This book provides a comprehensive treatment of all the steps of asset allocation: detecting the market invariants; estimating the invariants' distribution; modeling the market at any horizon; defining optimality; accounting for estimation- and model-risk; including the practitioner's experience...

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,...

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...

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,...

We measure the contributions to risk of a set of factors, strategies, or investments, based on "Minimum-Torsion Bets", namely a set of uncorrelated factors, optimized to closely track the factors used to allocate the portfolio. We then introduce a novel definition of contributions to risk, which...

The Entropy Pooling approach is a versatile theoretical framework to process market views and generalized stress-tests into an optimal "posterior" market distribution, which is then used for risk management and portfolio management. Entropy Pooling can be implemented non-parametrically or...

When estimating risk from a window of historical observations, the confidence interval is inverse to the number of scenarios used, which is the length of the window. When estimating risk with exponential decay, where the relative weight of each scenario decreases with time, the confidence...

We show how to mix machine learning signals such as kernel smoothing and fuzzy memberships via the Entropy Pooling approach by Meucci (2008). We illustrate a case study, where we overlay an exponentially time-decayed prior to a pseudo-Gaussian kernel that emphasizes market scenarios where...