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 ...

In this paper, we analyze and predict the number of daily confirmed cases coronavirus (COVID-19) based on two statistical models a deep learning (DL) model; autoregressive integrated moving average (ARIMA), generalized conditional heteroscedasticity (GARCH), stacked long short-term memory neural network (LSTM DNN). We find orders by autocorrelation function partial function, hyperparameters DL ...

This paper examines the spillover effect from Chinese stock market to select emerging economies check diversification opportunities. The study analysed data in three different periods including full period January 3, 2000 February 7, 2020; first sub October 18, 2009 and second 19 2020. We applied Granger Causality Dynamic Conditional Correlation Generalized Autoregressive Heteroscedasticity (DC...

In this article, a Multivariate Threshold Generalized Autoregressive Conditional Heteroscedasticity model with time-varying correlation (VC-MTGARCH) is proposed. The model extends the idea of Engle (2002) and Tse & Tsui (2002) in a threshold framework. This model retains the interpretation of the univariate threshold GARCH model and allows for dynamic conditional correlations. Extension of Boll...

In this paper usefulness of quasi-Newton iteration procedure in parameters estimation of the conditional variance equation within BHHH algorithm is presented. Analytical solution of maximization of the likelihood function using first and second derivatives is too complex when the variance is time-varying. The advantage of BHHH algorithm in comparison to the other optimization algorithms is that...

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 ...

The class of Multivariate BiLinear GARCH (MBL-GARCH) models is proposed and its statistical properties are investigated. The model can be regarded as a generalization to a multivariate setting of the univariate BLGARCH model proposed by Storti and Vitale (2003a; 2003b). It is shown how MBL-GARCH models allow to account for asymmetric effects in both conditional variances and correlations. An EM...

The current paper proposes a conditional volatility model with time varying coefficients based on a multinomial switching mechanism. By giving more weight to either the persistence or shock term in a GARCH model, conditional on their relative ability to forecast a benchmark volatility measure, the switching reinforces the persistent nature of the GARCH model. Estimation of this volatility targe...

This study examines how crude oil price volatility affected the stock returns of major global and gas corporations during three oil-price wars that took place between October 1991 June 2020. Episodes considered include 1998 Saudi Arabia – Venezuela war, 2014–2016 conflict 2020 Russia war in a time unprecedented crisis caused by COVID-19 pandemic. The persistence prices times specific is capture...