Among professionals and academics alike, it is well known that active portfolio management is unable to provide additional risk-adjusted returns relative to their benchmarks. For this reason, passive wealth management has emerged in recent decades to offer returns close to benchmarks at a lower...

In this work, we propose an ARMA(1,1)-GARCH(1,1) model with standard classical tempered stable (CTS) innovations for historical daily returns of 29 selected stocks. The non-Gaussian nature of the innovations captures the fat-tail property observed in data. The dependency between different assets...

This paper proposes a novel methodology to construct optimal portfolios that incorporates the occurrence of systemic events. Investors maximize a modified Sharpe ratio conditional on a systemic event. We solve the portfolio allocation problem analytically under the absence of short-selling...

At its core, portfolio and risk management is about gathering and processing market-related data in order to make effective investment decisions. To this end, risk and return statistics are estimated from relevant financial data and used as inputs within the investment process. It is this...

Background and target:The optimization of single assets or a portfolio of assets is a ubiquitous task in energy and commodity trading. Assets may be of various types, such as storage facilities (e.g. batteries or water reservoirs for power, heat or gas storage), decentralized or large power...

We introduce a novel measure to study the relationship between portfolio concentration in vertically related firms and the performance of actively managed U.S. mutual funds. We find a strong positive relationship between our supply chain concentration measure and fund performance. While funds'...

This paper applies specific quantitative methods to demonstrate a general theoretical model for measuring strategic performance. The theoretical concepts are universal and measurable for all types of strategic activity by applying the methodology through alternative quantitative analytical...

In typical robust portfolio selection problems, one mainly finds portfolios with the worst-case return under a given uncertainty set, in which asset returns can be realized. A too large uncertainty set will lead to a too conservative robust portfolio. However, if the given uncertainty set is not...

While it is often argued that allocation decisions can be best expressed in terms of exposure to rewarded risk factors, as opposed to somewhat arbitrary asset class decompositions, the practical implications of this paradigm shift for the optimal design of the policy portfolio still remain...

We study the distributionally robust stable tail adjusted return ratio (DRSTARR) portfolio optimization problem, in which the objective is to maximize the STARR performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties, named uncertain...