International Research Journal of Finance and Economics ISSN 1450-2887 Issue 93 (2012) © EuroJournals Publishing, Inc. 2012 http://www.internationalresearchjournaloffinanceandeconomics.com Analyzing High Frequency Financial Data and Modeling Volatility using GARCH Models with Normal and Stable Paretian Distributions: An Example from an Emerging Market Ece Oral Research and Monetary Policy Department Central Bank of the Republic of Turkey İstiklal Cad. 10 Ulus, 06100 Ankara, Turkey E-mail: Ece.Oral@tcmb.gov.tr Evrim Oral LSUHSC, School of Public Health, Biostatistics Program 2020 Gravier Street, New Orleans, LA 70112 USA Abstract Many methods in finance rest upon the assumption that asset returns follow a normal distribution. However, finance data often depart from the normal distribution. Since stable distributions can accommodate both fat tails and asymmetry, they often provide a very good fit to empirical data. This paper examines the statistical distributions of high frequency (intra-daily) TRY/USD foreign exchange changes and the daily-hourly volatility of the return series by employing normal and stable GARCH models before and after the global financial crisis. Empirical evidence supports that a GARCH model with stable Paretian innovations fits returns better than the normal distribution; empirical evidence also supports that while the global financial crisis has a negative impact on the distribution of returns, it does not affect volatility of an emerging market, namely the Turkish Interbank Foreign Exchange Market. Keywords: Stable distributions; Volatility; Non-normality; High-frequency data, Foreign exchange, Conditional heteroskedasticity 1. Introduction Forecasting volatility in financial markets is a key element in investment decisions, security valuation, risk management and modeling. Financial market volatility can have a wide effect on the economy as a whole. For instance, the September 11 attacks in the United States triggered a turmoil in financial markets not only in the US but across several continents. This example provides evidence of the strong link between stock market uncertainty and public confidence in the financial markets. Therefore, policy makers often rely on estimates of volatility as a measure of the vulnerability of the financial markets and the economy. Forecasting expected returns is also as important as forecasting volatility in finance. Financial decisions are exclusively based on expected returns and risk of alternative investment opportunities. The distributional assumptions for financial processes have important theoretical implications. Solutions to such problems like portfolio selection, option pricing, and risk management depend