Volatility versus downside risk: performance protection in dynamic portfolio strategies

Abstract

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.

Optimizing Pension Outcomes Using Target‐Driven Investment Strategies: Evidence from Three Asian Countries with the Highest Old‐Age Dependency Ratio*

“…Moreover, the target volatility mechanism is found to be an effective capital measure for different assets over a long investment timeframe (Albeverio and Wallbaum 2019). Furthermore, it is well acknowledged that pension management strategies with a target risk management component might be more suitable for pension investors in comparison to traditional target-date pension strategies in volatile markets (Elton et al 2016;Moreira and Muir 2017;Barro et al 2019).…”

Improving time series and cross-sectional momentum trading strategies using stochastic programming

“…When the distribution is specified, Monte Carlo simulation can be applied to generate scenarios by repeated random sampling from the specified distribution (Ripley, 1987;Sharifi et al, 2016;Guo and Ryan, 2020b). On the other hand, when the joint distribution of the uncertain parameters is not specified, bootstrapping, which samples the historical data randomly with replacement, is a straightforward way to generate scenarios (Zou et al, 2019;Barro et al, 2019;Thomann, 2021). Although bootstrapping is easy to implement, it is unable to capture some inherent structure of stock returns.…”