Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts

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    Forecasting exchange rate volatility: GARCHmodels versus implied volatility forecasts

    Keith Pilbeam & Kjell Noralf Langeland

    # Springer-Verlag Berlin Heidelberg 2014

    Abstract This study investigates whether different specifications of univariate GARCHmodels can usefully forecast volatility in the foreign exchange market. The studycompares in-sample forecasts from symmetric and asymmetric GARCH models withthe implied volatility derived from currency options for four dollar parities. The data setcovers the period 2002 to 2012. We divide the data into two periods one for the period2002 to 2007which is characterised by low volatility and the other for the period 2008 to2012 characterised by high volatility. The results of this paper reveal that the impliedvolatility forecasts significantly outperform the three GARCH models in both low andhigh volatility periods. The results strongly suggest that the foreign exchange marketefficiently prices in future volatility.

    Keywords Exchange Rate . Volatility modelling

    JEL classification E44 . G12

    1 Introduction

    The foreign exchange market is by far the largest and most liquid financial market inthe world. As reported by the Bank for International Settlement in April 2013 theaverage daily turnover was $5.0 trillion. The foreign exchange market is made upprimarily of three inter-related parts; spot transactions, forward transactions and deriv-ative contracts. As with other financial markets currency markets can be volatile andexhibit periods of volatility clustering as traders react to new information.

    Improving the forecasting of volatility in the foreign exchange market is important tomultinational firms, financial institutions and traders wishing to hedge currency risks see forexample, Broll and Hansen-Averlant (2010). Volatility is usually defined as the standarddeviation or variance of the returns of an asset during a given time period. Traders offoreign currency options attempt to make profits by buying options if they expect volatility

    Int Econ Econ PolicyDOI 10.1007/s10368-014-0289-4

    K. Pilbeam (*) : K. N. LangelandCity University London, Northampton Square, London EC1V 0HB, UKe-mail:

  • to rise above that implied in currency option premiums and writing options if they expectvolatility to be lower than that currently implied by option premiums.

    This paper examines the efficiency of the foreign exchange market in pricing optionvolatility by comparing the forecasts given the implied volatility from currency optionprices with volatility forecasts from three different univariate GARCH models. If theforeign exchange market is efficient, then the implied volatility forecasts should outper-form the GARCH forecasts. In addition, as Engle and Patton (2001) argue, the wholepoint of GARCH forecasting models is that they should help in forecasting futurevolatility and as such seeing whether they can beat implied volatility forecasts is aninteresting topic in itself. Our period of study which covers the period 200212 isparticularly interesting period, since it also incorporates the period of the financial crisiswhich also resulted in a noticeable increase in turbulence in the foreign exchangemarket.

    The paper is organized as follows section 2 gives a review on the three univariateGARCH models we use for our empirical forecasting exercise. Section 3 gives a moredetailed introduction to the models used and the estimation of volatility. Section 4 looksat the features of the data set and its properties. In section 5 we present the results of thestudy and section 6 concludes.

    2 Review of the use of GARCH models

    In the last decade, forecasting exchange rate volatility has been a very popular topic ineconomic and finance journals sees for example Busch et al. (2012). Using different timeperiods, data frequency and the exchange rate pairs research has used a wide range ofvolatility models. Conditional variance models, such as ARCH and GARCH are the mostoften used to forecast volatility. In this study, we use both symmetric and asymmetricGARCHmodels.1 The symmetric model we use is the GARCH (1,1) of Bollerslev (1986)and Taylor (1986) this model is far more widely used than ARCH due to the fact that it ismore parsimonious and avoids over fitting 2 and is consequently less likely to breach thenon-negativity constraint. We also look at two asymmetric models, the EGARCH ofNelson (1991) and GJR-GARCH of Glosten, Jagannathan, and Runkle (1993). TheEGARCH model has two key advantages over the GARCH (1,1). Firstly, the modelmeasures the log returns, and therefore even if the parameters are negative, the conditionalvariance will be positive. Secondly, because themodel allows for asymmetries can capturethe so called leverage effect .3 The second asymmetric model we use is the GJR-GARCHmodel of Glosten et al. (1993). The GJR-GARCH is an extension of GARCH with anadditional term added to capture possible asymmetries .4 We compare the forecasts ofthese models to the implied volatility series provided on Bloomberg.

