Uppal began his academic career in 1988 as an assistant professor at the University of British Columbia. He held this position until 1992,when he was appointed the B.I. Ghert Family Foundation Junior Professor,a role he held until 1995. He then served as an associate professor at the same university until 2000. That year,he joined the London Business School as an associate professor,later becoming Professor of Finance from 2002 to 2011. Since 2011,he has been Professor of Finance at EDHEC Business School in London.[1]
Uppal has also held various administrative and professional appointments throughout his career. Between 2002 and 2005,he was the co-director of the Financial Economics Programme of CEPR,and between 2011 and 2014,he was the Director of the American Finance Association. Moreover,since 2023,he has been a Consultant for the Bank of England.[1]
Research
In his early research,Uppal developed optimization models to minimize the cost of replicating derivative payoffs under transaction costs and trading constraints,showing that limited,non-continuous rebalancing was optimal when accounting for market frictions.[3] He also examined how transaction costs created a band around the PPP-implied exchange rate,explaining below-unity PPP regression slopes that moved toward unity during hyperinflation or over longer time horizons,independent of nontraded goods.[4] In his investigation of how investors made intertemporal portfolio choices under model uncertainty,his work showed that greater ambiguity about return distributions led to significantly under-diversified portfolios compared to standard mean-variance portfolio predictions.[5]
Uppal also studied how simultaneous global jumps in international equity returns reduced diversification benefits only slightly,using a multivariate jump-diffusion model to determine the investor's optimal portfolio under systemic risk.[6] He developed a robust portfolio optimization model incorporating ambiguity aversion through multiple priors around expected returns,providing closed-form solutions and showing empirically that ambiguity-averse portfolios were more stable and achieved higher out-of-sample Sharpe ratios.[7] He also proposed a unified portfolio optimization framework that constrained portfolio weights to reduce estimation error,encompassing and extending prior shrinkage methods,and demonstrated superior out-of-sample performance with higher Sharpe ratios across multiple datasets.[8]
Through his research,Uppal compared various mean-variance portfolio optimization models with the naïve 1/N strategy and found that estimation errors eliminate the theoretical advantages of optimization,making the equal-weighted approach perform just as well in out-of-sample tests across different datasets and metrics.[9] His later research has examined how transaction costs affect portfolio optimization,showing that such costs increased the number of significant stock characteristics by reducing overlapping trading needs,thereby expanding the effective dimensionality of asset-pricing models.[10]
Selected articles
Uppal,R.,&Wang,T. (2003). Model misspecification and underdiversification. The journal of finance,58(6),2465–2486.
Das,S. R.,&Uppal,R. (2004). Systemic risk and international portfolio choice. The Journal of Finance,59(6),2809–2834.
Garlappi,L.,Uppal,R.,&Wang,T. (2007). Portfolio selection with parameter and model uncertainty:A multi-prior approach. The Review of Financial Studies,20(1),41–81.
DeMiguel,V.,Garlappi,L.,&Uppal,R. (2009). Optimal versus naive diversification:How inefficient is the 1/N portfolio strategy?. The review of Financial studies,22(5),1915–1953.
DeMiguel,V.,Garlappi,L.,Nogales,F. J.,&Uppal,R. (2009). A generalized approach to portfolio optimization:Improving performance by constraining portfolio norms. Management science,55(5),798–812.
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