Economics Graduate Program - Master Degree

Bu koleksiyon için kalıcı URI

http://hdl.handle.net/11527/17505

Gözat

Yazar "Güloğlu, Bülent" ile Economics Graduate Program - Master Degree'a göz atma
  • Öge
    Constructing A Financial Stress Index For Turkey: A Multivariate Garch Approach
    (Institute of Social Sciences, 2018) Şenol, Pınar ; Güloğlu, Bülent ; 534471 ; Economics ; İktisat
    Various economic crises have been experienced due to the dynamics of the country as well as the global crises that have lived in the world from past to present. Many of the important studies in economics aimed to estimate these fluctuations in the markets and to examine their relations with each other. In this context, since the early 2000s, financial stress indexes have been established by economists or central banks for various countries. The financial stress indexes created are specific to countries or regions and have some differences. These differences are the methods as well as the data types that are used. In this study, the financial stress indexes created for Turkey. Also, different countries are examined in the literature review. There is the necessity of constructing a new financial stress index because of the economic fluctuations in Turkey in recent years. The financial stress index of Turkey demonstrates the economic activity after important financial events that are called as financial crisis. The financial stress index which is a continuous variable as a time serie and its extreme values are called financial crises in the literature. The financial stress index can be expected to increase in the case of expected financial loss, risk potential or uncertainty financial conditions. In this study, the financial stress index of Turkey is generated with using the banking sector, equity market, money market and exchange market variables. The financial stress index of Turkey is constructed on daily basis for the period from 2 January 2007 to 29 November 2017. The data diversity has been expanded in this study when compared to previous studies in Turkey that is why this study has importance for Turkey. Firstly, the variables of each sector were standardized.
  • Öge
    The Effects Of The Exchange Rate Volatility On Turkish Exports:a Panel Data Analysis
    (Institute of Social Sciences, 2019) Akıl, Halil İbrahim ; Güloğlu, Bülent ; 568995 ; Economics ; Ekonomi
    After the collapse of the Bretton Woods system(1946-1973), most of countries started to adopt the floating exchange rate system. Several research has focused the interaction between volatility of exchange rate and export performance of the countries after this period. Turkish export shows an increasing trend in 2000s with floating exchange rate regime. Literature documents that Turkish export has been significantly affected from volatility of exchange rate. In this paper, I analyze the impact of the exchange rate volatility on Turkish sectoral basis export volume. By considering the regime effect, I only cover the data for the period of 2001-2018, in which floating exchange rate regime adopted in Turkey.
  • Öge
    Forecasting Electricity Prices In Turkey: A Comparison Of Classical Econometrics And Machine Learning Techniques
    (Institute of Social Sciences, 2018) Karagülle, Yunus Emre ; Güloğlu, Bülent ; 512523 ; Economics ; İktisat
    Liberalization of electricity markets trends has been widening in the world since 1990s and the process is still continuing in many countries. As a result of liberalization, the private retailer and distribution companies role has increased in the markets. Like other countries, government enterprises was operating the Turkish Electricity market before 2000s. However, Turkish Government enacted a electricity market law for deregulation of the market in 2001. Accordingly, Turkish Electricity Trading and Contracting (TETAS) was established in 2001 with the aim of sustaining the energy trade and reach a competitive electricity market. Afterwards, Day-Ahead market and Intra-Day markets are established in the following years. As a result of the liberalization process, forecasting electricity prices has become an important requirement in energy companies' decision-making process to maximize profits by offering competitive bid and ask rates. On the contrary to other time series, electiricity prices have complex structures and dynamic determinants which makes difficult to forecast prices. For examle, one of the determinants of the electricity is demand and it is known that demand is highly related with temperatures. In summer, electricity demand expeced to increase due to cooling needs. However, it may change accordingly with tempetures. Futhermore, electiricty demand is also changing according to hour of the day or day of the week and so on. Another thing is that, generation source and type and the amount may also affect the prices. Consequently, electiricty prices are generally representing complex non-linear structures. There are many methods for forecasting electricity prices. However, artificial neural networks are known to be good forecasting method for covering non-linear effect of the prices. On the other hand, time series methods are easy to forecast and interpret the relationships. Lastly, quantile regressions are capturing the different conditional distributions and may be useful for capturing spkies. Therefore, artificial neural networks, time series and quantile regression is used. First, non-linear autoregressive network with exogenous inputs (NARX) is used with Levenberg-Marquardt learning algorithm. It is a two-layer feedforward network with sigmoid activation function.
  • Öge
    Volatility Spillovers And Dynamic Hedgings: Evidence From Selected Stock Markets, Precious Metalsand Oil Futures
    (Institute of Social Sciences, 2019) Yılmaz, Tunahan ; Güloğlu, Bülent ; 572571 ; Economics ; Ekonomi
    This paper investigates the most optimal hedging portfolio for each emerging countries by performing dynamic conditional correlations. Throughout the study, spanning the period from 02/01/2006 to 01/11/2018, we used daily index values of S&P 500 (USA), FTSE-100 (UK), NIKKEI 225 (Japan), Nasdaq(USA), DAX_30 (GERMANY) to represent developed stock markets. Investment instruments in emerging countries are represented by two groups. One of them is exchange rates in terms of U.S. Dollars such as Brazilian Real, Turkish Lira, Malaysian Ringgit, Indian Rupee Another one contains domestic stock market indices such as BOVESPA, BIST_100, FTSE_BURSA, and BSE_SENSEX. Also, we added some precious metals such as gold, silver, platinum, and palladium, as well as some types of oil such as Brent and WTI to the hedging portfolio as a commodity. In this essay, the presence of the long memory property and asymmetry were taken into account. To test for long term memory property, Rescaled Range Statistics (R/S), Geweke and Porter-Hudak (GPH) Model and Gaussian Semi Parametric (GSP) methods were employed.