From stochastic dominance to mean-risk models: Semideviations as risk measures

محاسبه ریسک با معیار تسلط تصادفی و مقایسه آن با سایر معیارهای متداول در بورس اوراق بهادار تهران

Nowadays stock market as a means to facilitate and direct micro investment for economic development is deemed as one of the most effective tools in the market economy system. Analyzing the risk and output of various companies' shares is among the most important preconditions of stock investment. In general, although conventional risk measures such as Beta and standard deviation are relatively e...

Dual stochastic dominance and quantile risk measures

Following the seminal work by Markowitz, the portfolio selection problem is usually modeled as a bicriteria optimization problem where a reasonable trade-off between expected rate of return and risk is sought. In the classical Markowitz model, the risk is measured with variance. Several other risk measures have been later considered thus creating the entire family of mean-risk (Markowitz type) ...

Decision criteria in risk analysis : an application of stochastic dominance with respect to a function

On Mean-Risk Models Consistent with Stochastic Dominance

Comparing uncertain prospects is one of fundamental interests of the economic decision theory. Two methods are frequently used for modeling the choice among uncertain outcomes: stochastic dominance and mean-risk approaches. The former is based on the axiomatic model of risk-averse preferences but does not provide a convenient computational recipe. It is, in fact, a multiple criteria model with ...

On Extending the LP Computable Risk Measures to Account Downside Risk

A mathematical model of portfolio optimization is usually quantified with mean-risk models offering a lucid form of two criteria with possible trade-off analysis. In the classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. Following Sharpe's work on linear approximation to the mean-variance model, many attempts have been made to lineariz...