    1 A symmetric model means that when a shock occurs, we will have a symmetric response of volatility to bothpositive and negative shocks. Asymmetric models on the other hand, allow for an asymmetric response withempirical results show that negative shocks will lead to higher volatility than a positive shock.2 Over fitting happens when the statistical model describes a random error or noise instead of the underlyingrelationship, causing biasedness in parameter estimates.3 The leverage effect is typically interpreted as a negative correlation between lagged negative returns andvolatility.4 As with the EGARCH the GJR-GARCH model captures the leverage effect but the way that it acts is not thesame as for the EGARCH, The GJR-GARCH does not measure log returns, so in this model we still need toimpose non-negative constraints.

    K. Pilbeam, K.N. Langeland

  • Bollerslev (1986) showed that the GARCH model outperformed the ARCHmodel. However, Baillie and Bollerslev (1991) used the GARCH model toexamine patterns of volatility in the US forex market and results weregenerally poor. In the two decades after the arrival of ARCH and GARCH,several approaches building on GARCH have been created. EGARCH wasintroduced by Nelson (1991), NGARCH by Higgins and Bera (1992), GJR-GARCH by Glosten, Jagannathan and Runkle (1993), TGARCH by Zakoan(1994), QGARCH by Sentana (1995), and many more are available see, forexample, Bollerslev (2008). In an interesting study, Hansen and Lunde (2005)find that none of the models in the GARCH family outperforms the simpleGARCH (1,1) which is somewhat surprising since the GARCH (1,1) does notrely upon a leverage effect. While Nelsons EGARCH model has severaladvantages over the linear GARCH model, authors such as Brownlees andGallo (2010) find that while at some horizons EGARCH produces the mostaccurate forecast, but at other horizons EGARCH is outperformed by the linearGARCH model. Donaldson and Kamstra (2005) used GJR-GARCH (1,1) toforecast international stock return volatility, and found that this model yieldedbetter forecasts than the GARCH(1,1) and EGARCH(1,1). However usingARCH, GARCH, GJR-GARCH and EGARCH, Balaban (2004) find that thestandard GARCH model was overall the most accurate forecast for monthlyU.S. dollar-Deutschemark exchange rate volatility.

    Dunis et al. (2003) examine the medium-term forecasting ability of several alterna-tive models of currency volatility with respect to 8 currency pairs and find that noparticular volatility model outperforms in forecasting volatility for the period 199199.Andersen and Bollerslev (1998) show how volatility at even very short term horizonsas low as 5 min can have an information content in explaining intra-day and even dailyvolatility. In a similar vein, Ghysels et al. (2005) suggest that mixing data at differenttime horizons can have useful information content in forecasting future volatility. In arecent study, Ranaldo (2008) shows that there are intra-day patterns in exchange ratevolatility depending upon the official opening and closing times of the domestic andforeign currency hours of business, with the domestic currency tending to weakenduring the opening hour as domestic residents sell the domestic currency to obtain theforeign currency.

    Regardless of the widespread literature on volatility model evaluation, we arenowhere close to finding the optimal model for providing the most favourable perfor-mance in forecasting volatility. In this study we concern ourselves with the efficiencywith which the foreign exchangemarket prices in future volatility when pricing currencyoptions. If the foreign exchange market prices in volatility efficiently, then one wouldexpect that implied volatility obtained from actual call and put premiums on variouscurrencies will outperform econometric models such as provided by the GARCHmodels.

    3 Alternative GARCH specifications

    In this section, we will look at three GARCH models that we use in this study; namelythe GARCH(1,1), EGARCH(1,1) and GJR-GARCH(1,1).

    Forecasting exchange rate volatility

  • The full GARCH (p,q) model is given by:

    yt 1 2x2t 3x3t 4x4t ut; 1

    h2t 0 Xi1





    j2t j 2

    In the GARCH model the conditional variance depends upon the q lags of thesquared error and the p lags of the conditional variance. From the equation (2) we seethat the fitted variance ht (t) is a weighted function the information about the volatilityfrom the previous periods, the fitted variance from the model during the previousperiod and the long-run variance (0) .

