Modelling the downside risk potential of mutual fund returns

Abstract

Investors are becoming more sensitive about returns and losses, especially when the investments are exposed to downside risk potential in the financial markets. Despite the computational intensity of the downside risk measures, they are very widely applied to construct a portfolio and evaluate performance in terms of the investors’ loss aversion. Value-at-risk (VaR) has emerged as an industry standard to analyze the market downside risk potential. The approaches used to measure VaR vary from the standard approaches to more recently introduced highly sophisticated volatility models. In this paper, the standard approaches (student-t-distribution, log normal, historical simulation) and sophisticated volatility models (EWMA, GARCH (1,1)) both have been used to estimate the VaR of mutual funds in the Saudi Stock Exchange between June 2017 and June 2020. The VaR approaches have been subjected to conditional coverage backtest to identify the model that is the best at predicting VaR. The empirical coverage probability of the models reveals that EWMA was able to capture VaR better than the other models at a higher significance level followed by GARCH (1,1).

Keywords:

• Downside risk
• VaR
• student-t
• log normal
• historical simulation
• EWMA
• GARCH(1,1)

JEL Classifications:

• C14
• G11

PUBLIC INTEREST STATEMENT

Investors prefer avoiding losses to making gains, because any losses are psychologically twice as powerful as similar gains. There is an increasing demand for protection against market falls with a wide range of instrument and strategies to capture the downside risk potential of an investment. As on Value-at-Risk (VaR) is a risk measurement tool to measure market risk, and more specifically, the maximum downside risk potential of a financial instrument. The present study captures the VaR of mutual fund returns using competitive measures such as GARCH (1,1) & EWMA and has identified the best tool available to capture volatility clustering. Using the downside risk, each individual investor can customize the risk calculation setting their tolerance level and create an optimal portfolio mix.

Acknowledgements

The author would like to thank the Deanship of Graduate Studies and Scientific Research, Dar Al-Uloom University, Riyadh, Saudi Arabia, for funding the research work.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the Deanship of Graduate Studies and Scientific Research, Dar Al-Uloom University, Riyadh, Saudi Arabia

Notes on contributors

Sunitha Kumaran

Risk can deceive even the most experienced investor. Intensive work on modelling or capturing risk has been the area mostly researched by the author. Risks that are not captured or considered in the portfolio management process will lead to future issues and reduce profits, hence it is critical to capture risk at ease. The current study has been an attempt to capture the downside risk and suggest a best method to quantity the same. The outcomes of the paper can enable investors to customize the risk estimation and set the tolerance level for the investment while setting optimal portfolio. VaR estimates are at present the most favored tool to capture unacceptable risk, but advancement in financial econometrics has been offering advanced and robust tools to model risk. Knowing more about the downside risk is crucial to the advancement of risk management techniques and avoiding future financial crisis.