Ozmen, A.
Weber, Gerhard Wilhelm
Karimov, A.
Recent financial crises, with an increased volatility and, hence, uncertainty factors, have introduced a high "noise" into the data taken from the financial sectors and overall from any data related to the financial markets, so that the known statistical models do not give trustworthy results. As we know the solutions of the optimization problem can show a remarkable sensitivity to perturbations, coming from the data, in the parameters of the problem. To overcome this kind of difficulties, the model identification problem has been generalized by including the existence of uncertainty with respect to future scenarios through Conic Multivariate Adaptive Regression Splines (CMARS), whose data are assumed to contain certain information with respect to input variables. Then, with the help of robust optimization which can deal with a wider data uncertainty, CMARS has been robustified and named as Robust CMARS (RCMARS). We decrease the estimation variance by using robustification in CMARS. In contrast to early studies, where RCMARS was presented in theory and method and illustrated with a numerical example, in this study, we present RCMARS results for real-world data from financial markets, particularly, from the Istanbul Stock Exchange, Turkish and US economy, showing that RCMARS can generate more accurate models with a smaller variance.


The new robust conic GPLM method with an application to finance: prediction of credit default
Ozmen, Ayse; Weber, Gerhard Wilhelm; Cavusoglu, Zehra; DEFTERLİ, ÖZLEM (2013-06-01)
This paper contributes to classification and identification in modern finance through advanced optimization. In the last few decades, financial misalignments and, thereby, financial crises have been increasing in numbers due to the rearrangement of the financial world. In this study, as one of the most remarkable of these, countries' debt crises, which result from illiquidity, are tried to predict with some macroeconomic variables. The methodology consists of a combination of two predictive regression model...
Empirical comparison of portfolio risk diversification algorithms
Yerli, Çiğdem; Kestel, Sevtap Ayşe; Schindler, Nilüfer; Department of Financial Mathematics (2018)
The enhanced correlations during global financial crisis has revealed that simple asset allocation portfolios prove to be not well-diversified across different risk factors, which makes the risk based asset allocation strategies popular. However, the strategies still construct the risk concentrated portfolios due to the correlation among the asset classes. As a result, risk allocation among uncorrelated risk factors instead of risk allocation among asset classes have become widely used. This thesis aims to di...
Application of r-vine copula method in Istanbul stock market data: A case study for the construction sector
Farnoudkia, Hajar; Purutçuoğlu Gazi, Vilda (Ankara Yıldırım Beyazıt Üniversitesi , 2020-12-01)
In the stock market, the relationship between the sectorial changes can be very informative in order to predict the changes in prices of assets from each sector. In order to understand these sectorial relations, various studies have been conducted. In one of the recent studies, the construction sector in Turkey was investigated in terms of its effect in other Turkish sectors since it is one of the leading sectors in Turkey and its assets have a significant impact in stock markets. Hereby, in this study we d...
A Comparative study for nonlinear structure of the interest rate pass through
Değer, Osman; Yıldırım Kasap, Dilem; Department of Economics (2012)
This study investigates the interest rate pass through from the money market rate to the lending rate by utilizing monthly data of fifteen countries, grouped as high income, upper middle income and lower middle income, over the period 1999:01-2011:09. Taking the linear cointegration test of Engle-Granger as benchmark, we employ threshold cointegration tests of Enders and Siklos (2001) in order to account for the possible nonlinearities in the pass-through process. Empirical results reveal that the pass thro...
Stability advances in robust portfolio optimization under parallelepiped uncertainty
Kara, Guray; Ozmen, Ayse; Weber, Gerhard Wilhelm (2019-03-01)
In financial markets with high uncertainties, the trade-off between maximizing expected return and minimizing the risk is one of the main challenges in modeling and decision making. Since investors mostly shape their invested amounts towards certain assets and their risk aversion level according to their returns, scientists and practitioners have done studies on that subject since the beginning of the stock markets' establishment. In this study, we model a Robust Optimization problem based on data. We found...
Citation Formats
A. Ozmen, G. W. Weber, and A. Karimov, “A NEW ROBUST OPTIMIZATION TOOL APPLIED ON FINANCIAL DATA,” PACIFIC JOURNAL OF OPTIMIZATION, pp. 535–552, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55224.