Researcher ORCID Identifier
Date of Submission
Campus Only Senior Thesis
Bachelor of Arts
The predictability of asset returns has been examined by many studies that use explanatory variables that range from bonds to inflation rates. Volatility and the predictability of asset prices can be quantified by the Variance Risk Premium (VRP), which is the premium paid by investors to hedge against future volatility fluctuations. Historically, the VRP has performed well in 1 to 6-month intervals, in times of economic stability. However, the VRP lacks stress testing in high volatility environments. As such, I examine VRP’s delicacy in monthly periods both pre- and post-COVID through the US, emerging markets ETFs, as well as volatility indices in India, China, and Brazil. By regressing VRP on monthly forward returns, I find that VRP is not significant in predicting asset returns in high volatility environments, and also loses statistical significance in the US, and emerging markets, China, and India post-COVID. During COVID, the VRP experiences its most extreme high and low values compared to the period of 2010-2019 as a result of high uncertainty. In order to create a more dynamic model, I introduce a decay rate to the historical volatilities in an Exponentially Weighted Moving Average model, varying from 1% to 5% to 10%. This decay rate causes further reductions in the predictability of the VRP model in the US, India, and China during COVID; on the contrary, it improves the statistical significance of the emerging markets, largely due to a more reactive measure of volatility. Ultimately, the novel high volatility environment of COVID negatively affects the VRP in terms of predictability of asset returns.
Ying, David, "The Predictive Power of the Variance Risk Premium Model on Equity Returns in Emerging Markets During the COVID-19 Global Pandemic" (2021). CMC Senior Theses. 2578.
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.