GARCH v. Stochastic Volatility Model

[Sapphire Capital]

A Simple Test for GARCH Against a Stochastic Volatility Model

Philip Hans Franses

Marco Van Der Leij
Universidad de Alicante - Department of Economic Analysis

Richard Paap



Journal of Financial Econometrics, Vol. 6, Issue 3, pp. 291-306, 2008


Abstract:
GARCH models and Stochastic Volatility (SV) models can both be used to describe unobserved volatility in asset returns. We consider the issue of testing a GARCH model against an SV model. For that purpose, we propose a new and parsimonious GARCH-t model with an additional restricted moving average term, which can capture SV model properties. We discuss model representation, parameter estimation, and our simple test for model selection. Furthermore, we derive the theoretical moments and the autocorrelation function of our new model. We illustrate our model and test for nine daily stock-return series.

papers.ssrn.com/sol3/papers.cfm?abstract_id=1146713

[Sapphire Capital]

The Spline-Garch for Low-Frequency Volatility and its Global Macroeconomic Causes

Robert F. Engle
Leonard N. Stern School of Business - Department of Economics; National Bureau of Economic Research (NBER)

Jose Gonzalo Rangel

The Review of Financial Studies, Vol. 21, Issue 3, pp. 1187-1222, 2008


Abstract:
Twenty-five years of volatility research has left the macroeconomic environment playing a minor role. This paper proposes ing equity volatilities as a combination of macro- economic effects and time series dynamics. High-frequency return volatility is specified to be the product of a slow-moving component, represented by an exponential spline, and a unit GARCH. This slow-moving component is the low-frequency volatility, which in this coincides with the unconditional volatility. This component is estimated for nearly 50 countries over various sample periods of daily data. Low-frequency volatility is then ed as a function of macroeconomic and financial variables in an unbalanced panel with a variety of dependence structures. It is found to vary over time and across countries. The low-frequency component of volatility is greater when the macroeconomic factors of GDP, inflation, and short-term interest rates are more volatile or when inflation is high and output growth is low. Volatility is higher not only for emerging markets and markets with small numbers of listed companies and market capitalization relative to GDP, but also for large economies. The allows long horizon forecasts of volatility to depend on macroeconomic developments, and delivers estimates of the volatility to be anticipated in a newly opened market.
papers.ssrn.com/sol3/papers.cfm?abstract_id=1154428