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Chaire analyse de risque et optimisation

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Publications associées (104)

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Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Zhi Chen, Wolfram Wiesemann

We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed ...

2018

Robust Multidimensional Pricing: Separation without Regret

Daniel Kuhn, Napat Rujeerapaiboon, Cagil Kocyigit

We study a robust monopoly pricing problem with a minimax regret objective, where a seller endeavors to sell multiple goods to a single buyer, only knowing that the buyer's values for the goods range over a rectangular uncertainty set. We interpret this pr ...

2018

On Risk Reduction in Kelly Betting Using the Conservative Expected Value

Daniel Kuhn, Napat Rujeerapaiboon

The celebrated Kelly betting strategy guarantees, with probability one, higher long-run wealth than any other causal investment strategy. However, on the way to its long-term supremacy, this strategy has a notable downfall: it typically displays high varia ...

2018

Non-myopic betas

Semyon Malamud

An overlapping generations model with investors having heterogeneous investment horizons leads to a two-factor asset pricing model. The risk premiums are determined by the exposure to the market (myopic betas) and the future return on the efficient portfol ...

2018

Robust Portfolio Optimization

Zheng Wei Yap

Since the 2008 Global Financial Crisis, the financial market has become more unpredictable than ever before, and it seems set to remain so in the forseeable future. This means an investor faces unprecedented risks, hence the increasing need for robust port ...

EPFL2017

MATHICSE Technical Report : Multilevel Monte Carlo approximation of functions

Fabio Nobile, Sebastian Krumscheid

Many applications across sciences and technologies require a careful quantification of non-deterministic effects to a system output, for example when evaluating the system's reliability or when gearing it towards more robust operation conditions. At the he ...

MATHICSE2017

Model Uncertainty And Scenario Aggregation

Damir Filipovic, Mathieu Jacques David Cambou

This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the defi ...

Wiley-Blackwell2017

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

Multi-Period Portfolio Optimization: Translation of Autocorrelation Risk to Excess Variance

Napat Rujeerapaiboon, Byung-Geun Choi

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees hav ...

2016

Robust Growth-Optimal Portfolios

Daniel Kuhn, Napat Rujeerapaiboon, Wolfram Wiesemann

The growth-optimal portfolio is designed to have maximum expected log-return over the next rebalancing period. Thus, it can be computed with relative ease by solving a static optimization problem. The growth-optimal portfolio has sparked fascination among ...

Informs2016