This paper examines international equity market co-movements using time-varying copulae. We examine distributions from the class of Symmetric Generalized Hyperbolic (SGH) distributions for modelling univariate marginals of equity index returns. We show based on the goodness-of-fit testing that...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

This paper applies a non- and a semiparametric copula-based approach to analyze the first-order autocorrelation of returns in high frequency financial time series. Using the EUREX D3047 tick data from the German stock index, it can be shown that the temporal dependence structure of price...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

This study is designed to model and forecast Nigeria's stock market using the AllShare Index (ASI) as a proxy. By employing the Markov regime-switching autore-gressive (MS-AR) model with data from April 2005 to September 2019, the studyanalyzes the stock market volatility in three distinct...

We generalize the score test for time-varying copula parameters proposed by [Abegaz & Naik-Nimbalkar, 2008] to a setting where more than one-parametric copulas can be tested for time variation in at least one parameter. In a next step we model the daily log returns of the Commerzbank stock using...