It is well known in the literature that the joint parameter estimation of the Smooth Autoregressive – Generalized Autoregressive Conditional Heteroskedasticity (STAR-GARCH) models poses many numerical challenges with unknown causes. This paper aims to uncover the root of the numerical difficulties in obtaining stable parameter estimates for a class of three-regime STAR-GARCH models using Quasi-...

Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS). Following popular copula–GARCH approach modeling dependent MTS data, a framework based on GMMN–GARCH is presented. First, ARMA–GARCH utilized to capture serial within each univariate marginal series. Second, if number large, principal component...

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

After the so-called Asia crisis in the summer of 1997 the nancial markets were shaken by increased volatility transmission around the world. Therefore, in this paper we will analyse the daily exchange rates in New York, Germany, and Japan for the period of 2 years (June 21, 1996 to June 22, 1998). We estimate a VAR-GARCH in mean model and estimate the multivariate volatility e ects between the ...

1. Methods and application Several studies in empirical finance literature have highlighted the importance of allowing for skewness, tail-fatness, non normality of returns for asset allocation and pricing models. Moreover, the dependence between returns, that can impact portfolio decisions, often exhibits nonlinear structures and asymmetric extremal behavior that the usual correlation coefficie...

This paper proposes a methodology for building Multivariate Time-Varying STCC–GARCH models. The novel contributions in this area are the specification tests related to correlation component, extension of general model allow additional regimes, and detailed exposition systematic, improved modelling cycle required such nonlinear There is an R-package that includes steps cycle. Simulations demonst...

We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework we allow for different distributions of the historical and the pricing return dynamics enhancing the model flexibility to fit market option prices. An extensive empirical analysis based on S&P 500 index options shows that our model outperforms other competing...

In order to capture the dependency among exchange rates we construct semiparametric multivariate copula models with ARMA-GARCH margins. As multivariate copula models we utilize pair-copula constructions (PCC) such as regular and canonical vines. As building blocks of the PCC’s we use bivariate t-copulas for different tail dependence between pairs of exchange rates. Alternatively we also conside...

We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics enhancing the model flexibility to fit market option prices. An extensive empirical analysis based on S&P 500 index options shows that our model outperforms other competing GARCH ...

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowin...