Many static and dynamic models exist to forecast Value-at-Risk other quantile-related metrics used in financial risk management. Industry practice favours simpler, such as historical simulation or its variants. Most academic research focuses on the GARCH family. While numerous studies examine accuracy of multivariate for forecasting metrics, there is little accurately predicting entire distribu...

a r t i c l e i n f o JEL classification: C32 C51 L94 Q40 Keywords: Wholesale spot electricity price markets Constant and dynamic conditional correlation Multivariate GARCH This paper examines the interrelationships of wholesale spot electricity prices among the four regional A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Dynamic c...

Exponential models of Autoregressive Conditional Heteroscedasticity (ARCH) are of special interest, since they enable richer dynamics (e.g. contrarian or cyclical), provide greater robustness to jumps and outliers, and guarantee the positivity of volatility. The latter is not guaranteed in ordinary ARCH models, in particular when additional exogenous and/or predetermined variables (“X”) are inc...

We develop Bayesian inference of a multivariate GARCH model where the dependence is introduced by a D-vine copula on the innovations. A D-vine copula is a special case of vine copulas which are a relatively new and very flexible concept to construct multivariate copulas. In particular it allows to model dependency between pairs of margins individually. In a simulation study and three real data ...

ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate andmultivariate ARCH/GARCHmodels. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatil...

This paper addresses several questions surrounding volatility forecasting and its use in the estimation of optimal hedging ratios. Specifically: Are there economic gains by nesting time-series econometric models (GARCH) and dynamic programming models (therefore forecasting volatility several periods out) in the estimation of hedging ratios whilst accounting for volatility in the futures bid–ask...

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...