Volatility-forecasting-using-high-frequency-data-Evidence-from-stock-markets_2014_Economic-Modelling

Volatility forecasting using high frequency data:Evidence from stock markets ☆

Sibel Çelik a ,⁎,Hüseyin Ergin b

a Dumlupinar University,School of Applied Sciences,Turkey b

Dumlupinar University,Business Administration,Turkey

a b s t r a c t

a r t i c l e i n f o Article history:

Accepted 24September 2013JEL classi fication:C22G00

Keywords:Volatility

Realized volatility High frequency data Price jumps

The paper aims to suggest the best volatility forecasting model for stock markets in Turkey.The findings of this paper support the superiority of high frequency based volatility forecasting models over traditional GARCH models.MIDAS and HAR-RV-CJ models are found to be the best among high frequency based volatility forecasting models.Moreover,MIDAS model performs better in crisis period.The findings of paper are important for financial institutions,investors and policy makers.

©2013Elsevier B.V.All rights reserved.

1.Introduction

Volatility plays an important role in theoretical and practical applica-tions in finance.The availability of high frequency data brings a new dimension to volatility modeling and forecasting of returns on financial assets.First and foremost,nonparametric estimation of volatility of asset returns becomes feasible and so modeling and forecasting volatility of asset returns has been a focus for researchers in the literature (Andersen and Bollerslev,1998;Andersen et al.,2001,2003b ,2007;Corsi,2004;Engle and Gallo,2006;Ghysels et al.,2004,2005,2006a,b;Hansen et al.,2010;Shephard and Sheppard,2010).The empirical find-ings of existing studies support the superiority of high frequency based volatility models to popular GARCH models and stochastic volatility models in the literature (Andersen et al.,2003b ).Besides,earlier studies point to importance of allowing for discontinuities (jumps)in volatility models and pricing derivatives (Andersen et al.,2002;Chernov et al.,2003).Availability of high frequency data is also a turning point in order to distinguishing jump from continuous part of price process.Empirical findings from recent studies show that incorporating the jumps to volatility models increase the forecasting performance of models supporting the earlier evidence (Andersen et al.,2003b,2007).

This paper aims to suggest the best volatility forecasting model in stock markets in Turkey.For this purpose,first,we analyze the data generating process and calculate the high frequency based volatility and examine the return and volatility characteristics.Second,we propose the best volatility forecasting model by comparing different volatility forecasting models.

In doing so,the paper will contribute to the literature in terms of filling five main gaps.First,it suggests the best volatility forecasting model from the alternatives including high frequency-based models and traditional GARCH models.Second,it reveals the forecasting performance of volatility models during the periods of structural change.Because,recent studies in the literature indicate that financial crisis affect the volatility dynamics deeply (Dungey et al.,2011).Third,it analyses forecasting performance of volatility in stock futures markets rather than spot markets.There are three reasons for usage of stock futures markets in this study.Firstly,there are findings in the literature that futures markets respond to new information faster than spot markets (Stoll and Whaley,1990).Secondly,using futures contracts rather than spot indexes re-duces nonsynchronous trading problems (Wu et al.,2005).Thirdly,using futures contracts provides additional evidence to the existing literature on spot markets (Wu et al.,2005).Fourth,it compares the findings at different frequencies to inference about optimal fre-quency since the sampling selection is important for high frequency data based studies.Because,while higher sampling frequency may cause bias in realized volatility,lower sampling frequency may cause information st,it contributes to literature in terms of presenting evidence from an Emerging Market.

Economic Modelling 36(2014)176–190

☆This paper is based on my doctoral dissertation “Volatility Forecasting in Stock Markets:Evidence From High Frequency Data of Istanbul Stock Exchange ”which was completed at Dumlupinar University,in 2012.

⁎Corresponding author at:Dumlupinar University,School of Applied Sciences,Insurance and Risk Management Department,Turkey.Tel.:+902742652031x4664.

E-mail address:sibelcelik1@ (S.

Çelik).0264-9993/$–see front matter ©2013Elsevier B.V.All rights reserved.

/10.1016/j.econmod.2013.09.038

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