This paper investigated exchange rate and stock price volatility connectedness spillover in Brazil, Russia, India, China, South Africa (BRICS) during pandemic-induced crises. We first extracted using the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. Then were by (Diebold Yilmaz, International Journal of Forecasting, 28(1), 57–66, 2012) method. find that return volati...

this paper employs a multivariate dynamic conditional correlation garch model, which is developed by engle (2001, 2002), to detect the timing and nature of changes in the comovement between iranian output and prices for the periods after iran–iraq war , known as imposed war . the results showed that there is a weak correlation between output and prices after imposed war and  varies periodically...

ARCH and GARCH models are widely used to model financial market volatilities in risk management applications. Considering a GARCH model with heavy-tailed innovations, we characterize the limiting distribution of an estimator of the conditional Value-at-Risk (VaR), which corresponds to the extremal quantile of the conditional distribution of the GARCH process. We propose two methods, the normal ...

We test the importance of multivariate information for modelling and forecasting inflation’s conditional mean and variance. In the literature, the existence of inflation’s conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of econ...

This paper stresses the importance of assessing the risk-return trade-off faced by environmental industries in financial markets. One of the most widely-used theoretical models in finance is the conditional CAPM, which describes the conditional risk-return tradeoff in financial markets, whereby both the conditional mean return and conditional beta risk are allowed to vary over time. This paper ...

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the dependent extremes may not necessarily be in the domain of attraction of the classical generalised ex...

This article discusses the application of Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) time series models for representing the dynamics of traffic flow volatility. The methods encountered in the literature so far, focus on the levels of traffic flows while regarding variance constant through time. The approach adopted in this paper concentrates mostly on the autoregressive...

The univariate Generalised Autoregressive Conditional Heterscedasticity (GARCH) model has successfully captured the symmetric conditional volatility in a wide range of time series financial returns. Although multivariate effects across assets can be captured through modelling the conditional correlations, the univariate GARCH model has two important restrictions in that it: (1) does not accommo...