Extreme downside risk: implications for asset pricing and portfolio management
Nguyen, Linh Hoang
Date: 13 May 2015
University of Exeter
PhD in Finance
This thesis investigates different aspects of the impact of extreme downside risk on stock returns. We first investigate the impact at market level, where the return of the stock market index is expected to be positively correlated to its tail risk. More specifically, we incorporate Markov switching mechanism into the framework of Bali ...
This thesis investigates different aspects of the impact of extreme downside risk on stock returns. We first investigate the impact at market level, where the return of the stock market index is expected to be positively correlated to its tail risk. More specifically, we incorporate Markov switching mechanism into the framework of Bali et al. (2009) to analyse the relationship between risk and returns under different market regimes. Interestingly, although highly significant in calm periods, the tail risk-return relationship cannot be captured during turbulent times. This is puzzling since this is the time when the distress risk is most prominent. We show that this pattern persists under different modifications of the framework, including expanding the set of state variables and accounting for the non-iid feature of return process. We suggest that this result is due to the leverage and volatility feedback effects. To better filter out these effects, we propose a simple but effective modification to the risk measures which reinstates the positive extreme risk-return relationship under any state of market volatility. The success of our method provides insights into how extreme downside risk is factored into expected returns. In the second investigation, this thesis explores the impact of extreme downside risk on returns in a security level analysis. We demonstrate that a stock with higher tail risk exposure tends to experience higher average returns. Motivated by the limitations of systematic extreme downside risk measures in the literature, we propose two groups of new ‘co-tail-risk’ measures constructed from two different approaches. The first group is the natural development of canonical downside beta and comoment measures, while the second group is based on the sensitivity of stock returns on innovations in market systematic crash risk. We utilise our new measures to investigate the asset pricing implication of extreme downside risk and show that they can capture a significant positive relationship between this risk and expected stock return. Moreover, our second group of ‘co-tail-risk’ measures show a highly consistent performance even in extreme settings such as low tail threshold and monthly sample estimation. The ability of this measure to generate a number of observations given limited return data solves one of the most challenging problems in tail risk literature. In the last investigation, this thesis examines the influence of extreme downside risk on portfolio optimisation. It is motivated by the evidence in Chapter 4 regarding the size pattern of the extreme downside risk impact on stock returns where the impact is larger for small stocks. Accordingly, portfolio optimisation practice that focuses on tail risk should be more effective when applied to small stocks. In comparing the performance of mean-Expected Tail Loss against that of mean-variance across size groups of Fama and French’s (1993) sorted portfolios, we confirm this conjecture. Moreover, we further investigate the performance of different switching approaches between mean-variance and mean-Expected Tail Loss to utilise the suitability of these optimisation methods for specific market conditions. However, our results reject the use of any switching method. We demonstrate the reason switching could not enhance performance is due to the invalidity of the argument regarding the suitability of any optimisation method for a specific market regime.
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