Tail Risk Management for Multi-Asset Multi-Factor Strategies
David Chambers (University of Cambridge), et al.
January 8, 2019
Multi-asset multi-factor portfolio allocation is typically centred around a risk-based allocation paradigm, often striving for maintaining equal volatility risk budgets. Given that the common factor ingredients can be highly skewed, we specifically incorporate the notion of tail risk management into the construction of multi-asset multi-factor portfolios. Indeed, we find that the minimum CVaR concentration approach of Boudt, Carl and Peterson (2013) effectively mitigates the dangers of tail risk concentrations. Yet, diversifying across multiple assets and style factors can be in and of itself a good means of tail risk management, irrespective of the risk-based allocation technique employed.
A New Tail-Based Correlation Measure and its Application in Global Equity Markets
Jinjing Liu (World Bank)
January 17, 2019
The co-dependence between assets tends to increase when the market declines. This paper develops a correlation measure focusing on market declines using the expected shortfall (ES), referred to as the ES-implied correlation, to improve the existing value at risk (VaR)-implied correlation. Simulations which define period-by-period true correlations show that the ES-implied correlation is much closer to true correlations than is the VaR-implied correlation with respect to average bias and root-mean-square error. More importantly, this paper develops a series of test statistics to measure and test correlation asymmetries, as well as to evaluate the impact of weights on the VaR-implied correlation and the ES-implied correlation. The test statistics indicate that the linear correlation significantly underestimates correlations between the US and the other G7 countries during market downturns, and the choice of weights does not have significant impact on the VaR-implied correlation or the ES-implied correlation.
Systematic Extreme Downside Risk
Richard D. F. Harris (University of Exeter), et al.
February 25, 2019
We propose new systematic tail risk measures constructed using two different approaches. The first is a non-parametric measure that captures the tendency of a stock to crash at the same time as the market, while the second is based on the sensitivity of stock returns to innovations in market crash risk. Both tail risk measures are associated with a significantly positive risk premium after controlling for other measures of downside risk, including downside beta, coskewness and cokurtosis. Using the new measures, we examine the relevance for investors of the tail risk premium over different horizons.
Momentum Crashes and Variations to Market Liquidity
Hilal Butt (University of Karachi) and Nader Virk (University of Plymouth)
January 11, 2019
We document that the variation in market liquidity is an important determinant of momentum crashes that is independent of other known explanations surfaced on this topic. This relationship is driven by the asymmetric large return sensitivity of short-leg of momentum portfolio to changes in market liquidity that flares the tail risk of momentum strategy in panic states. This identification explains the forecasting ability of known predictors of tail risk of momentum strategy such that the contemporaneous increase in market liquidity predominantly sums up the trademark negative relationship between predictors and future momentum returns. Our results are robust using a different momentum portfolio and alternative measures of market liquidity that make a substantial part of the common source of variation in aggregate liquidity.
Crowded Trades and Tail Risk
Gregory W. Brown (University of North Carolina), et al.
February 2, 2019
A growing body of research examines the implications of common holdings for asset price determination; however, far less is known about the impact of hedge fund ownership concentration on risk and return. Yet, hedge fund positions are an important component of the degree of crowdedness because these investment vehicles tend to be particularly active in their pursuit of outperformance, they often take highly concentrated positions, and they utilize leverage and short sales. Using a large database of U.S. equity position-level holdings for hedge funds, we measure the degree of security level crowdedness. We construct a new factor by taking the difference between returns of high and low crowdedness portfolios. The average return on the crowdedness factor is sizable, and its variation is distinct from other traditional risk factors for U.S. equities. When hedge fund returns are regressed onto other risk factors and the crowdedness factor, the exposures to the latter are statistically and economically significant in explaining hedge fund return variation. Most important, the crowdedness factor is related to downside “tail risk” as stocks with higher exposure to crowdedness experience relatively larger drawdowns during periods of market distress. This tail risk extends to hedge fund portfolio returns as the crowdedness factor explains why some funds experience relatively large drawdowns.
Assessing Macroeconomic Tail Risk
Francesca Loria (European University Institute), et al.
April 19, 2019
What drives macroeconomic tail risk? To answer this question, we borrow a definition of macroeconomic risk from Adrian et al. (2019) by studying (left-tail) percentiles of the forecast distribution of GDP growth. We use local projections (JordÃ , 2005) to assess how this measure of risk moves in response to economic shocks to the level of technology, monetary policy, and financial conditions. Furthermore, by studying various percentiles jointly, we study how the overall economic outlookâ€”as characterized by the entire forecast distribution of GDP growthâ€”shifts in response to shocks. We find that contractionary shocks disproportionately increase downside risk, independently of what shock we look at.
Adjusted Sharpe Ratio: Some Caveats
Didier Maillard (Amundi Asset Management)
November 14, 2018
Researchers and investors are concerned with the shortcomings of various measures of portfolio management performances, among them the famous Sharpe ratio. In particular, the Sharpe ratio does not give due consideration to tail risk: negative skewness and fat tails, which justly are a matter of concern for investors. Various ways of correcting the Sharpe ratio have been proposed and continue to be proposed. One of them is the concept of Adjusted Sharpe Ratio (ASR) which gives a performance measure easy to compute from the basic statistics of returns. The aim of this paper is to trace back the derivation of this formula and stress the assumptions and approximations needed for obtaining it. Those caveats should be kept in mind when using the ASR.