This study examines the volatility persistence and asymmetry with exogenous breaks in Nigerian stock market. The study utilizes daily closing quotations of stock prices from the Nigerian stock exchange for the period 3rd July, 1999 to 12th June, 2017. Standard symmetric GARCH (1,1), asymmetric...

In the class of univariate conditional volatility models, the three most popular are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the...

We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second...

Autoregressive models such as the heterogeneous autoregressive (HAR) model capture the linear footprint inherent in realized volatility. We cast the problem of estimating structural breaks in the autoregressive volatility dynamics as a model selection problem. Interestingly, we find the number...

This paper shows that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, (unconditional) combinations, and hybrid forecasts. Hence, it finds empirical evidence that both, combining...

This paper investigates the empirical properties of oil price and Stock market return volatilities using a range of univariate and multivariate GARCH models and monthly data from the U.S. The study relates the period August 1987 to October 2016, a total of 351 observations given. The aim of this...

This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coefficients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for...

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity....

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional...