The objective of the study is to analyze the effects of two breakpoints on the relationships of Istanbul Stock Exchange with the European stock markets and on the relationships among these European stock markets to increase the economic integration. The breakpoints are the execution of the Customs Union Agreement of Turkey with the European Union in 1/1/1996 and the introduction of the Euro in 1/1/1999. While both breakpoints have effects on Turkey&rsquo / s economic relations, the European Union countries are expected to be influenced by only the introduction of the Euro. Stock market indices provided by DataStream is utilized. The statistical techniques used include the correlation and cointegration analysis. Results indicate that when examined on pair wise basis Turkish stock market has more liaisons with the European stock markets, in general, after the Customs Union / but less liaisons after the conversion to Euro. However, when examined as a group, the cointegration result finds the Euro as influential as the Customs Union. Alternatively, the European stock markets have decreasing integrations as a result of correlation analysis after the Euro, but it is an influential breakpoint according to cointegrating structures.

In the original Vasicek model interest rates are calculated assuming that volatility remains constant over the period of analysis. In this study, we constructed a stochastic volatility model for interest rates. In our model we assumed not only that interest rate process but also the volatility process for interest rates follows the mean-reverting Vasicek model. We derived the density function for the stochastic element of the interest rate process and reduced this density function to a series form. The parameters of our model were estimated by using the method of moments. Finally, we tested the performance of our model using the data of interest rates in Turkey.

The Prime Ministry of Undersecretariat of Treasury maintaining the financial administration of Republic of Turkey has several tasks to handle one of which is to manage the government&rsquo / s debt in a way that minimizes the cost regarding risk. Choosing the right instrument and maturity composition that has the least cost and risk is the debt management problem to be dealt with and is affected by many stochastic factors. The objective of this thesis is the optimization of the debt management problem of the Turkish Government via a stochastic simulation framework under the constraints of changes in portfolio positions. Value-at-Risk of the optimal portfolio is calculated to measure market risk. Macroeconomic variables in the optimization problem are modeled with econometric models like autoregressive processes (AR), autoregressive integrated moving average processes (ARIMA) and generalized autoregressive conditionally heteroscedastic (GARCH) processes. The simulation horizon is 2005-2015. Debt portfolio is optimized at 2006 and 2015 where the representative scenarios for the optimization are found by clustering the previously generated 25,000 scenarios into 30 groups at each stage.

In this study, both uni-regime GARCH and Markov Regime Switching GARCH (SW-GARCH) models are examined to analyze Turkish Stock Market volatility. We investigate various models to find out whether SW-GARCH models are an improvement on the uni-regime GARCH models in terms of modelling and forecasting Turkish Stock Market volatility. As well as using seven statistical loss functions, we apply Superior Predictive Ability (SPA) test of Hansen (2005) and Reality Check test (RC) of White (2000) to compare forecast performance of various models.

International Fisher Effect (IFE) is a theory in international finance which asserts that the spot exchange rate between countries should move in opposite direction with the interest rate differential between these countries. The aim of this thesis is to analyze whether differences in nominal interest rates between countries and the movement of spot exchange rates between their currencies tend to move together over the long run. The presence of IFE is tested among the G-5 countries and Turkey for the period from 1985:1 to 2007:12. The long run relationship is estimated with the Johansen co-integration method and supportive evidence is found for all country pairs. Individually modeled equations are further tested with the Dynamic SUR method. Those DSUR equations that include the Turkish currency provide supportive evidence for IFE that higher interest rates in favor of Turkey would cause depreciation of the Turkish Lira. The magnitude of the effect is found to be lower than expected which indicates that there might be other factors in economy, such as inflation rates, that affect the exchange rate movements.

Over the past decade, fuzzy systems have gained remarkable acceptance in many fields including control and automation, pattern recognition, medical diagnosis and forecasting. The fuzzy system application has also been accepted as a promising approach to dealing with uncertainty in real estate valuation analysis. This is mainly due to the necessity of coping with a large number of qualitative and quantitative variables that affect the value of a real property. The appraisers use a great deal of judgment to identify both the characteristics that contribute to property values and the relationships among these characteristics in order to derive estimates of market values. This thesis uses the two widely-used fuzzy rule-based systems / namely the Mamdani and Takagi- Sugeno-Kang (TSK) type fuzzy models in an attempt to examine the main determinants of office rents in Istanbul Central Business District (CBD). The input variables of the fuzzy rule-based systems (FRBS) comprise: i) physical attributes of office spaces and office buildings, ii) lease contract terms, and iii) tenants&rsquo / perception of the office rent determinants, tenants&rsquo / location of residence, tenants&rsquo / transportation modes, etc and as the output the system proposes the office property&rsquo / s rental price. Obtaining office rent determinants is a significant issue for both practitioners and academics. While,practitioners use them directly in demand and sensitivity analyses, academics are more interested in the relative significance of these variables and their effect on the variation in office rent to forecast market behavior. Our data set includes a detailed survey of 500 office spaces located in Istanbul CBD. We have carried out two Mamdani-type FRBS and two TSK-type FRBS for the office space and office building data sets. In these FRBS analyses, firstly the so-called representative office spaces are determined, then the average office space rents are estimated. Finally, the spatial variation in the average office rents across the CBD sub-districts, along with the Office space rent variations with respect to different clusters, like number of workers, number of floors and so on, have been analyzed. We believe that presenting the spatial variation in office rents will make a noteworthy contribution both to the real estate investors and appraisers interested in Istanbul office market.

In this thesis the relationship between Brent oil prices, LME copper prices, Turkish gold and silver spot prices, XU100 index, interest rate and exchange rate is examined. Their long run Granger causality relationship is investigated by looking at Wald statistics. The short run relationship between them is examined by using generalized impulse responses. The data range is from January 2, 2002 to February 24, 2011. Due to the oil crisis in 2008, we divide the data into three periods: January 2, 2002 to December 31 as first period, 2007, from January 1, 2008 to December 31 as second period, 2008 and January 1, 2009 and February 24, 2011 as third period. We conduct each test separately for these periods but in third period we use Toda-Yamamoto procedure since maximum order of integration is 1.

Day-ahead spot electricity markets are the most transparent spot markets where one can find integrated supply and demand curves of the market players for each settlement period. Since it is an indicator for the market players and regulators, in this thesis we model the spot electricity prices. Logarithmic daily average spot electricity prices are modeled as a summation of a deterministic function and multi-factor stochastic process. Randomness in the spot prices is assumed to be governed by three jump processes and a Brownian motion where two of the jump processes are mean reverting. While the Brownian motion captures daily regular price movements, the pure jump process models price shocks which have long term effects and two Ornstein Uhlenbeck type jump processes with different mean reversion speeds capturing the price shocks that affect the price level for relatively shorter time periods. After removing the seasonality which is modeled as a deterministic function from price observations, an iterative threshold function is used to filter the jumps. The threshold function is constructed on volatility estimation generated by a GARCH(1,1) model. Not only the jumps but also the mean reverting returns following the jumps are filtered. Both of the filtered jump processes and residual Brownian components are estimated separately. The model is applied to Austrian, Italian, Spanish and Turkish electricity markets data and it is found that the weekly forecasts, which are generated by the estimated parameters, turn out to be able to capture the characteristics of the observations. After examining the future contracts written on electricity, we also suggest a decision technique which is built on risk premium theory. With the help of this methodology derivative market players can decide on taking whether a long or a short position for a given contract. After testing our technique, we conclude that the decision rule is promising but needs more empirical research.

The purpose of this study is to examine the volatility spillover among a country&rsquo / s foreign exchange, bond and stock markets and the volatility transmission from the global bond, stock and commodity markets to these local financial markets. The sample for the study includes data from both emerging and developed economies in the time period between 2004 and 2011. A multivariate GARCH methodology with the BEKK representation is applied for the local financial markets and global variables are included as exogenous variables into the model. The volatility integration of the financial markets of the emerging economies is stronger compared to the integration of the developed economies. Global variables have a spillover effect on the developed markets only after the global financial crisis, whereas they significantly affect the volatility in emerging markets for both the pre- and post-crisis period. North American countries in the sample, U.S. and Mexico, have low local volatility integration in the pre-crisis era and the integration rises in the post-crisis period. Moreover, they are more open to the internal and global short-term shocks in the post-crisis period. Germany and Turkey are the representatives of the EMEA (Europe, Middle East and Africa) region and they have high local market integration and are open to global shocks for both sub-periods. Far Eastern markets, Japan and Korea, also have high local market integration and their vulnerability to the global effects is large and getting larger for the post-crisis period. The most important limitation of this thesis is the difficulty of reaching sharp generalizations due to the small number of countries analyzed. This limitation can be addressed by the inclusion of a larger number of geographically dispersed countries. The most noteworthy originality of this study is the addition of the exogenous global variables for modeling volatility spillovers. Furthermore, comparison of results for emerging versus developed markets and the pre- versus post-crisis periods is another contribution of this study to the existing literature. The findings of this study can be used by investors interested in assessing the risks of investing internationally.

This work is an extension of the classical Cox-Ross-Rubinstein discrete time market model in which only one risky asset is considered. We introduce another risky asset into the model. Moreover, the random structure of the asset price sequence is generated by bivariate finite state Markov chain. Then, the interest rate varies over time as it is the function of generating sequences. We discuss how the model can be adapted to the real data. Finally, we illustrate sample implementations to give a better idea about the use of the model.