Prediction of risk: Decoding the Serial Dependence of Stock Return Volatility with Copula

Abstract

In this article, a new approach based on the copula theory is employed in the analysis and forecasting of hospitality and tourism-related stock return volatility (HTSRV). The application of copula-based models for univariate time series is state-of the-art methodologies with new perspectives for economic analysis in tourism and hospitality. This flexible method provides numerous functions for serial dependence specification of volatility series. Eight hospitality–tourism stocks are analyzed for their serial dependence structures to provide insight for forecasting stock return volatility. While the forecasting performance between our chosen copulas and benchmark models is inconclusive, the empirical results show that copulas well specify both linear and nonlinear serial dependence structures, which lead to forecasting results as good as or even better than those of the benchmark models. This property allows us to use copulas for HTSRV forecasting without the concern of model misspecification.