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Empirical Economics

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05-09-2023

Research on the effectiveness of the volatility–tail risk-managed portfolios in China’s market

Authors: Zirui Guo, Yihan Li, Guangyan Jia

Published in: Empirical Economics | Issue 3/2024

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Abstract

This paper attempts to extend the approach of quantitative investment and provide investors with suggestions about volatility timing. Based on the volatility-managed portfolios strategy, we propose two volatility–tail risk-managed portfolios strategies by combining the volatility and tail risk forecasting methods. We subject seven indices in China’s stock market to verify the performance of these two strategies. The empirical results show that volatility–tail risk-managed portfolios yield better performance than buy-and-hold portfolios and volatility-managed portfolios in terms of annualized average returns, Sharpe ratio, maximum drawdown and Calmer ratio. Furthermore, we find that the effectiveness also depends on the volatility clustering of portfolios and the selection of investment periods.

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For monthly rebalancing, the time of adjustment can be set to the first trading day of the month t.

Volatility indicates the degree of asset returns’ dispersion and is generally measured as the sample standard deviation, but sometimes variance is used also as a volatility measure (Poon 2005).

When we use this method to forecast volatility and then manage portfolios (that is the realized volatility management in Sect. 3.1), this strategy is the same as volatility-managed portfolios strategy proposed by Moreira and Muir (2017).

Cederburg et al. (2020) suggest that a positive \(\alpha \) in regression (5) is a lower bar for declaring success of a given managed strategy relative to a positive Sharpe ratio difference in a direct comparison. Therefore, we employ the Sharpe ratio instead of the \(\alpha \) used by Moreira and Muir (2017).

The Calmar ratio is a common metric that combines excess return and maximum drawdown. The Sterling ratio and the Burke ratio are also metrics which combine excess return and maximum drawdown(Eling and Schuhmacher 2007).

We use different distributions to calculate volatility and tail risk, but it is not contradictory for the following reasons: First, the two distributions describe different objects: the GHD in the GARCH model describes the distribution of the random variable in Eq. (7), thus indirectly affects the distribution of returns, whereas the skew-t distribution in the ES calculation is directly fitted by the series of returns within the window. Second, the window lengths for the two calculations are different.

Table 13 shows the statistical significance of the return and Sharpe ratio differences.

We also plot the time-varying weight of the risky assets for strategy RV*ES and EGARCH*ES in “Appendix” Figs. 6 and 7.

Only SSEC, SSEA, SCI, SZSC, SZSA are managed here, as SSE50 and CSI300 were published since 2004 and 2005, these two indices are not valuable to discuss in the above time segment.

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Title: Research on the effectiveness of the volatility–tail risk-managed portfolios in China’s market
Authors: Zirui Guo
Yihan Li
Guangyan Jia
Publication date: 05-09-2023
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 3/2024
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-023-02493-9

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