Volatility Modeling with a GeneralizedtDistribution

Volatility Modeling with a Generalized t-distribution

Beta-t-EGARCH models in which the dynamics of the logarithm of scale are driven by the conditional score are known to exhibit attractive theoretical properties for the t-distribution and general error distribution (GED). The generalized-t includes both as special cases. We derive the information matrix for the generalized-t and show that, when parameterized with the inverse of the tail index, i...

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...

Volatility modeling

• So far we have maintained the assumption of iid data in the estimation of distributions and risk measures. However, this assumption does not hold for financial data, which are known to be intertemporally dependent, especially in the volatility. Estimation of risk measures under the assumption of iid can be viewed as unconditional estimates. In contrast, we can model the intertemporal dependen...

Modeling Volatility Spillovers in Iran Capital Market

This paper investigates the conditional correlations and volatility spillovers between the dollar exchange rate return, gold coin return and crude oil return to stock index return. Monthly returns in the 144 observations (2005 - 2017) are analyzed by constant conditional correlation, dynamic conditional correlation, VARMA-GARCH and VARMA-AGARCH models. So this paper presents interdependences in...

A Brief Introduction to Stochastic Volatility Modeling