Invited Session Thu.1.H 3027

Thursday, 10:30 - 12:00 h, Room: H 3027

Cluster 7: Finance & economics [...]

Risk management in financial markets


Chair: Nikos Trichakis and Dan Andrei Iancu



Thursday, 10:30 - 10:55 h, Room: H 3027, Talk 1

Gerry Tsoukalas
Dynamic portfolio execution

Coauthors: Kay Giesecke, Jiang Wang


We analyze the problem of dynamic portfolio execution for a portfolio manager facing adverse market impact and correlated assets. We focus on the market microstructure and show that supply/demand information,contained in the assets' limit order books, can be utilized to improve execution efficiency.
Adopting a partial-equilibrium framework, we show that the multivariate problem requires an extended liquidity model which cannot be efficiently solved via the usual
dynamic programming methods. We provide an equivalent static reformulation of the problem that is solvable in polynomial time. We find that a strategic manager can take advantage of asset cross-elasticities to mitigate
adverse market impact and significantly reduce risk-adjusted execution costs. We also introduce and analyze an important trade-off that arises in heterogeneous portfolios, between the manager's need to minimize costs, and his desire to remain well-diversified throughout the horizon. We develop a simple risk management tool which gives managers dynamic control over this trade-off.



Thursday, 11:00 - 11:25 h, Room: H 3027, Talk 2

Zachary Glen Feinstein
Set-valued dynamic risk measures

Coauthor: Birgit Rudloff


Set-valued risk measures appear naturally when markets with transaction costs are considered and capital requirements can be made in a basket of currencies or assets. We discuss the definition for such functions and the financial interpretation. Results for primal and dual representations of set-valued dynamic risk measures are deduced. Definitions of different time consistency properties in the set-valued framework are given. It is shown that in the set-valued case the recursive form for multivariate risk measures as well as an additive property for the acceptance sets is equivalent to a stronger time consistency property called multi-portfolio time consistency. As an example we consider the superhedging problem in markets with proportional transaction costs.



Thursday, 11:30 - 11:55 h, Room: H 3027, Talk 3

Vishal Gupta
A data-driven approach to risk preferences

Coauthor: Dimitris Bertsimas


Accurately specifying risk preferences is critical to financial applications; yet, risk preferences are not directly observable. Typical industry practice asks investors to self-describe as "conservative'' or "risky''. In this work we take a data-driven perspective. Using ideas from inverse optimization, we construct risk measures that are consistent with an investor's historical portfolio holdings. When applied to a single investor's portfolio, our technique recovers a coherent risk measure approximately describing her behavior. This risk measure can then be used to inform subsequent reallocation or to cluster similar investors. When applied to the market portfolio, our approach provides an alternative derivation of the popular Black-Litterman estimator. Unlike the original Bayesian derivation, our approach requires no probabilistic assumptions, and generalizes beyond the mean-variance paradigm. Indeed, we propose "BL''-type estimators in environments characterized by volatility uncertainty. Computational experience suggests portfolios built from these estimators offer a better risk-reward tradeoff than their traditional counterparts.


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