QP application. The advantage of Monte Carlo methods is that they may be extended to risk functions that are more … classical Gaussian limitations. The optimization of quadratic risk-return functions, VaR, CVaR, may be handled in a similar … risk preferences are optimized with differing multivariate distributions. Good comparisons with established results in Mean …

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model …

tests of performance suffer from the look-ahead benchmark bias, when they use the assets constituting the benchmarks of …-ahead benchmark bias can exhibit a surprisingly large amplitude for portfolios of common stocks (up to 8% annum for the Samp;P500 … running top 500 US capitalizations to demonstrate that this bias can account for a gross overestimation of performance metrics …

overcome the risk of not receiving an optimal solution to the portfolio optimization (suboptimal outcomes of attribution of … empirical distribution or a theoretical distribution (mitigating estimation risk). All computational results are reported for … theory. Research implications/limitations - The research emphasized that in order to get a more diversified investment …

properties as well such as extremely large bets for short term favorable investment situations because the Arrow-Pratt risk …

portfolio, by balancing the portfolio's expected return against the portfolio's volatility risk and its leverage risk …

by actions of the investor. Using the classical filtering theory, we reduce this problem with partial information to one … with full information and solve it for logarithmic and power utility functions. In particular, we apply control theory for …