Abstract
Strong volatility forecasts in making financial decisions play an important role for financial institutions and other users. In practice, volatility models like the MA, EWMA and GARCH are used as volatility outlook. Particularly GARCH and EWMA type volatility forecasting models perform better in capturing fluctuations and developments in financial markets than the models that assume the variance is constant. However, results of these models turn out to be close to each other and comparing the results of calculations performed on the same underlying asset does not bear much meaning. Therefore in this study, comparisons with HL volatility models have also been made to better capture the differences between the volatility models. Volatility calculations of this study have been made by using the GARCH (1.1) base model and over USD/TL exchange rate and ISE 30 index between the dates 06/30/2008 – 06/30/2009. Comparisons between predictions of the models have been performed by MSPE, CW and DMW tests and significant differences between GARCH (1.1) model and HL volatility model have been determined.
Highlights
Abstract*Strong*volatility*forecasts*in*making*financial*decisions*play*an*important*role* for*financial*institutions*and*other*users.*In*practice,*volatility*models*like*the* MA,* EWMA* and* GARCH* are* used* as* volatility* outlook.* * GARCH* and* EWMA* type* volatility* forecasting* models* perform* better* in* capturing* fluctuations* and* developments* in* financial* markets* than* the* models* that* assume* the* variance* is* constant.* * ,* results* of* these* models* turn* out* to* be* close* to* each* other* and* comparing* the* results* of* calculations* performed* on* the* same* underlying* asset* does* not* bear* much* meaning.* * in* this* study,* comparisons* with* HL* volatility* models* have* * been* made* to* better* capture*the*differences*between*the*volatility*models.*Volatility*calculations*of* this* study* have* been* made* by* using* the* GARCH* (1.1)* base* model* and* over* USD/TL* exchange* rate* and* ISE* 30* index* between* the* dates* 06/30/2008* –* 06/30/2009.* Comparisons* between* predictions* of* the* models* have* been* performed* by* MSPE,* CW* and* DMW* tests* and* significant* differences* between* GARCH*(1.1)*model*and*HL*volatility*model*have*been*determined.** * Keywords:!Forecasting,!Volatility,!Risk!
! INTRODUCTION*Since!the!start!of!floating!exchange!rate!system!in!the!early!1970s!there!have!been!continuous! intense! discussions! until! present! day! regarding! the! impact! of! foreign! exchange! rates! on! international! trade! (Cheong,! 2004,! p.! 1)! Today! no! financial! analyst! can! disregard! the! importance! of! volatility! forecast! methods! in! future! oriented! decision! making! processes! (Suganuma,!2000,!p.!1).!!!That!said,!we!can!give!some!answers!to!the!question!of!which!method! should!be!used!in!forecasting!the!volatility.!When!the!literature!studies!are!assessed!it!is!seen! that!there!are!findings!showing!the!negative!and!positive!effects!of!exchange!rate!fluctuations.!! ! In! today’s! financial! markets! volatility! forecasts! are! frequently! used! in! asset! management,! portfolio!management!and!pricing!derivative!products.!They!play!key!role!for!financial!markets!.
Ci Oi ! !Historical*OpenTHighTLowTClose*Volatility:*Garman*Klass* Garman! Klass! (1980)! has! proposed! a! new! volatility! estimator! which! is! based! on! opening,! closing,! lowest! and! highest! price! information.! This! model! derives! its! forecasts! with! the! assumptions! that! there! is! no! jump! in! the! opening! price,! drift! variable! is! zero! and! the! security! price!follows!historical!Brownian!motion!without!drift.! !.
Summary
Strong*volatility*forecasts*in*making*financial*decisions*play*an*important*role* for*financial*institutions*and*other*users.*In*practice,*volatility*models*like*the* MA,* EWMA* and* GARCH* are* used* as* volatility* outlook.* * GARCH* and* EWMA* type* volatility* forecasting* models* perform* better* in* capturing* fluctuations* and* developments* in* financial* markets* than* the* models* that* assume* the* variance* is* constant.* * ,* results* of* these* models* turn* out* to* be* close* to* each* other* and* comparing* the* results* of* calculations* performed* on* the* same* underlying* asset* does* not* bear* much* meaning.* * in* this* study,* comparisons* with* HL* volatility* models* have* * been* made* to* better* capture*the*differences*between*the*volatility*models.*Volatility*calculations*of* this* study* have* been* made* by* using* the* GARCH* (1.1)* base* model* and* over* USD/TL* exchange* rate* and* ISE* 30* index* between* the* dates* 06/30/2008* –* 06/30/2009.* Comparisons* between* predictions* of* the* models* have* been* performed* by* MSPE,* CW* and* DMW* tests* and* significant* differences* between* GARCH*(1.1)*model*and*HL*volatility*model*have*been*determined.** * Keywords:!Forecasting,!Volatility,!Risk!
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