This document summarizes research into using a technique called dynamic style analysis (DSA) to estimate daily risk characteristics for hedge funds from their monthly return data. DSA uses a dynamic regression model and market factors to simulate daily hedge fund performance. The research found that DSA allowed accurate derivation of daily risk estimates like Value at Risk, even during periods of high volatility. This provides investors with greater insight into intra-month risks than only using monthly data, helping improve risk management.

1. Industry Spotlight
Autumn 2014
M
ost hedge funds report
their NAVs monthly, some-
times with a significant
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. The 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
The 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. The 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 efficient parameter-
calibration techniques and factor-selection
procedures based on predictive cross-valida-
tion rather than on pure fit.
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 fit 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. ᇝ
(DISCLAIMER: The 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
95%VaR
95%VaR