This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional...

This study employs the Structural Break Threshold Vector Autoregression (SBTVAR) models to examine the asymmetric effects of policy shocks on the price volatility of various asset classes. The results indicate that pandemic-induced economic policy uncertainty and national lockdowns triggered a...

The Dynamic Conditional Correlation GARCH (DCC-GARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, time-dependence variation is visually demonstrated. Fifteen indices were analyzed from the main financial markets of developed and...

Modelling the volatility (or kurtosis) of the implied volatility is an important aspect of financial markets when analysing market consensus and risk strategies. The purpose of this study is to evaluate the ability of symmetric and asymmetric GARCH systems to model the volatility of the FTSE 100...

We implement multivariate, self-exciting Peaks-over-Threshold (POT) methods to measure extremal losses in high-frequency return series of cryptocurrencies. For that purpose, we implement trivariate Hawkes-POT and the autoregressive conditional intensity ACI-POT models for Bitcoin, Ethereum, and...

We apply the statistical sparse jump model, a recently developed, interpretable and robust regime switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state...