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Spanning tests for Markowitz stochastic dominance

Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas - 2018 - Current draft: January 2018

Using properties of the cdf of a random variable defined as a saddle-type point of a real valued continuous stochastic process, we derive first-order asymptotic properties of tests for stochastic spanning w.r.t. a stochastic dominance relation. First, we define the concept of Markowitz...

Persistent link: https://www.econbiz.de/10011877232

Ethanol Hedging Strategies Using Dynamic Multivariate GARCH

Garcia, Sergio - 2016

established that the gasoline supply in the United States (U.S.) must contain 10% ethanol. This work seeks to identify hedging … ratios using dynamic multivariate GARCH to best identify hedging opportunities in a newly developed futures market. The … ability for firms to hedge and regulators to supervise the ethanol futures market is crucial to both hedging potential losses …

Persistent link: https://www.econbiz.de/10012979327

Currency Hedging Strategies Using Dynamic Multivariate GARCH

Chang, Chia-Lin - 2012

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 …, Euro, British pound and Japanese yen, against the American dollar, are used to analyze hedge ratios and hedging …

Persistent link: https://www.econbiz.de/10013113663

Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH

Tansuchat, Roengchai - 2010

The paper examines the performance of four multivariate volatility models, namely CCC, VARMA-GARCH, DCC and BEKK, for … the optimal portfolio weights of all multivariate volatility models for Brent suggest holding futures in larger … volatility model give the time-varying hedge ratios, and recommend to short in crude oil futures with a high proportion of one …

Persistent link: https://www.econbiz.de/10013149486

Spanning analysis of stock market anomalies under prospect stochastic dominance

Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas - 2020 - This version: April 2020

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and...

Persistent link: https://www.econbiz.de/10012219063

Sparse Parametric Portfolio Selection

Croessmann, Roman - 2018

This article shows how sparse solutions can be generated in parametric portfolio selection methods. Sparse mean-variance optimization procedures can be applied after the translation of parametric weight estimates into implied mean return estimates. The results of our empirical analysis suggest...

Persistent link: https://www.econbiz.de/10012915299

Norm constrained empirical portfolio optimization with stochastic dominance : robust optimization non-asymptotics

Arvanitis, Stelios - 2025

The present note provides an initial theoretical explanation of the way norm regularizations may provide a means of controlling the non-asymptotic probability of False Dominance classification for empirically optimal portfolios satisfying empirical Stochastic Dominance restrictions in an iid...

Persistent link: https://www.econbiz.de/10015194229

Deep parametric portfolio policies

Simon, Frederik; Weibels, Sebastian; Zimmermann, Tom - 2023 - This version: February 2025

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP)...

Persistent link: https://www.econbiz.de/10015329382

Enhanced Portfolio Optimization

Pedersen, Lasse Heje - 2020

Portfolio optimization should provide large benefits to investors, but standard mean-variance optimization (MVO) works so poorly in practice that optimization is often abandoned. The approaches developed to address this issue are often surrounded by mystique regarding how, why, and whether they...

Persistent link: https://www.econbiz.de/10012842510

Deep Parametric Portfolio Policies

Simon, Frederik; Weibels, Sebastian; Zimmermann, Tom - 2022

We generalize the parametric portfolio policy framework to learning portfolio weights via deep neural networks. We find that network-based portfolio policies result in an increase of investor utility of between 30 and 100 percent over a comparable linear portfolio policy, depending on whether...

Persistent link: https://www.econbiz.de/10013404767