This paper constructs a class of multivariate Gaussian marked Poisson processes to model asset returns. The model proposed accommodates the cross section properties of trades, allows for returns to be correlated conditional on trading activity, and preserves the economic intuition of normality of returns conditional on trading activity. We prove that the new class of processes are in law subord...

Empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. That is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of VaR. Copula theo...

This paper provides empirical evidence to compare the abilities of the forecasting correlations among market returns. The conditional models, which are based on the estimation of the time-varying correlation equations, provide strong forecast accuracy. The unconditional measures of correlation, however, perform poorly in all equity markets. The popular Pearson’s correlation coefficient is infer...

The uncertainty originated by the COVID-19 pandemic and unpredictability of both real financial market indicators have increased volatility global markets. As a result globalization, determination risk information transfer between markets has gained importance during process. In this context, spread cryptocurrency stock was analyzed considering Bitcoin, which represents 42% total cap, used to r...

This paper introduces a new multivariate conditional volatility model for returns that utilizes realized covariance matrices. The model decomposes the conditional and realized covariance matrices into standard deviations and correlations matrices. On a first level, the univariate variances are estimated by a modified Generalized Autoregressive Conditional Heteroskedasticity (GARCH) that exploit...