We propose a quantile-based ranking and selection (R&S) procedure for comparing a finite set of stochastic systems via simulation. Our R&S procedure uses a quantile set of the simulated probability distribution of a performance characteristic of interest that best represents the most appropriate selection criterion as the basis for comparison. Since this quantile set may represent either the do...

In this paper, we carry out the numerical study of a fuzzy portfolio selection model where the objective is to minimize the downside risk and the rates of returns on securities are approximated by means of LR-fuzzy numbers of trapezoidal form. Data from 96 securities over 195 month are used to compare the selected portfolios with a simple utility function and with the outof-sample data as well ...

This paper examines the intertemporal relation between downside risk and expected stock returns. Value at Risk (VaR), expected shortfall, and tail risk are used as measures of downside risk to determine the existence and significance of a risk-return tradeoff. We find a positive and significant relation between downside risk and the portfolio returns on NYSE/AMEX/Nasdaq stocks. VaR remains a su...

Current research suggests that the large downside risk in hedge fund returns disqualifies the variance as an appropriate risk measure. For example, one can easily construct portfolios with nonlinear pay-offs that have both a high Sharpe ratio and a high downside risk. This paper examines the consequences of shortfall-based risk measures in the context of portfolio optimization. In contrast to p...

Previous research has shown that the returns on individual properties and listed property securities are skewed (Lizieri and Ward 2001, Young and Graff 1995 and Liu et al. 1992). This claim is investigated in the context of listed UK property companies and US REITs. In particular, the shape of the conditional distribution of total monthly returns is examined for a group of 20 UK companies and 2...

The risk premium in the US stock market has fallen far below its historic level, which Shiller (2000) attributes to a bubble driven by psychological factors. As an alternative explanation, we point out that the observed risk premium may be reduced by one-sided intervention policy on the part of the Federal Reserve which leads investors into the erroneous belief that they are insured against dow...

We investigate why spreads on corporate bonds are so much larger than expected losses from default. Systematic factors make very little contribution to spreads, even if higher moments or downside effects are taken into account. Instead we find that sizes of spreads are strongly related to idiosyncratic-risk factors: not only to idiosyncratic equity volatility, but even more to idiosyncratic bon...

When the risk premium in the US stock market fell far below its historic level, Shiller (2000) attributed this to a bubble driven by psychological factors. As an alternative explanation, we point out that the observed risk premium may be reduced by one-sided intervention policy on the part of the Federal Reserve, which leads investors into the erroneous belief that they are insured against down...

Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we ...