Optimal portfolio liquidation with distress risk
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2010-11-01
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We analyze the problem of an investor who needs to unwind a portfolio in the face of recurring and uncertain liquidity needs, with a model that accounts for both permanent and temporary price impact of trading. We first show that a risk-neutral investor who myopically deleverages his position to meet an immediate need for cash always prefers to sell more liquid assets. If the investor faces the possibility of a downstream shock, however, the solution differs in several important ways. If the ensuing shock is sufficiently large, the nonmyopic investor unwinds positions more than immediately necessary and, all else being equal, prefers to retain more of the assets with low temporary price impact in order to hedge against possible distress. More generally, optimal liquidation involves selling strictly more of the assets with a lower ratio of permanent to temporary impact, even if these assets are relatively illiquid. The results suggest that properly accounting for the possibility of future shocks should play a role in managing large portfolios. © 2010 INFORMS.
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Brown, DB, BI Carlin and MS Lobo (2010). Optimal portfolio liquidation with distress risk. Management Science, 56(11). pp. 1997–2014. 10.1287/mnsc.1100.1235 Retrieved from https://hdl.handle.net/10161/4429.
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David B. Brown
David B. Brown is the Snow Family Business Professor in Decision Sciences and the Faculty Director for the Center for Energy, Development and Global Environment (EDGE) at Duke University's Fuqua School of Business.
Professor Brown's research focuses on designing and analyzing algorithms for decision problems involving uncertainty and complex tradeoffs. This work is methodological in nature and cuts across various application areas. Part of the GRACE project funded by the US Department of Energy's Advanced Research Projects Agency, Professor Brown is actively working with researchers at Duke and several other institutions on improving the efficiency and reliability of electricity grid operations in the face of uncertainty in renewable energy sources.
His recent research also includes developing and analyzing solution techniques for problems such as network revenue management, dynamic pricing in shared vehicle systems, stochastic scheduling problems, and sequential search problems. Professor Brown's research has appeared in publications such as Management Science and Operations Research, and the Institute for Operations Research and the Management Sciences (INFORMS) has recognized his research with several awards. At Fuqua, he has taught Decision Models, Data Analytics and Applications, Probability and Statistics, and Convex Optimization, and he has won teaching awards in multiple programs.
Professor Brown received a Bachelor's and Master's of Science in Electrical Engineering from Stanford University and has been on the faculty at Fuqua since receiving his Ph.D. in Electrical Engineering and Computer Science from MIT.
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