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This article was downloaded by: [University of North Texas]On: 26 November 2014, At: 17:25Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Applied Financial EconomicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/rafe20

Forecasting accuracy of stochastic volatility, GARCHand EWMA models under different volatility scenariosJie Ding a & Nigel Meade aa Imperial College London, Tanaka Business School, South Kensington , London SW7 2AZ, UKPublished online: 17 May 2010.

To cite this article: Jie Ding & Nigel Meade (2010) Forecasting accuracy of stochastic volatility, GARCH and EWMA modelsunder different volatility scenarios, Applied Financial Economics, 20:10, 771-783, DOI: 10.1080/09603101003636188

To link to this article: http://dx.doi.org/10.1080/09603101003636188

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Applied Financial Economics, 2010, 20, 771783

Forecasting accuracy of stochastic

volatility, GARCH and EWMA

models under different volatility

scenarios

Jie Ding and Nigel Meade*

Imperial College London, Tanaka Business School, South Kensington,

London SW7 2AZ, UK

The forecasting of the volatility of asset returns is a prerequisite for many

risk management tasks in finance. The objective here is to identify the

volatility scenarios that favour either Generalized Autoregressive

Conditional Heteroscedasticity (GARCH) or Stochastic Volatility (SV)

models. Scenarios are defined by the persistence of volatility (its robustness

to shocks) and the volatility of volatility. A simulation experiment

generates return series using both volatility models for a range of volatility

scenarios representative of that observed in real assets. Forecasts are

generated from SV, GARCH and Exponentially Weighted Moving

Average (EWMA) volatility models. SV model forecasts are only

noticeably more accurate than GARCH in scenarios with very high

volatility of volatility and a stochastic volatility generating process. For

scenarios with medium volatility of volatility, there is little penalty for

using EWMA regardless of the volatility generating process. A set of

return time series selected from FX rates, equity indices, equities and

commodities is used to validate the simulation-based results. Broadly

speaking, the real series come from the medium volatility of volatility

scenarios where EWMA forecasts are reliably accurate. The robust

structure of EWMA appears to contribute to its greater forecasting

accuracy than more flexible GARCH model.

I. Introduction

The forecasting of the volatility of asset returns is

required for many risk management tasks in finance.

The use of Value-at-Risk (VaR) is ubiquitous in

financial institutions and is increasingly widespreadas a risk management tool in corporate institutions.

Future volatility is the crucial input to VaR calcula-

tions; an overestimate of volatility leads to an

opportunity loss due to capital being tied up unne-

cessarily and an underestimate of volatility leads to

risks being under protected. Risk control strategies,

such as delta hedging, are reliant on the estimates offuture volatility for the creation of riskless portfoliosof options and the underlying asset. Mean-variance

portfolio selection relies on the estimates of futurevolatility. Poon and Granger (2003) review the

extensive literature on forecasting the volatility offinancial markets.

Our contribution to this literature is to explorethe comparative effectiveness of the main two

volatility modelling methodologies, Generalized

*Corresponding author. E-mail: n.meade@imperial.ac.uk

Applied Financial Economics ISSN 09603107 print/ISSN 14664305 online 2010 Taylor & Francis 771http://www.informaworld.com

DOI: 10.1080/09603101003636188

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Autoregressive Conditional Heteroscedasticity(GARCH) and Stochastic Volatility (SV) in thecontext of possible volatility scenarios. We use twodimensions, the persistence of volatility (its robust-ness to shocks) and the volatility of volatility, todefine the space of volatility scenarios. We investigateif there are regions where either modelling approachis dominant using the accuracy of volatility forecastsas our measure of dominance. The investigation is intwo parts. First, a simulation experiment is per-formed where data from known volatility models aregenerated to cover the space of volatility scenariosand the accuracy of forecasts prepared from thecompeting models is compared. Second, data fromdifferent financial markets are located within thespace of volatility scenarios and a similar comparisonof forecasting accuracy is carried out.

In addition to forecasts using SV and GARCHmodels, we compute forecasts using ExponentiallyWeighted Moving Average (EWMA) volatility, thespecial case of the GARCH model favoured byRiskMetrics. For the simulated data, we contrast theaccuracy of volatility estimates in-sample and out-of-sample using a range of error measures. Weconclude from this experiment that, for most scenariosand regardless of the data generating process, usinga GARCH volatility forecast involves little penaltycompared to using a SV forecast. SV model forecastsare only noticeably more accurate than GARCH inscenarios with very high volatility of volatility and astochastic volatility generating process. For scenarioswith medium volatility of volatility, there is littlepenalty for using EWMA regardless of the volatilitygenerating process.

In order to validate these simulation-based results,a set of return time series selected from a ForeignExchange (FX) rates, equity indices, equities andcommodities is used. The real series are related to thepersistence and volatility of volatility scenarios andout-of-sample forecasts are generated from the threemethods discussed. We find that, broadly speaking,the real assets come from the medium volatility ofvolatility scenarios where EWMA forecasts are reli-ably accurate.

This article is structured as follows. In Section II,we discuss volatility models and we describe thesimulation experiment in Section III. The validationof our findings using real data is given in Section IVand we give our conclusions in Section V.

II. Volatility Models

A stylized fact of time series of returns on financialassets is the clustering behaviour of volatility.

Two modelling approaches have been used to capture

this behaviour. The GARCH model represents con-

ditional variance as a function of lagged squared

residuals and lagged conditional variance. The sto-

chastic variance model (as implied by its name)

assumes that the variance follows a stochastic pro-

cess. Both approaches will be described below. Note

that we will focus on the basic formulation of each

model, we wish to facilitate comparisons between the

modelling approaches rather than be distracted by the

differences of models within each approach. Our

notation is rt, which is the log-return in period t, this the estimated volatility in period t h, given dataup to period t.

Generalised autoregressive conditionalheteroscedasticity models

The GARCH model was proposed by Bollerslev

(1986), generalizing Engles (1982) Autoregressive

Conditional Heteroscedasticity (ARCH) model. For

surveys of the extensive literature on these models, see

Bollerslev et al. (1992, 1994), and Li et al. (2002). The

GARCH( p, q) model is defined as follows:

rt "t 1

where "t ztt and E(zt) 0 and V(zt) 1. Thevariance, 2t , obeys this process

2t !Pqi1

i"2ti

Ppj1

j2tj 2

such that !4 0, i 0, j 0,P

iP

j 5 1.Although there is no consensus about the ideal

GARCH specification (see, e.g. Brailsford and Faff,

1996), there is little doub

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