.08

500 1000 1500 2000 2500500 1000 1500 2000 2500

Figure 3b: Eurozone

.28

.322000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.20

.24

.12

.16

04

.08

.00

.04

500 1000 1500 2000 2500

Figure 3c: Hungary

4

5 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

3

4

2

1

0500 1000 1500 2000 2500

Figure 3d: Poland

.25

.302000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.20

10

.15

.05

.10

.00500 1000 1500 2000 2500

Figure 3e: Sweden

.09

.10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.07

.08

.05

.06

.03

.04

.01

.02

500 1000 1500 2000 2500500 1000 1500 2000 2500

Figure 3f: The United Kingdom

.14

.16 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.10

.12

.06

.08

02

.04

.00

.02

500 1000 1500 2000 2500

Figure 3g: The United States

10

.12 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

.08

.10

04

.06

.02

.04

.00500 1000 1500 2000 2500

Source: Author’s own estimation based on Datastream data.

Table 4: GARCH(1,1)-M-GED estimation of percentage changes (Δlogs) in exchange rates (domestic currency values of 1EUR).(domestic currency values of 1EUR).

Czech R.

CZK/EUR Hungary

HUF/EUR Poland

PLN/EUR Sweden

SEK/EUR U.K.

GBP/EUR U.S.

USD/EUR Cond Mean EqCond. Mean Eq.

(coeff.x1000) Constant term Log(GARCH)

-0.824 0.024

-0.556*** -4.450***

1.537 0.169

-3.360 -0.312

1.710 0.105

4.651** -0.470**

Cond. Variance Eq. Constant term

ARCH(1) GARCH(1)

0.000*** 0.072*** 0.914***

0.000*** 0.173*** 0.879***

0.000*** 0.094*** 0.896***

0.000*** 0.067*** 0.926***

0.000*** 0.052*** 0.944***

0.000*** 0.032*** 0.964***

GED parameter 1.185*** 0.818*** 1.343*** 1.570*** 1.473*** 1.477*** GED parameter crisis sub-period

1.198***

1.243***

1.238***

1.747***

1.577***

1.298***

L lik lih d 10854 10604 9514 10815 10209 9287Log likelihood Schwartz Info. Crit.

10854-8.562

10604-8.362

9514-7.502

10815-8.531

10209-8.052

9287-7.323

Notes: January 3, 2000-September 14, 2009 sample period; crisis sub-period is August 17, 2007-March 31, 2009. *** denotes significance at 1%, ** at 5%, and * at 10%.Source: Author’s own estimation based on Datastream data.

Table 4 Results Leptokurtosis also detected in all cases of exchange rates

(stated in domestic currency values of EUR), although less severe than in all cases of interbank ratessevere than in all cases of interbank rates

Positive risk premium for the HUF/EUR series, especially following the abandonment of the crawling devaluation regime in Hungary

Volatility of the remaining currencies broadly synchronized with the EURwith the EUR

CZK and SEK rates show a mild volatility compression

Figures 5a-f: GARCH one standard deviation residuals series for exchange rates (domestic currency values of one EUR).

Notes: The left solid vertical line (observation #1989) coincides with the outbreak of the US subprime mortgage crisis on August 17, 2007 and the right solid line (observation # 2289) corresponds with the crisis peak on October 10, 2008. Figure 5a: Czech Koruna (CZK) series

.014

.010

.012

.006

.008

.002

.004

.000500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5b: The Hungarian Forint (HUF) series

.024

.028

.016

.020

.012

.004

.008

.000500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5c: The Polish Zloty (PLN) series

020

.024

.016

.020

.012

.004

.008

.000500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5d: The Swedish Krona (SEK) series

.010

.012

.008

004

.006

.002

.004

.000500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5e: The British Pound (GBP) series

.014

.016

010

.012

.014

006

.008

.010

.004

.006

.000

.002

500 1000 1500 2000 25002000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5f: The US Dollar (USD) series

.014

.016

010

.012

.008

.010

.004

.006

.002500 1000 1500 2000 2500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Source: Author’s own estimation based on Datastream data.