Optimizing Optimization: The Next Generation of Optimization Applications and Theory (Quantitative Finance)

144 Optimizing Optimization requirements for what they call a coherent risk measure. VaR and volatility are known to violate the ...

Staying ahead on downside risk 145 In addition, several dynamic EVaR models have been developed in the recent econometric litera ...

146 Optimizing Optimization In other words, we minimize the weighted squared deviations from the obser- vations. Weights are asy ...

Staying ahead on downside risk 147 exist portfolios that would be chosen by all nonsatiable risk averse agents over the mean – v ...

148 Optimizing Optimization De Rossi and Harvey (2009) assume that a time series y t , a risk tolerance parameter 0 ω 1, and a s ...

Staying ahead on downside risk 149 Equivalently , the problem can be cast in a nonparametric framework. The goal is to find the ...

150 Optimizing Optimization Both Ω and D are T  T matrices. Starting with an initial guess, μ (1) , the optimal μ is found by i ...

Staying ahead on downside risk 151 straightforward to extend the analysis to the case in which we target a preas- signed portfol ...

152 Optimizing Optimization estimated expectile at the end of the sample period, μˆT. From the previous discussion, we know that ...

Staying ahead on downside risk 153 In particular, this class of utility functions introduces loss aversion. Suppose ω  0.1. Whe ...

154 Optimizing Optimization estimate the covariance matrix. Table 6.2 displays the estimated volatilities and correlations. The ...

Staying ahead on downside risk 155 that, in the limit as ω approaches 0.5, the dynamic model simplifies to a lin- ear state spac ...

156 Optimizing Optimization Table 6.4 further compares the mean – variance and the mean – EVaR portfo- lios. By using the covari ...

Staying ahead on downside risk 157 approach tends to underweight the asset compared to mean – variance. Once again, the main dif ...

158 Optimizing Optimization more severe extreme losses in the final part of the sample period. The results of the Monte Carlo an ...

Staying ahead on downside risk 159 when the same signal is used. An important question is whether it is possible to limit the do ...

160 Optimizing Optimization Patton , A. J. ( 2004 ). On the out-of-sample importance of skewness and asymmetric dependence for a ...

© 2009 Elsevier Limited All rights reserved. This chapter will appear simultaneously in: Sortino, The Sortino Framework for Cons ...

162 Optimizing Optimization may or may not be reasonable (see end notes). Early assumptions were that distributions were bell sh ...

Optimization and portfolio selection 163 estimate of the shape of the joint distribution than assuming a bell shape (stand- ard ...