Hedge fund portfolio selection with modified expected shortfall

Modified Value-at-Risk (VaR) and Expected Shortfall (ES) are recently introduced downside risk estimators based on the Cornish-Fisher expansion for assets such as hedge funds whose returns are non-normally distributed. Modified VaR has been widely implemented as a portfolio selection criterion. We are the first to investigate hedge fund portfolio selection using modified ES as optimality criterion. We show that for the EDHEC hedge fund style indices, the optimal portfolios based on modified ES outperform out-of-sample the EDHEC Fund of Funds index and have better risk characteristics than the equal-weighted and Fund of Funds portfolios.portfolio optimization, modified expected shortfall, non-normal returns

Why Do Firms Offer Risky Defined Benefit Pension Plans?

Even risky pension sponsors could offer essentially riskless pension promises by contributing a sufficient level of resources to their pension trust funds and by investing those resources in fixed-income securities designed to deliver their payoffs just as pension obligations are coming due. However, almost no firm has chosen to fund its plan in this manner. We study the optimal funding choice for plan sponsors by developing a simple model of pension financing in which the total compensation offered to workers must clear the labor market. We find that if workers understand the implications of pension risk, they will demand greater compensation for riskier pension promises than for safer ones, all else equal. Indeed, in our model, pension sponsors maximize their value by making their pension promises free of risk. We close by positing some explanations for why no real-world firm follows the prescription of our model.

Why Do Firms Offer Risky Defined Benefit Pension Plans?

Even risky pension sponsors could offer essentially riskless pension promises by contributing a sufficient level of resources to their pension trust funds and by investing those resources in fixed-income securities designed to deliver their payoffs just as pension obligations are coming due. However, almost no firm has chosen to fund its plan in this manner. We study the optimal funding choice for plan sponsors by developing a simple model of pension financing in which the total compensation offered to workers must clear the labor market. We find that if workers understand the implications of pension risk, they will demand greater compensation for riskier pension promises than for safer ones, all else equal. Indeed, in our model, pension sponsors maximize their value by making their pension promises free of risk. We close by positing some explanations for why no real-world firm follows the prescription of our model.

Filtered Extreme Value Theory for Value-At-Risk Estimation

Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets where high volatility and nonlinear behaviors in returns are observed. The Extreme Value Theory (EVT) with conditional quantile proposed by McNeil and Frey (2000) is based on the central limit theorem applied to the extremes rater than mean of the return distribution. It limits the distribution of extreme returns always has the same form without relying on the distribution of the parent variable. This paper uses 8 filtered EVT models created with conditional quantile to estimate value-at-risk for the Istanbul Stock Exchange (ISE). The performances of the filtered expected shortfall models are compared to those of GARCH, GARCH with student-t distribution, GARCH with skewed student-t distribution and FIGARCH by using alternative back-testing algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), RMSE (70 days) h-step ahead forecasting RMSE (70 days), number of exception and h-step ahead number of exception. The test results show that the filtered expected shortfall has better performance on capturing fat-tails in the stock returns than parametric value-at-risk models do. Besides increase in conditional quantile decreases h-step ahead number of exceptions and this shows that filtered expected shortfall with higher conditional quantile such as 40 days should be used for forward looking forecasting.Value at-Risk; Filtered Expected shortfall; Extreme value theory; emerging markets