We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

Following Levy and Roll [2010], we posit that the market portfolio is the efficient tangent Markowitz portfolio, i.e., it is mean-variance efficient. We then reverse engineer the expected returns and variance terms with constraints imposed by empirical data on a hierarchy of asset baskets. This...

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in...

With the advances in time-series prediction, several recent developments in machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose a novel approach to portfolio formation strategy based on a hybrid machine...

This paper examines the effectiveness of using futures contracts as hedging instruments of: (1) alternative models of volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of futures contracts. For this purpose, daily data of...

The purpose of this study is to incorporate some of the influential findings in the forecasting literature in an integrated framework to examine whether a real-time optimizing investor can benefit from the stock market by allocating assets based on a predictive model that only uses industry...

The paper examines the performance of four multivariate volatility models, namely CCC, VARMA-GARCH, DCC and BEKK, for the crude oil spot and futures returns of two major benchmark international crude oil markets, Brent and WTI, to calculate optimal portfolio weights and optimal hedge ratios, and...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...