Sistemik ve Likidite Risk Faktörleriyle Portföy Optimizasyonu

Evolution of portfolio optimization is expected in the direction that satisfies the needs of traders by answering the current status of global markets. In the past decade, previously unknown new risk types that are needed to be taken into consideration in the portfolio optimization process has emerged. These risk types are systemic risk and liquidity risk. Even without the economic new landscape, continuous improvement of portfolio optimization results is an ongoing challenge of both academicians and practitioners. My original contribution to knowledge is by evaluating new risk measures from portfolio optimization perspective, analyzing portfolio optimization results in detail based on empirical data and developing a new methodology, Markov Transition Matrix Approach for return estimation. In addition to this, I adapted an emerging multi-criteria decision-making methodology, TODIM, to portfolio optimization. The main conclusion of the thesis would be summarized as follows: "Improvement of return estimation methodologies is an infinite journey that will last forever as the dynamics of the markets and investor philosophies will not stay same forever. What can be done resides on the efficient usage of data. This thesis approaches the return estimation from this angle by utilizing the data more efficiently for portfolio optimization problems. However, the same Markov Transition Approach methodology could be adapted for different purposes as well. Another improvement angle would be utilizing TODIM method for portfolio allocation to mitigate risk of noise in estimates or incorrect risk and return estimates, form more diversified portfolios providing better risk-adjusted returns than equally weighted portfolios"