The use of GARCH models with stable Paretian innovations in financial modeling has been recently suggested in the literature. This class of processes is attractive because it allows for conditional skewness and leptokurtosis of financial returns without ruling out normality. This contribution illustrates their usefulness in predicting the downside risk of financial assets in the context of mode...

We develop a multivariate generalization of the Markov–switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth– moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a statistical and economic perspective, including out–of–sample portfolio selection and computation of Value– ...

Distributional theory for Quasi-Maximum Likelihood estimators in long memory conditional heteroskedastic models is not formally defined, even asympotically. Because of that, this paper analyses the performance of the Likelihood Ratio and the Lagrange Multiplier misspecification tests for Periodic Long Memory GARCH models. The real size and power of these tests are studied by means of Monte Carl...

the research problem investigated in this paper is modeling volatility and analyzing risk and return’s relationship in tehran stock exchange using garch-family models including garch(1,1), garch(2,2), egarch(1,1), pgarch(1,1), tgarch(1,1), garch(1,1)-m and cgarch(1,1). using the daily returns of tehran stock exchange companies, we focused on two portfolios of all the companies during a 10-year-...

Purpose This study aims to analyse whether investment in green and sustainable stocks provide some cushion during current precarious time. To compare the impact of COVID-19 on volatility market-capitalisation-based stocks, daily returns from Greenex, Carbonex, Large-Cap, Mid-Cap Small-Cap index have been analysed over a period six years 2015 2021. Design/methodology/approach At outset, logarith...

Meteorological variables are not constant but shows some regular variations. This is also true for temperature during day and night time. Asymmetric variations also observed due to seasonality or other causes. In particular, for a long term observed temperature data, the correlation between conditional volatility and unexpected temperature behaviour is negative in winter and positive in summer....

This paper investigates the hedging effectiveness of time-varying hedge ratios in the agricultural commodities futures markets based on four different versions of the GARCH models. The GARCH models applied are the standard bivariate GARCH, the bivariate BEKK GARCH, the bivariate GARCH-X and the bivariate BEKK GARCH-X. The GARCH-X and the BEKK GARCH-X models are uniquely different from the other...

The generalized autoregressive conditional heteroscedasticity (GARCH) approach is one of the common and simpler ways to use historical data to produce estimates of current and future levels of volatilities. This model recognizes that volatilities are not constant, for instance, a particular volatility may be high or low depending on the period of time. One of goals of a GARCH model is to track ...

The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the generalized autoregressive conditional heteroscedasticity (GARCH) model. The HMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it updates all parameters at once. We demonstrate that how the HMC reproduces the GARCH parameters correctly. The algorithm is rather general and it can be applied to other ...

We investigate the conditional interest rate risk premium in Treasury bill futures returns. A one-factor model predicts that the premium depends on the conditional variance. An Intertemporal CAPM based two-factor model predicts that it also depends on conditional covariance with the equity premium. Univariate and bivariate Integrated GARCH-in-Mean models suggest that the premium relates positiv...