    5 It should be noted that the GARCH model issymmetric because of the sign of the disturbance being ignored. Since we are using theGARCH(1,1) the conditional variance of the model is:

    2t 0 1u2t1 12t1 3Where t

    2 is the conditional variance because it is a one period ahead estimate for thevariance calculated on any past information thought to be relevant. While the condi-tional variance depends on past observations the unconditional variance of GARCHmodel is constant and more concerned with the long-term behaviour of the time series.The unconditional variance is given by:

    Var ut 01 1 4

    The coefficient measures the extent to which a volatility shock today feeds throughinto next periods volatility, in other words it corresponds to the long term volatility. Aslong as 1+

  • The GJR-GARCH variant also includes a leverage term to model asymmetricvolatility. In the GJR-GARCH model, large negative changes are more likely to befollowed by large negative changes than positive changes. The GJR model is only asimple extension of the GARCH model, with an additional term added to capturepossible asymmetries. The GJR-GARCH specification is given by:

    2t 0 1u2t1 2t1 u2t1I t1 7Where It1=1 if ut10. it can also be observed that the

    non-negativity constraint that has to be imposed requires that 0>0,1>0, 0,and 1+0 and explains why this model is less likely to breach the non-negativityconstraint than the standard GARCH model. The model is still tolerable if

  • ~N(0,t2) so that the variance varies with time. In the log likelihood function for the

    GARCH model we substitute the second term, 12 log 2 with 12



    log 2t

    . In

    addition, we replace 2 in equation (11) with t2. When there is heteroscedasticity in

    the error terms, the calculation of LLF are more complicated and we used MATLAB todo the calculations. In line with many earlier studies, we ended up with constant meanGARCH(1,1) model, hence the conditional variance is dependent upon one movingaverage lag and one autoregressive lag. We performed a Ljung-Box-Pierce Q-test inorder to verify that there was no correlation in the raw returns up to 20 lags.

    Following Andersen et al. (2001 and 2003) we used the following calculation tocalculate realized volatility 8:

    Realized Volatility ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi252



    !vuut 12

    Ri ln PtPt1

    ;Daily return on exchange rates from Pt to Pt1

    N Number of trading days in the periodPt Underlying reference price at time tPt1 The underlying reference price the the time period preceding time t

    We use the Root Mean Square Deviation (RMSE) to measure the accuracy of theforecasts as given by equation (13):

    RMSE e ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiMSE e r ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1



    N eivuut i2 13

    Where: e = the predicted value of the data and is the actual value of the data andN the number of observations.

    The RMSE has the advantage of being measured in the same unit as the forecastedvariable.

    In this study we generate forecasts using the in-sample forecasting, all observationswithin the period are be used to estimate the models, and the results are compared to theactual value (realized volatility). Using in sample forecasts maximises the chance thatthe GARCH models will beat the implied volatility forecast. In the next section we willlook at the data sampled for this study.

    8 Bollerslev et al. (2001) argue that this type of volatility is an unbiased and very efficient estimator of returnvolatility.

    K. Pilbeam, K.N. Langeland

  • 4 Data

    We used daily closing prices for four currency pairs the euro, pound, swiss franc andyen against the dollar. The data covers the period from 1/1-2002 to 30/12-2011. Eachcurrency pair had 2609 observations; Fig. 1 shows the empirical distribution of returns,w use a histogram to illustrate the density of returns and a curve from normaldistribution is overlaid.

    From Fig. 1 we see that the returns approximate to a normal distribution. Figure 2shows that daily log of returns during the time period under study and there are clearperiods of volatility clustering.

    In Fig. 2 we can see that that the series are stationary with most of the returns beinglocated around zero. However these show spikes in the first order difference in periodswith high volatility. To compare the proposed models we will use the realised volatilitywhich is shown in Fig. 3:

    The credit crunch which started in August 2007 caused a spike in the volatility in allof the exchange rate pairs starting in 2008, the properties of the realised volatility areoutlined in Table 1

    From Table 1 we see that the swiss francdollar parity has both the highest mean ofvolatility and highest standard deviation, this currency pair also has by far the largestspread in volatility, much caused by the two spikes in volatility.

    Figures 3 and 4 plots the data on implied volatility. We can see the similaritybetween the realized and implied volatility. However, we see that in case of the swissfrancdollar parity, the estimated peaks look different for the realized and impliedvolatility. The properties of the data are summarized in Table 2.




    -.04 -.02 0 .02 .04




    -.05 0 .05 .1




    -.06 -.04 -.02 0 .02 .04




    -.04 -.02 0 .02 .04



    Fig. 1 The distribution of...


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