Designing financial instruments to protect against financial stress
Submitting InstitutionUniversity of Oxford
Unit of AssessmentEconomics and Econometrics
Summary Impact TypeEconomic
Research Subject Area(s)
Mathematical Sciences: Statistics
Commerce, Management, Tourism and Services: Banking, Finance and Investment
Summary of the impact
Research by Oxford econometricians provided the basis for innovative new
methods for predicting periods of potential financial stress and providing
protection for investors against extreme events. During periods of
financial stress, equity funds tend to sharply lose value while volatility
tends to increase. Adding some long volatility exposure to a standard
equity portfolio can significantly improve the tail behaviour of a
portfolio. However, it is expensive to continually hold volatility
contracts due to the volatility risk premium. Researchers at Man Group
have applied the Oxford research to create new strategies to protect
against tail risk and these are incorporated in their Tail Protect fund
launched in October 2009.
- Professor Neil Shephard joined Oxford University in 1991, first as a
Research Fellow, then as an Official Fellow at Nuffield College, Oxford,
and since 1996 he has been a Professor of Economics. In 2007, he founded
the Oxford-Man Institute for Quantitative Finance with core funding from
the Man Group, which he directed until 2011.
- Dr Kevin Sheppard has been a University Lecturer since 2004 and is a
Fellow of the Oxford-Man Institute.
- Professor Ole Barndorff-Nielsen is Professor Emeritus at the Thiele
Centre for Applied Mathematics in Natural Science, University of Aarhus.
- Dr Diaa Noureldin was appointed to a Postdoctoral Research Fellowship
in Economics in 2011, and prior to this he was a DPhil student in the
Department of Economics and supervised by Professor Shephard and Dr
The Oxford-Man Institute of Quantitative Finance (OMI) was established in
2007 with core funding from the Man group, under the directorship of
Shephard, as a multidisciplinary centre for research on quantitative
aspects of finance. The OMI provides an innovative model of collaboration
between academic researchers and their counterparts in the commercial
world with the OMI co-located with the Man Research Laboratory (MRL) which
houses long-term commercial researchers from AHL. AHL is a dedicated group
within the Man Group who specialise on using purely quantitative
investment strategies. The two centres are independent — OMI's focus is on
academic research for the public domain whilst AHL's research is
commercial and profit driven — but researchers come together for seminars,
workshops and conferences, allowing OMI researchers to benefit from
practitioner expertise and Man's commercially-focused staff to benefit
from discussions with leading academics in the field. This research was
born in, and benefits from, this co-creative environment and is a product
of direct engagement beyond academia with the financial sector.
From the late 1990s onwards, Professor Shephard has carried out
significant work with Prof Barndoff-Nielsen, developing new statistical
methods which allow researchers to extract econometrically useful long and
medium term information from high frequency financial data. These methods
have been very widely applied in academic research, allowing various
researchers to develop a new generation of predictive models to be built
for financial volatility, a major component of financial risk.
Shephard's early work [Section 3: R1] showed how to frame
realised volatility as a simple non-parametric estimator of the increment
to quadratic variation and derived a central limit theory for this
estimator. This theory was used to provide a principled and simple way of
carrying out volatility forecasting using a non-parametric continuous time
stochastic volatility model. This research has been extended to the
multivariate case in Barndorff-Nielsen and Shephard [R3] and has
been further developed to explore how to mitigate the impact of market
microstructure effects [R2].
Other extensions to the research include the development of the first
non-parametric quantifiers of the importance of jumps in financial markets
[E.G. R4Since these first papers there has been extensive
econometric research in this area, allowing researchers to have a better
gauge of extreme price moves in equity markets. Unusual moves are critical
in terms of the pricing of risk, but also for their potential spillover
into the macroeconomy.
In more recent Oxford research, Sheppard and Shephard produced a simple
volatility forecasting model called the high-frequency-based volatility
(HEAVY) model [R5]. Building on the results of Shephard's earlier
2002 paper [R2], this used high frequency data to make low
frequency volatility forecasts. In subsequent research, the techniques
were extended to the multivariate case [R6]The key advantage to
this kind of model, compared to traditional GARCH or RiskMetrics models,
is that it is able to react quickly to abrupt changes in the level of
volatility, which is a feature of nearly all periods of financial
distress. The reason for this is that it is built using realised
volatility measures of daily volatility, rather than using absolute daily
returns. These realised volatility measures are simply much more
informative, which means it is not necessary to temporally average them
over many months to produce coherent medium term forecasts. Such models
can be used to adjust portfolio allocations and potentially to value
References to the research
[R1] Barndorff-Nielsen, O.E. and N. Shephard (2002) "Econometric
analysis of realised volatility and its use in estimating stochastic
volatility models", Journal of the Royal Statistical Society,
Series B, 63, 2002, 253-280.
[R2] **Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde and N.
Shephard (2008) "Designing realised kernels to measure the ex-post
variation of equity prices in the presence of noise", Econometrica,
2008, 76, No. 6, 1481-1536.
[R3] *Barndorff-Nielsen, O.E. and N. Shephard (2004) "Econometric
analysis of realised covariation: high frequency based covariance,
regression and correlation in financial economics", Econometrica,
2004, 72, 885-925
[R4] Barndorff-Nielsen, O.E. and N. Shephard (2006) "Econometrics
of testing for jumps in financial economics using bipower variation", Journal
of Financial Econometrics, 2006, 4, 1-30.
[R5] **Sheppard, K.K. and N. Shephard (2010) "Realising the
future: forecasting with high frequency based volatility (HEAVY) models",
Journal of Applied Econometrics, 25, 197-231. (Revised version
of Economics Discussion Paper Series 438, July 2009) Winner of
the Richard Stone Prize in Applied Econometrics 2012.
[R6] **Noureldin, D., N. Shephard and K.K. Sheppard (2012) "Multivariate
high-frequency-based volatility (HEAVY) models", Journal of Applied
Econometrics, 27, 907-933.(Revised version of Department
of Economics Discussion Paper Series 533, Feb 2011)
Econometrica is a `top-five' world leading general interest
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Journal of Applied Econometrics is a leading field journal for
econometrics and is classed as "A" in the Combes-Linnemer (2010) ranking.
Journal of Financial Econometrics is a leading field journal for
Journal of the Royal Statistical Society (series B) is a leading
statistics journal, focusing on statistical methodology.
Details of the impact
As described above (section 2), the OMI provides a fruitful co-creative
collaborative environment for researchers and their counterparts in the
commercial world via co-location within the Man Research Laboratory (MRL)
and a series of collaborative seminars, workshops and conferences. It was
through the research seminars that AHL researchers became aware of
Professor Shephard's research on volatility, and, in particular, of the
predictive model that he had developed with Dr. Kevin Sheppard [R6].
The predictive research methodology of Shephard and Sheppard provided the
basis for a decision rule developed by AHL researchers to generate what
they termed the `spike detection strategy'. This tool was then used by AHL
to attempt to statistically hedge significant `tail events'. Shephard and
Sheppard were consulted over the subsequent development of the spike
detection strategy, providing feedback on the approach adopted and
pointing to some fruitful directions for subsequent work [Section 5:
Shephard and Sheppard's research was particularly pertinent because, for
some time before AHL became aware of the work [R6], Man Group had
identified the need to build an investment fund that protected a hedge
fund portfolio against so-called `tail events' [C1] . These `tail
events' (very bad monthly equity portfolio returns) are difficult to
predict and can lead to challenging periods both for institutional
investors (e.g. pension companies) or fund managers. Various asset classes
have traditionally been used to provide some insurance against severe
equity losses. The most famous are bonds, although, in recent years, bond
and equity returns have been positively correlated.
AHL researchers recognised that volatility contracts are a class of
investable assets which do perform well during periods of financial stress
[C1]. (A simple version of such a contract has a payoff related to
the square root of the sum of squares of daily returns of the underlying
measured over a pre-specified period). Realised volatility has nearly
always been very high during periods where equity indexes have fallen
significantly. There are various economic theories as to why this may be
the case. Overall, however, it means that buying volatility contracts
provides a form of statistical insurance on equity positions. If equities
rally, the contract will tend to pay off little; if equities fall
dramatically, it will tend to pay off very substantially. This suggests
that an equity portfolio could have some of its `left hand tail' risk
reduced by holding a small amount of volatility contracts. The difficulty
with this conclusion, and the resulting action, is that the expected
long-run payoff of variance contracts is substantially negative. The size
of expected loss is called the volatility risk premium, and it rewards
sellers of volatility contracts for being exposed to the risk of
substantial losses at the same time as equities are losing.
The critical value of the predictive model developed by Shephard and
Sheppard [R6] is that it provided the methodological basis for a
strategy to predict periods of stress in the financial markets, and so
reduce the costs of holding volatility contracts [C1]. Man Group's
`spike detection' strategy, derived from Shephard and Sheppard's
predictive model [R6] "looks at noise in the markets and tries to
translate that noise into when a crisis is likely to take place" [C2].
When a challenging period is detected, exposure to volatility is increased
to provide greater protection. The spike detection strategy is a key
component of the Man group's TailProtect Fund. In a 2012 interview, Sandy
Rattray (CEO of AHL) described the strategy as the "more creative and
innovative part" of the Man Group's TailProtect Fund (Dickinson, 2012) [C2].
The TailProtect fund opened to external investors in January 2011, and in
2012 won the annual European award for most innovative hedge fund [C3].
Sources to corroborate the impact
[C1] Chief Scientist, Man Group, confirms that the research by
Shephard and Sheppard provided the basis for the spike detection strategy
that forms an important component of Man group's TailProtect fund. (Letter
[C2] Dickinson, C. (2012) "Man Tail Protect: Man Investments/GLG
Partners", Hedge Funds Review, 01 June 2012. (http://www.risk.net/hedge-funds-review/profile/2245251/man-tail-protect-man-investments-glg-partners)