Industry Spotlight

Autumn 2014

Most hedge funds report

their NAVs monthly, some-

times with a signi!cant

delay. But more timely

information can be of great

value to hedge fund investors who wish

to understand intra-month gains/losses,

plan redemptions or contributions, or even

consider potential hedging strategies.

To bridge this gap, a recent research

paper1 explored an approach for hedge fund

investors to infer daily risk characteristics

from monthly observed hedge fund returns

data. "e goal of this research was: (a) to

demonstrate whether daily risk projections

are possible both across various hedge fund

strategies and market volatility regimes, and

(b) to attribute precision of daily projec-

tions to various factors, such as model type,

reporting lag, use of monthly hedge fund

returns versus daily, etc.

Methodology and data

"e underlying idea was to simulate the daily

performance of a hedge fund by creating a

factor proxy portfolio, based on a monthly

factor model, by using market indices and

factors with available daily data. "e research

deployed a dynamic regression model, as

opposed to a static one, to enhance the

capture of dynamic factor exposures, so

improving the quality of performance projec-

tions. In addition, it used a factor selection

procedure to select optimal factors from a

pool of more than 100 market factors.

Other analytical techniques commonly use

a rolling-window regression methodology

to account for a hedge fund’s dynamic factor

exposures over time. However, academic

research suggests this approach is not

applicable to most hedge fund strategies. In

order to overcome this and other drawbacks,

this research study used a dynamic regres-

sion technique called dynamic style analysis

(DSA). DSA assumes time-varying factor

1 Li, Markov, Wermers, “Monitoring Daily Hedge Fund

Performance When Only Monthly Data is Available”, Journal

of Investment Consulting, Vol 14, 2013. Winner of the

Journal of Investment Consulting’s 2012 Academic Paper

Competition.

exposures, building both e#cient parameter-

calibration techniques and factor-selection

procedures based on predictive cross-valida-

tion rather than on pure !t.

Estimating daily VaR

Academics agree that calculating VaR esti-

mated using monthly data is of limited use.

At the same time, applying DSA to generate

daily data provides enough return data

points either to !t in a parametric distribu-

tion function or to use empirical quantiles

for VaR estimation.

In the charts below, we compare the rolling

daily VaR estimates for the four major HFRX

indices (Relative Value, Macro, Event Driven

and Equity Hedge) obtained using daily index

returns (yellow line), and through monthly

modelling with daily factors (blue line),

during the period of high market volatility in

2007-2008.2

We see that even if only monthly data were

made available (from the HFRX indices), DSA

allows us to derive precise factor exposures

that track the original daily risk estimates

very closely, especially the spike around

October 2008. If only monthly data were

used to compute VaR, the increase in VaR

only becomes apparent after several months.

So an investor monitoring risk using DSA

would discover this much earlier.

Enhancing risk management

Given the lack of daily hedge fund returns,

this new approach provides investors with

greater insight into what risks and pain their

hedge funds are experiencing intra-month,

which could serve as an early warning

system. It allows hedge fund investors and

analysts to monitor daily hedge fund risk

and to make proactive investment decisions

intra-month. Portfolio managers and inves-

tors can apply this approach to improve their

existing risk management measures.

(DISCLAIMER: !e model does not attempt nor claim to

understand the trades, leverage or positions that a hedge fund

could take on a daily basis.).

2 Parametric, Cornish-Fisher expansion VaR estimated using

252-day, exponentially-weighted windows.

It’s 4pm. Do you know where your hedge funds are?New research shows that dynamic style analysis could give managers an early warning about sudden increases in risk

by Kieran Dolan, Managing Director, Citco Alternative Investor Services (CAIS)kdolan@citco.com

“ Applying DSA to generate

daily data provides

enough return data points

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HFRX Event Driven Index

Daily Projection Returns

HFRX Equity Hedge Index

Daily Projection Returns

HFRX Macro Index

Daily Projection Returns

HFRX Relative Value Arbitrage Index

Daily Projection Returns

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aR

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