Stochastic volatility models with endogenous breaks in volatility forecasting

The need for research on modelling and forecasting financial volatility has increased noticeably due to its essential role in portfolio and risk management, option pricing, and dynamic hedging. This paper contributes to the ongoing discussion of how researchers use regime shifts or structural breaks information to improve forecast accuracy. To accomplish this, we use the data on renewable energy markets. Thus, this study examines several models that accommodate regime shifts and investigates their forecasting performance. First, a subset of competing models (GARCH-class and stochastic volatility) employ the modified iterative cumulative sum of squares method to determine the estimation windows. This paper's novel aspect is that it studies the forecasting performance of various specifications of stochastic volatility models under this procedure. Second, we employ Markov switching GARCH models under alternative distribution assumptions. The rolling window-based forecast analysis reveals that Markov switching models offer more accurate volatility forecast results for most cases. Regarding distribution functions’ relevance, the normal distribution followed by Students t, skew Student t, and generalized hyperbolic distribution commonly dominates the series under investigation in the superior sets under all considered loss metrics.