We revisit the question whether commodities should be included in investors' portfolios. We employ for the first time a stochastic dominance efficiency (SDE) approach to construct optimal portfolios with and without commodities and we evaluate their comparative performance. SDE circumvents the...

We introduce Dynamic Entropy Pooling, a quantitative technique to perform dynamic portfolio construction with discretionary, non-synchronous views. With Dynamic Entropy Pooling, the portfolio manager can embed in the allocation process signals with life spans ranging from minutes to years,...

Modelling and forecasting of asset volatility and covariance is of prime importance in the construction of portfolios. In this paper, we present a generalised multi-factor model that incorporates heteroskedasticity and dependence in the idiosyncratic error terms. We apply this model to...

We introduce a simple approach to managing portfolio interest rate risk that is consistent and performs well across different interest rate regimes, including when interest rates are low or even negative. Inspired by Black (1995), this approach uses a novel inverse-call transformation...

We study the out-of-sample and post-publication return-predictability of 97 variables that academic studies show to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data...

Exercises and case studies for a rigorous approach to risk- and portfolio-management. This booklet stems from the review sessions of the six-day ARPM bootcamp.Contents include:Advanced multivariate statistics; copula-marginal decompositionAnnualization/projection (FFT, cumulants,...

With the exception of naive methods for portfolio selection, such as the equal weighted approaches, all other methods of portfolio allocation are more or less sensitive to the quality of the inputs considered in constructing the models and risk measures utilised in the allocation framework. The...

We provide a model-free framework for studying the dynamics of the state vector and its risk prices. Specifically, we derive a frequency domain decomposition of the unconditional asset return premium in a general setting with a log-affine stochastic discount factor (SDF). Importantly, we show...

To study the characteristics-sorted factor model in asset pricing, we develop a bottom-up approach with state-of-the-art deep learning optimization. With an economic objective to minimize pricing errors, we train a non-reduced-form neural network using firm characteristics [inputs], and generate...

We analyze the economic benefits of several covariance estimation approaches on a tactical asset-allocation problem in the presence of high-frequency return data. Our analysis confirms that the use of robust-to-noise and asynchronicity estimators not only gives statistically more accurate...