The paper considers robust optimization to cope with uncertainty about the stock return process in one period option hedging problems. The robust approach relates portfolio choice to uncertainty, making more cautious hedges when uncertainty is high. We represent uncertainty by a set of plausible expected returns of the underlying stocks and show that for this set the robust problem is a second ...

The present study is an attempt toward evaluating the performance of portfolios and asset selectionusing cross-efficiency evaluation. Cross-efficiency evaluation is an effective way of ranking decisionmaking units (DMUs) in data envelopment analysis (DEA). Conventional DEA models assume nonnegativevalues for inputs and outputs. However, we know that unlike return and skewness, varianceis the on...

Multi-asset class (MAC) portfolios can be comprised of investments in equities, fixed-income, commodities, foreign-exchange, credit, derivatives, and alternatives such as real-estate and private equity. The return for such non-linear portfolios is asymmetric with significant tail risk. The traditional Markowitz Mean-Variance Optimization (MVO) framework, that linearizes all the assets in the po...

Dr. V. J.Sebastian (Corresponding author) Institute of Management Technology Gaziabad, Delhi, India E-mail: [email protected] Abstract Portfolio optimization, in case of finance, is the tradeoff between risk and return to maximize profit or return from the portfolio. Financial regulations are country specific and it depends upon the economic conditions prevailing in the country. The portfolio of...

This paper features a tri-criteria analysis of Eurekahedge fund data strategy index data. We use nine Eurekahedge equally weighted main strategy indices for the portfolio analysis. The tri-criteria analysis features three objectives: return, risk and dispersion of risk objectives in a Multi-Criteria Optimisation (MCO) portfolio analysis. We vary the MCO return and risk targets and contrast the ...

The portfolio construction problem usually has been viewed in the framework of risk-return trade-off. Using deterministic and stochastic portfolio models used to solve the problem lead to unrealistic results as both the expected return rate and the risk are vague. Moreover, the decision maker frequently deals with insufficient data when selecting a portfolio. Using fuzzy models allows removal o...

In this chapter we provide a methodology to solve dynamic portfolio strategies considering realistic assumptions regarding the return distribution. First, we analyze the empirical behavior of some equities, suggesting how to approximate an historical return series with a factor model that accounts for most of the variability and proposing a methodology to generate realistic return scenarios. Th...

Dynamic portfolio trading system is used to allocate one’s capital to a number of securities through time in a way to maximize the portfolio return and to minimize the portfolio risk. Genetic programming (GP) as an artificial intelligence technique has been used successfully in the financial field, especially for the forecasting tasks in the financial markets. In this paper, GP is used to devel...