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We study the economic value of market timing in foreign exchange (FX) markets, that is, using information about the conditional Sharpe ratio to adjust the notional value of a conditionally mean–variance efficient currency portfolio. Our strategy trades more (less) aggressively when the conditional risk-return trade-off is more (less) favorable. This leads to a significant improvement in the out-of-sample unconditional Sharpe ratio, skewness, and maximum drawdown per 1% expected excess return. The strategy's market timing predicts returns, volatility, and skewness in FX markets. Popular currency pricing factors do not explain the strategy's high average excess returns. Our findings suggest that it is costly to impose leverage or risk (i.e., conditional volatility) limits or other inferior market timing policies when constructing currency trading strategies. [ABSTRACT FROM AUTHOR]

The best constant re-balanced portfolio represents the standard estimator for the log-optimal portfolio. It is shown that a quadratic approximation of log-returns works very well on a daily basis and a mean-variance estimator is proposed as an alternative to the best constant re-balanced portfolio. It can easily be computed and the numerical algorithm is very fast even if the number of dimensions is high. Some small-sample and the basic large-sample properties of the estimators are derived. The asymptotic results can be used for constructing hypothesis tests and for computing confidence regions. For this purpose, one should apply a finite-sample correction, which substantially improves the large-sample approximation. However, it is shown that the impact of estimation errors concerning the expected asset returns is serious. The given results confirm a general rule, which has become folklore during the last decades, namely that portfolio optimization typically fails on estimating expected asset returns. [ABSTRACT FROM AUTHOR]

Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using MPC when estimates of future returns are updated every time a new observation becomes available, since the optimal control actions are reconsidered anyway. MPC outperforms a static decision rule for changing the allocation and realizes both a higher return and a significantly lower risk than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces the number of trades is found to increase the robustness of the approach. [ABSTRACT FROM AUTHOR]

Optimization of the electricity markets under modern portfolio theory has a crucial role for financial decision makers. Power suppliers in deregulated electricity markets need to optimize their generation capacities and bidding strategies so as to effectively participate in bilateral contract and spot markets. Market players have to deal with continuously changing electricity prices in competitive electricity market environment during their daily routine system operations. Electricity not like the others is a unique product/service and cannot be stored economically, however it should be generated and consumed simultaneously. In addition to all, power suppliers face with fuel price, water regime, delivery, and network risks. In view of the scene described above, prudent decision making methodologies are of critical importance to maximize profit while minimizing managing risks. This paper presents a comprehensive comparison of mean-variance, down-side, and semi-variance methods for optimization in electricity markets and the corresponding methodologies to maximize the return while minimizing risk. Real Turkish day-ahead market data set between December 2009 and December 2012 is used in numerical calculations. Generation cost data of Hydraulic plants, lignite coal fired thermal power plants, and natural gas combined cycle power plants are taken into consideration in the course of optimization evaluations. In the present of real data, these methods can also be applied to renewable energy generation types. These three methods were able to be applied to all case scenarios effectively and produced efficient frontiers, optimal/minimal portfolios, and utility functions successfully. The results have revealed that the methods significantly provide decisions for power suppliers with different risk aversion levels, and for various instruments to maximize the profit while minimizing the associated market risks, and to meet generation obligations. Consequently, financial optimization under Lower Partial Moments constraints would give notable results in analyzing the efficient frontiers for electricity markets in Turkey. [ABSTRACT FROM AUTHOR]

Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed, and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance, the ‘resampled efficiency’ of Michaud [1998.Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation.Boston: Harvard Business School Press]. We compare the out-of-sample performance of traditional mean–variance optimization by Markowitz [1952. “Portfolio Selection.”Journal of Finance7 (1): 77–91] with Michaud's resampled efficiency in a comprehensive simulation study for a large number of relevant estimators appearing in the literature. In addition, we perform an empirical study to confirm the simulation results. Within the framework of the analyses we consider different estimation periods as well as unconstrained and constrained portfolio optimization problems. The main findings are that Markowitz outperforms Michaud on average but the impact of different estimators and constraints is significantly larger. Precisely, in most situations, the estimator of Frost and Savarino [1988. “For Better Performance: Constrain Portfolio Weights.”Journal of Portfolio Management15 (1): 29–34] proves to work excellent. However, if the variance of estimators is large, for example, for short observation periods or large samples, it is recommendable to additionally implement constraints or to use the estimator of Ledoit and Wolf [2003. “Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection.”Journal of Empirical Finance10 (5): 603–622]. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of asset allocation in a mean-variance framework. The theoretical model of portfolio optimization is specified and then applied to a long panel data set from historic to most recent times, March 1990 - March 2013. The paper contributes in three ways. First, an alternative asset return model is proposed that combines the historical returns, capital asset pricing model (CAPM) and returns estimated based on firm fundamentals. These return estimates enter the optimization problem. The second contribution is the application of an improved covariance matrix estimator that has superior properties compared to the typical sample covariance estimator. Third, the paper proposes two investments strategies. The first proposition suggests always choosing the maximized Sharpe ratio portfolio and the second one, the portfolio with the highest information ratio. The nature of both strategies is designed for investors with different appetites for risk. The performance of these choices is analyzed in light of four types of constraints: upper/lower investment limits, group constraints and transaction costs. The one-period optimal investment portfolio is rebalanced at quarterly intervals. Both strategies are benchmarked against an alternative investment choice such as holding the S&P 500 index, or investing in a risk-free asset such as a bond. Portfolio analysis and backtesting reveal that the strategies are superior to simply holding an equally weighted portfolio, a risk-free asset or the S&P 500 index. [ABSTRACT FROM AUTHOR]