Modeling energy price dynamics: GARCH versus stochastic volatility

Bayesian analysis of GARCH and stochastic volatility: modeling

This paper develops a Bayesian model comparison for two broad major classes of varying volatility model, GARCH and stochastic volatility (SV) models on financial time series. The leverage effect, jumps and heavy-tailed errors are incorporated into the two models. For estimation, the efficient Markov chain Monte Carlo methods are developed and the model comparisons are examined based on the marg...

Modeling Gold Volatility: Realized GARCH Approach

F orecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Real...

Efficient Semiparametric Garch Modeling of Financial Volatility

We consider a class of semiparametric GARCH models with additive autoregressive components linked together by a dynamic coefficient. We propose estimators for the additive components and the dynamic coefficient based on spline smoothing. The estimation procedure involves only a small number of least squares operations, thus it is computationally efficient. Under regularity conditions, the propo...

Modeling Volatility of Price of Some Selected Agricultural Products in Ethiopia: ARIMA-GARCH Applications

Volatility and Commodity Price Dynamics

Commodity prices tend to be volatile, and volatility itself varies over time. Changes in volatility can affect market variables by directly affecting the marginal value of storage, and by affecting a component of the total marginal cost of production: the opportunity cost of exercising the option to produce the commodity now rather than waiting for more price information. I examine the role of ...