Risk Measurement and Management for Hedge Funds

2017 ◽  
Author(s):  
Guillaume Weisang
Keyword(s):  
Hedge Funds ◽  
Value At Risk ◽  
Risk Measures ◽  
Hedge Fund ◽  
Risk Measurement ◽  
Tail Risk ◽  
Equity Risk ◽  
Wide Range ◽  
Measurement Tools ◽  
Hedge Fund Risk

Risk measurement and management is an important and complex subject for hedge fund stakeholders, managers, and investors. Given that hedge funds dynamically trade a wide range of financial instruments, their returns show tail risk and nonlinear characteristics with respect to many financial markets that require advanced downside risk measures, such as value-at-risk, expected shortfall, and tail risk, to capture risk adequately. This chapter reviews the nature of these risks and presents the measurement tools needed, focusing on fixed-income instruments, derivative securities, and equity risk measurement, and stressing the importance of frequent assessment to capture the possibly rapidly changing risk profiles of hedge funds. This chapter also provides an overview of the linear factor models that investors often use to measure hedge fund risk exposures along many risk factors.

scholarly journals Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

2021 ◽  
Vol 14 (11) ◽  
pp. 540
Author(s):  
Eyden Samunderu ◽  
Yvonne T. Murahwa
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Hedge Funds ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Extensive Literature ◽  
Data Set ◽  
Normally Distributed

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

scholarly journals Modelling Hedge Fund Indices Using Levy Processes

2021 ◽  
Author(s):  
Ugochi T. Emenogu
Keyword(s):  
Hedge Funds ◽  
Value At Risk ◽  
Lévy Processes ◽  
Hedge Fund ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Levy Processes ◽  
Empirical Characteristic Function ◽  
Empirical Comparison ◽  
Double Exponential

In this thesis, the use of Levy processes to model the dynamics of Hedge fund indices is proposed. Merton (1976) and Kou (2002) models which differ on the specifcation of the jump components are employed to model hedge funds in continuous time. Secondly, an alternative to the Maximum Likelihood Estimation (MLE) method, Empirical Characteristic Function (ECF) estimation method, is explored in our analysis and compared to MLE. The Cumulant Matching Method (CMM) is used in getting the starting parameters; and the method that overcomes the major problem associated with this estimation method is outlined. Calibration shows that these two models t the data well, however, the empirical comparison shows that double exponential jumps are more consistent with the empirical data. Each fund's exposure to risk is calculated using Monte Carlo Value-at-Risk (VaR) estimation method.

scholarly journals Robustness in the Optimization of Risk Measures

2021 ◽  
Author(s):  
Paul Embrechts ◽  
Alexander Schied ◽  
Ruodu Wang
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Financial Regulation ◽  
Optimization Problems ◽  
Risk Measures ◽  
Risk Measurement ◽  
Loss Functions ◽  
Insurance Regulation ◽  
Convex Risk Measures ◽  
Comparative Advantages

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to nonrobust optimizers, whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.

Limit Theory for Forecasts of Extreme Distortion Risk Measures and Expectiles*

2019 ◽  
Author(s):  
Yannick Hoga
Keyword(s):  
Value At Risk ◽  
Risk Measures ◽  
Limit Theory ◽  
Extreme Risk ◽  
Wide Range ◽  
Scale Models ◽  
Adequate Coverage ◽  
Nonparametric Alternatives ◽  
Do So ◽  
Distortion Risk Measures

Abstract We develop central limit theory for tail risk forecasts in general location–scale models. We do so for a wide range of risk measures, viz. distortion risk measures (DRMs) and expectiles. Two popular members of the class of DRMs are the Value-at-Risk and the Expected Shortfall. The forecasts we consider are motivated by a Pareto-type tail assumption for the innovations and allow for extrapolation beyond the range of available observations. Simulations reveal adequate coverage of the forecast intervals derived from the limit theory. An empirical application demonstrates that our estimators outperform nonparametric alternatives when forecasting extreme risk in sufficiently large samples.

