C22 - Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion ProcessesReturn

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump-Diffusion (SVJD) models are being explained. Three versions of the SIR particle filter with adapted proposal distributions to the jump occurrences, jump sizes, and both are derived and their performance is compared in a simulation study to the un-adapted particle filter. The filter adapted to both the jump occurrences and jump sizes achieves the best performance, followed in their respective order by the filter adapted only to the jump occurrences and the filter adapted only to the jump sizes. All adapted particle filters outperformed the unadapted particle filter.

This study assesses the influence of error distributional assumption on appearance or disappearance of day-of-the-week effects in returns and volatility using the Nigerian stock exchange (NSE-30). The Gaussian, Student-t, and the Generalized error distribution were incorporated in the GARCH (2,1) and EGARCH (2,1) models. Result reveals that day-of-the-week effects are sensitive to error distribution. Our finding also shows that evidence of good or bad news in volatility does not only depend on the asymmetric model but also the choice of the error distribution. Thus, this study will provide adequate knowledge to policy makers, investors and researchers about day-of-the-week effect in stock markets.