We develop an integrated simulation and optimization framework for multicurrency asset allocation problems. The simulation applies principal component analysis to generate scenarios depicting the discrete joint distributions of uncertain asset returns and exchange rates. We then develop and implement models that optimize the conditional-value-at-risk (CVaR) metric. The scenario-based optimizati...

According to Markowitz (1952) portfolio theory assumed that the investor has a concave utility function that expresses an attitude of risk aversion and managed to put portfolio selection based on two criteria, mean and variance. Other studies have improved this approach and following Basel II recommendations by using Value-at-Risk (VaR) as a standard risk measure in finance, Alexander & Baptist...

When decisions are based on empirical observations, a trade-off arises between flexibility of the decision and ability to generalize new situations. In this paper, we focus that obtained by minimization Conditional Value-at-Risk (CVaR) argue in CVaR generalization can be understood ground theoretical results under very general assumptions system generates observations. The have implications top...

Risk management in IT projects still is more an art than a science. Reliable figures about the risks of a project portfolio still depend on intuition and experience of project managers. A central challenge is to aggregate the risks of a project into a single risk measure that makes it easy for the senior management to compare projects and see which projects need their attention. We first analyz...

Conditional Value at Risk (CVaR) has been recently used to approximate a chance constraint. In this paper, we study the convergence of stationary points when sample average approximation (SAA) method is applied to a CVaR approximated joint chance constrained stochastic minimization problem. Specifically, we prove, under some moderate conditions, that optimal solutions and stationary points obta...

This paper introduces the notions of stability, ultimate boundedness, and positive invariance for stochastic systems in view risk. More specifically, those are defined terms worst-case Conditional Value-at-Risk (CVaR), which quantifies conditional expectation losses exceeding a certain threshold over set possible uncertainties. Those allow us to focus our attention on tail behavior analysis dyn...

Investors use different approaches to select optimal portfolio. so, Optimal investment choices according to return can be interpreted in different models. The traditional approach to allocate portfolio selection called a mean - variance explains. Another approach is Markov chain. Markov chain is a random process without memory. This means that the conditional probability distribution of the nex...

In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion. Conditional value-at-risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of the well-known variance-related risk measures, and because of its computational efficiencies has gained popularity in ...

This study investigates the properties of risk measure, value at (VaR) and conditional VaR (CVaR), using high-frequency Bitcoin data. These data allow us to conduct a high statistical analysis. Our findings reveal disparity in CVaR values between left right tails return probability distributions. We refer this as “long–short asymmetry”. In domain, tail distribution can be accurately described b...

We propose a scenario based optimization framework for solving the cash flow matching problem where the time horizon of the liabilities is longer than the maturities of available bonds and the interest rates are uncertain. Standard interest rate models can be used for scenarios generation within this framework. The optimal portfolio is found by minimizing the cost at a specific level of shortfa...