Margins and Hedge Fund Contagion

2011 ◽  
Vol 46 (5) ◽  
pp. 1227-1257 ◽  
Author(s):  
Evan Dudley ◽  
Mahendrarajah Nimalendran
Keyword(s):  
Hedge Funds ◽  
Risk Measures ◽  
Hedge Fund ◽  
Subprime Crisis ◽  
Order Of Magnitude ◽  
Funding Risk

AbstractFunding risk measures the extent to which a fund can borrow money by posting collateral. Using a novel measure of funding risk based on futures margins, we are able to empirically identify the mechanism by which changes in funding risk affect the likelihood of contagion. An increase in margins of the order of magnitude observed during the subprime crisis increases the probability of contagion among certain types of funds by up to 34%. Our analysis shows that some types of hedge funds are more vulnerable to contagion than others. Our results also suggest that policies that limit the magnitude of changes in margins over short periods of time may reduce the likelihood of contagion among hedge funds.

Hedge fund risk measurement

Wilmott ◽  
2004 ◽  
Vol 2004 (4) ◽  
pp. 28-31 ◽  
Author(s):  
Aaron Brown
Keyword(s):  
Hedge Fund ◽  
Risk Measurement ◽  
Hedge Fund Risk

scholarly journals Hedge Fund Flows and Performance Streaks: How Investors Weigh Information

2021 ◽  
Author(s):  
Guillermo Baquero ◽  
Marno Verbeek
Keyword(s):  
Hedge Funds ◽  
Hedge Fund ◽  
Cash Flows ◽  
Fund Flows ◽  
Hot Hand ◽  
Highly Sensitive ◽  
Wide Range ◽  
Processing Abilities ◽  
And Performance ◽  
Types Of Information

Cash flows to hedge funds are highly sensitive to performance streaks, a streak being defined as subsequent quarters during which a fund performs above or below a benchmark, even after controlling for a wide range of common performance measures. At the same time, streaks have limited predictive power regarding future fund performance. This suggests investors weigh information suboptimally, and their decisions are driven too strongly by a belief in continuation of good performance, consistent with the “hot hand fallacy.” The hedge funds that investors choose to invest in do not perform significantly better than those they divest from. These findings are consistent with overreaction to certain types of information and do not support the notion that sophisticated investors have superior information or superior information processing abilities. This paper was accepted by David Simchi-Levi, finance.

scholarly journals Market Risk Measurement

2020 ◽  
Author(s):  
Emese Lazar ◽  
Ning Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Preliminary Analysis ◽  
Risk Measures ◽  
Market Risk ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Regulatory Framework ◽  
Bank Capital ◽  
Bank Capital Requirements

This chapter presents a preliminary analysis on how some market risk measures dramatically increased during the COVID-19 pandemic, with measures computed over longer horizons experiencing more pronounced effects. We provide examples when regulatory market risk measurement proved to be suboptimal, overestimating risk. A further issue was the large number of Value-at-Risk ‘exceptions’ during the first few months of the crisis, which normally leads to overinflated bank capital requirements. The current regulatory framework should address these problems by suggesting improvements to the calculation of risk measures and/or by modifying the rules which determine capital requirements to make them appropriate and realistic in crisis situations.

A Theory for Measures of Tail Risk

2021 ◽  
Author(s):  
Fangda Liu ◽  
Ruodu Wang
Keyword(s):  
Value At Risk ◽  
Financial Regulation ◽  
Risk Measures ◽  
Risk Measure ◽  
Continuity Condition ◽  
Tail Risk ◽  
Dual Representations ◽  
Joint Properties ◽  
Risk Aggregation ◽  
Popular Classes

The notion of “tail risk” has been a crucial consideration in modern risk management and financial regulation, as very well documented in the recent regulatory documents. To achieve a comprehensive understanding of the tail risk, we carry out an axiomatic study for risk measures that quantify the tail risk, that is, the behaviour of a risk beyond a certain quantile. Such risk measures are referred to as tail risk measures in this paper. The two popular classes of regulatory risk measures in banking and insurance, value at risk (VaR) and expected shortfall, are prominent, yet elementary, examples of tail risk measures. We establish a connection between a tail risk measure and a corresponding law-invariant risk measure, called its generator, and investigate their joint properties. A tail risk measure inherits many properties from its generator, but not subadditivity or convexity; nevertheless, a tail risk measure is coherent if and only if its generator is coherent. We explore further relevant issues on tail risk measures, such as bounds, distortion risk measures, risk aggregation, elicitability, and dual representations. In particular, there is no elicitable tail convex risk measure other than the essential supremum, and under a continuity condition, the only elicitable and positively homogeneous monetary tail risk measures are the VaRs.

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