USING EVMA AND GARCH METHODS IN VAR CALCULATIONS: APPLICATION ON ISE-30 INDEX

Turhan Korkmaz (Zonguldak Karaelmas University)
Kazım Aydın (Zonguldak Karaelmas University)

Volatility tends to happen in clusters. The assumption that volatility remains constant at all times can be fatal. In order to forecast volatility in stock market, there must be methodology to measure and monitor volatility modeling. Recently, EWMA and GARCH models have become critical tools for time series analysis in financial applications.

In this study, after providing brief descriptions, ISE-30 Index return volatility and individual stocks return volatility have been tested by using EWMA and GARCH methods.

JP Morgan Riskmetrics method has been used for EWMA method. Various data ranges (number of days) have been selected to use in calculations. It is determined that the most recent data have asserted more influence on future volatility than past data.

RATS program has been used for GARCH methodology. Time series has been used to estimate volatility and giving more weighting to recent events as opposed to older events. The outcome is GARCH provides more accurate number than EWMA.

Daily VaR numbers have been calculated by using EWMA and GARCH models for stocks inside the ISE-30 Index. The results are satisfactory for forecasting volatility at 95% and 99% confidence level. These two methods enhance the quality of the VaR models.

These findings suggest that traders and risk managers are able to generate portfolio profit and minimize risks if they obtain a better understanding of how volatility is being forecasted.