Volatility versus Tail Risk: <i>Which One Is Compensated in Equity Funds?</i>

Xiong, James X.; Idzorek, Thomas M.; Ibbotson, Roger G.

doi:10.3905/jpm.2014.40.2.112

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…The decision on whether to purchase a particular financial asset should hinge on how this marginal instrument affects the statistical distribution of the portfolio value (and its central moments), rather than on a stand-alone evaluation of that particular instrument's features (this is the Markowitz, 1952 idea). Xiong et al (2014) apply both the ECVaR and the co-skewness with similar empirical results.…”

Section: Value At Risk Based On Historical Return Distributionmentioning

confidence: 67%

“…None of these are calculated in this paper as we focus on the copula methodology. The Conditional Value at Risk (CVaR), also called mean shortfall or expected tail loss, is calculated as the average loss, conditional on the losses being at least as large as the losses under a Normal distribution VaR would be (Xiong et al, 2014;Rockafellar & Uryasev, 2000). This average is probability-weighted.…”

Section: Value At Risk Based On Historical Return Distributionmentioning

confidence: 99%

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Risky Risk Measures: A Note on Underestimating Financial Risk Under the Normal Assumption

Goodfellow

Salm

2017

CJFA

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This note compares three different risk measures based on the same stock return data: (1) the portfolio variance as in Markowitz (1952), (2) the value at risk based on the historical return distribution, and (3) the value at risk based on a t copula. Unless return series follow a Normal distribution, Normal-based risk measures underestimate risk, particularly so during periods of market stress, when accurate risk measurement is essential. Based on these insights, we recommend that supervisors discontinue to accept Normal-based value at risk estimations. We are happy to share our commented R-code with practitioners who wish to implement our methodology. Risk measurement is the foundation of risk management and hence of vital importance in any financial institution. Supervisory capital requirements according to Basel III or Solvency II are also derived from risk measures. Investors are interested in ratings which are based on risk assessments. This note is therefore relevant to practitioners and supervisors alike.

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Section: Value At Risk Based On Historical Return Distributionmentioning

confidence: 67%

Section: Value At Risk Based On Historical Return Distributionmentioning

confidence: 99%

Risky Risk Measures: A Note on Underestimating Financial Risk Under the Normal Assumption

Goodfellow

Salm

2017

CJFA

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“…This anomaly is discussed in Xiong et al [36] which investigates several risk measures, including volatility and tail risk, and found that tail risk is compensated with higher expected return while volatility seems to be not compensated in equity funds.…”

Section: Introductionmentioning

confidence: 99%

“…Given the importance of volatility in investment decisions (see, for example, [11][19] [34]) and the debate on the advantages and shortcomings of volatility (see, for example, [15][16] [36]), in this contribution we focus on the role of volatility in downside risk reduction and, in particular, in tail risk reduction.…”

Section: Introductionmentioning

confidence: 99%

Volatility vs. Downside Risk: Optimally Protecting Against Drawdowns and Maintaining Portfolio Performance

Barro

Canestrelli

Lanza³

2014

SSRN Journal

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As a consequence of recent market conditions an increasing number of investors are realizing the importance of controlling tail risk to reduce drawdowns thus increasing possibilities of achieving long-term objectives. Recently, so called volatility control strategies and volatility target approaches to investment have gained a lot of interest as strategies able to mitigate tail risk and produce better risk-adjusted returns. Essentially these are rule-based backward looking strategies in which no optimization is considered. In this contribution we focus on the role of volatility in downside risk reduction and, in particular, in tail risk reduction. The first contribution of our paper is to provide a viable way to integrate a target volatility approach, into a multiperiod portfolio optimization model, through the introduction of a local volatility control approach. Our optimized volatility control is contrasted with existing rule-based target volatility strategies, in an out-of sample simulation on real data, to assess the improvement that can be obtained from the optimization process. A second contribution of this work is to study the interaction between volatility control and downside risk control. We show that combining the two tools we can enhance the possibility of achieving the desired performance objectives and, simultaneously, we reduce the cost of hedging. The multiperiod portfolio optimization problem is formulated in a stochastic programming framework that provides the necessary flexibility for dealing with different constraints and multiple sources of risk

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“…For empirical evidence and economic foundations see, for example, [10,19,35]. The importance of volatility in investment decisions is discussed also in [16,17,20,38]. In 2016 the CBOE launched the TYVIX/VIX index to propose a market instrument capturing jointly volatility signals from the Treasury and the equity markets and their inherent trade-offs to determine an effective bond-equity portfolio composition.…”

Section: Introductionmentioning

confidence: 99%

Volatility versus downside risk: performance protection in dynamic portfolio strategies

2018

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Volatility-based and volatility targeting approaches have become popular among equity fund managers after the introduction in 1993 of the VIX, the implied volatility index on the S&P500 at the Chicago Board of Exchange (CBOE), followed, in 2004, by futures and option contracts on the VIX: since then we have assisted to an increasing interest in risk control strategies based on market signals. In January 2000 also the FTSE implied volatility index (FTSEIVI) was introduced at the London Stock Exchange. As a result, specifically in the US, portfolio strategies based on combinations of market indices and derivatives have been proposed by Stock Exchanges and investment banks: one such example is the S&P500 protective put index (PPUT). Early in 2016, relevant to the definition of optimal bond-equity strategies, CBOE launched an Index called TYVIX/VIX featuring an investment rotation strategy based jointly on signals coming from the VIX and the 10-year Treasury Yield implied volatility (TYVIX). All these are rule-based portfolio strategies in which no optimization methods are involved. While rather effective in reducing the downside risk, those index-based portfolio approaches do not allow an optimal risk-reward trade-off and may not be sufficient to control financial risk originated by extreme market drops. To overcome these limits we propose an optimization-based approach to portfolio management jointly focusing on volatility and tail risk controls and able to accommodate effectively the return payoffs associated with option strategies, whose cost as market volatility increases may become excessive. The model is based on a mean absolute deviation formulation and tested in the US equity market over the 2000-2016 period and with a focus on three periods of high volatility, in 2000, 2001 and 2008. The results confirm that optimal volatility controls produce better risk-adjusted returns if compared with rule-based approaches. Moreover the portfolio return distribution is dynamically shaped depending on the adopted risk management approach.

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Volatility versus Tail Risk: Which One Is Compensated in Equity Funds?

Cited by 18 publications

References 17 publications

Risky Risk Measures: A Note on Underestimating Financial Risk Under the Normal Assumption

Risky Risk Measures: A Note on Underestimating Financial Risk Under the Normal Assumption

Volatility vs. Downside Risk: Optimally Protecting Against Drawdowns and Maintaining Portfolio Performance

Volatility versus downside risk: performance protection in dynamic portfolio strategies

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