Pdf Distributionally Robust Optimization With Wasserstein Metric For Multi Period Portfolio

(PDF) Distributionally Robust Optimization With Wasserstein Metric For Multi-period Portfolio ...

(PDF) Distributionally Robust Optimization With Wasserstein Metric For Multi-period Portfolio ... Using the w asserstein metric to characterize the uncertainty of returns in eac h perio d, a new distributionally robust mean variance model is proposed to solve multi period portfolio. We revisit markowitz's mean variance portfolio selection model by considering a dis tributionally robust version, where the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the wasserstein distance.

Robust Portfolio Optimization.pdf - Robust Portfolio Optimization Prof. Daniel P. Palomar ...

Robust Portfolio Optimization.pdf - Robust Portfolio Optimization Prof. Daniel P. Palomar ... Wasserstein distributionally robust optimization seeks data driven decisions that perform well under the most adverse distribution within a certain wasserstein distance from a nominal distribution constructed from the training samples. In this paper we study distributionally robust optimization problems with a wasserstein ambiguity set centered at the uniform distribution bpn on n independent and identically distributed training samples. Distributionally robust optimisation (dro), and risk estimation with wasserstein distances jan obl ́oj mathematical institute university of oxford joint works with daniel bartl, samuel drapeau, johannes wiesel. In particular, this dissertation considers optimal decision making for portfolio problems in counterparty credit risk, funding risk, statistical arbitrage, option exercise, asset purchasing/selling, and quantification of certain profit and risk metrics.

(PDF) Multi-Period Portfolio Optimization Under Cash-In Strategies

(PDF) Multi-Period Portfolio Optimization Under Cash-In Strategies Distributionally robust optimisation (dro), and risk estimation with wasserstein distances jan obl ́oj mathematical institute university of oxford joint works with daniel bartl, samuel drapeau, johannes wiesel. In particular, this dissertation considers optimal decision making for portfolio problems in counterparty credit risk, funding risk, statistical arbitrage, option exercise, asset purchasing/selling, and quantification of certain profit and risk metrics. Using the wasserstein metric to characterize the uncertainty of returns in each period, a new distributionally robust mean variance model is proposed to solve multi period portfolio selection problem. we further transform the developed model into a tractable convex problem using duality theory. It is demonstrated that the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs—in many interesting cases even as tractable linear programs. we consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. using the wasserstein metric, we construct a. Data driven distributionally robust optimization using the wasserstein metric daniel kuhn risk analytics and optimization chair École polytechnique fédérale de lausanne rao.epfl.ch erick’s notes. Hu and hong (2013) studied distributional robust optimization problems in a research where the uncertainty set of the probability distribution is defined by k l divergence, but this research discussed distributional robust portfolio optimization based on the kalmar ratio with the wasserstein metric.

(PDF) Regularization For Wasserstein Distributionally Robust Optimization

(PDF) Regularization For Wasserstein Distributionally Robust Optimization Using the wasserstein metric to characterize the uncertainty of returns in each period, a new distributionally robust mean variance model is proposed to solve multi period portfolio selection problem. we further transform the developed model into a tractable convex problem using duality theory. It is demonstrated that the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs—in many interesting cases even as tractable linear programs. we consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. using the wasserstein metric, we construct a. Data driven distributionally robust optimization using the wasserstein metric daniel kuhn risk analytics and optimization chair École polytechnique fédérale de lausanne rao.epfl.ch erick’s notes. Hu and hong (2013) studied distributional robust optimization problems in a research where the uncertainty set of the probability distribution is defined by k l divergence, but this research discussed distributional robust portfolio optimization based on the kalmar ratio with the wasserstein metric.

(PDF) Distributionally Robust Chance-Constrained Voltage-Concerned DC-OPF With Wasserstein Metric

(PDF) Distributionally Robust Chance-Constrained Voltage-Concerned DC-OPF With Wasserstein Metric Data driven distributionally robust optimization using the wasserstein metric daniel kuhn risk analytics and optimization chair École polytechnique fédérale de lausanne rao.epfl.ch erick’s notes. Hu and hong (2013) studied distributional robust optimization problems in a research where the uncertainty set of the probability distribution is defined by k l divergence, but this research discussed distributional robust portfolio optimization based on the kalmar ratio with the wasserstein metric.

Part 2: Wasserstein Metric