Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the...

Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which result from the assumption of conditionally...

Conditional returns distributions generated by a GARCH process, which are important for many problems in market risk assessment and portfolio optimization, are typically generated via simulation. This paper extends previous research on analytic moments of GARCH returns distributions in several...

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the...

This paper investigates sensitivity of U.S. natural gas price to crude oil price changes, using time-varying coefficient models. Identification of the range of variation of the sensitivity of natural gas price to oil price change allows more accurate assessment of upper and minimum risk levels...

Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which result from the assumption of conditionally...

We revisit the specification of GARCH processes with Johnson Su innovations examined in Choi and Nam [2008. Journal of Empirical Finance 15, 41–63]. This model, allowing for skewed and leptokurtic innovations, has many advantages over well known alternatives. We examine a simpler version of...