ISE review

Istanbul Stock Exchange

Volume 9 No:35

Subjects

How to Manage the Mortgage Credit Risk in Turkey?
Can Dual-indexed Mortgages be a Remedy?
Ali ALP & M. Mete DOĞANAY

Forecasting Financial Variables by the Grey Theory
Sadık ÇUKUR & Erdoğan KOTİL & Resul ERYİĞİT

Overreaction Hypothesis and an Empirical Work on the Istanbul Stock Exchange
Şerafettin SEVİM & Birol YILDIZ & Soner AKKOÇ

Purchasing Power Parity and ARIMA Models in Forecasting
Exchange Rates: The Case of Turkey
Hasan VERGİL & Filiz ÖZKAN

Global Capital Markets

ISE Market Indicators

How To Manage The Mortgage Credit Risk In Turkey? Can Dual-Indexed Mortgages Be A Remedy?

Ali ALP
M. Mete DOĞANAY

Abstract
A market-oriented housing finance system has been under discussion in Turkey recently. In this article we analyze different types of mortgages that have been used in developed and developing countries to select the one that is most appropriate for Turkey—one which minimizes risks for both lenders and borrowers. Each type of mortgage presents different risks to borrowers and lenders. After taking into consideration the economic history of Turkey, we conclude that the most appropriate mortgage for Turkey that minimizes risk is the dual-indexed mortgage model. We test this model by using data from the most volatile period of the Turkish economy, applying historical simulation and Monte-Carlo simulation. We find that, using this model; the total loan is paid off in a reasonable period without causing substantial difficulty for lenders and borrowers. Analyses confirm that borrowers and lenders are exposed to minimum risk if this type of mortgage is originated in Turkey.

Forecasting Financial Variables By The Grey Theory

Sadık ÇUKUR
Erdoğan KOTİL
Resul ERYİĞİT

Abstract
Forecasting financial and economic variables is one of the most attracted subjects in finance and many models and studies are devoted for predicting future. This study is one of them and uses relatively new prediction model, Grey Theory. This theory is suitable for forecasting competitive environment where the model uncertainty is present and decision makers have insufficient information. We use this model for one-step-ahead prediction of daily and monthly Turkish Lira/US Dollar exchange rate and Istanbul Stock Exchange Market Composite Index (ISEM-100). The results indicate a moderate success for a highly volatile environment comparing to the previous studies and models.

Overreaction Hypothesis And An Emprical Work On The Istanbul Stock Exchange

Şerafettin SEVİM
Birol YILDIZ
Soner AKKOÇ

Abstract
In this study, the weak form efficiency of the ISE is tested with the operational strategy which the overreaction hypothesis projects. The period between January 1, 1988 and December 31, 2002 is examined. The total number of 4 periods consisted of 36 months, were examined. According to the results of the empirical study show that the Winner portfolio provides lower yield with a ratio of 50,57 %, which is below the market whereas the Loser portfolio produces higher yield with a ratio of 66,11 %, above the market. The findings gathered by under the operational strategy which overreaction hypothesis foresees have shown that the ISE is not a weak form of efficient market. The side effect is seen to become more evident in the fourth period including financial crisis that prevailed in Turkey between November 2000 and February 2001.

Purchasing Power Parity And Arima Models In Forecasting Exchange Rates: The Case Of Turkey

Hasan VERGİL
Filiz ÖZKAN

Abstract
This study aims to forecast the exchange rates of Turkey by using the Purchasing Power Parity theory (PPP) and its competitor the ARIMA models and compare their forecasting powers. In doing so, the models are estimated using the monthly real exchange rate data between Turkish Lira and Turkey’s five biggest trading partners (The USA, Germany, England, France and Italy) for the period 1980-2003 and using the monthly real exchange rate data between Turkish Lira and European Union for the period 1999-2005. The conclusions based on the error terms statistics and the regression coefficients suggest that, compared to the PPP models, the ARIMA models have better forecasting powers.