In this paper, we study intertemporal portfolio choice when an investor accounts explicitly for model misspecification. We develop a framework that allows for ambiguity about not just the joint distribution of returns for all stocks in the portfolio, but also for different levels of ambiguity for the marginal distribution of returns for any subset of these stocks. We find that when the overall ambiguity about the joint distribution of returns is high, then small differences in ambiguity for the marginal return distribution will result in a portfolio that is significantly underdiversified relative to the standard mean‐variance portfolio.

Returns on international equities are characterized by jumps; moreover, these jumps tend to occur at the same time across countries leading to systemic risk . We capture these stylized facts using a multivariate system of jump‐diffusion processes where the arrival of jumps is simultaneous across assets. We then determine an investor's optimal portfolio for this model of returns. Systemic risk has two effects: One, it reduces the gains from diversification and two, it penalizes investors for holding levered positions. We find that the loss resulting from diminished diversification is small, while that from holding very highly levered positions is large.

Global warming is the defining challenge of our times. An emerging literature is investigating the interactions between climate change and financial markets. Climate finance is studying the pricing of climate risks across asset classes and the ways to channel public and private capital towards climate mitigation and adaptation investments. In 2019, the Journal of Corporate Finance launched a call for papers on the finance of climate change with the goal of publishing a special volume. In this context, we discuss how finance can help to address climate challenges, the contribution of the papers selected for the Special Issue and what we view as a broader climate finance research program.

Our objective is to identify the trading strategy that would allow an investor to take advantage of “excessive” stock price volatility and “sentiment” fluctuations. We construct a general equilibrium “difference‐of‐opinion” model of sentiment in which there are two classes of agents, one of which is overconfident about a public signal, while still optimizing intertemporally. Overconfident investors overreact to the signal and introduce an additional risk factor causing stock prices to be excessively volatile. Consequently, rational investors choose a conservative portfolio; moreover, this portfolio depends not just on the current price divergence but also on their prediction about future sentiment and the speed of price convergence.

Moreira and Muir question the existence of a strong risk‐return trade‐off by showing that investors can improve performance by reducing exposure to risk factors when their volatility is high. However, Cederburg et al. show that these strategies fail out‐of‐sample, and Barroso and Detzel show they do not survive transaction costs. We propose a conditional multifactor portfolio that outperforms its unconditional counterpart even out‐of‐sample and net of costs. Moreover, we show that factor risk prices generally decrease with market volatility. Our results demonstrate that the breakdown of the risk‐return trade‐off is more puzzling than previously thought.

With transaction costs for trading goods, the nominal exchange rate moves within a band around the nominal purchasing power parity (PPP) value. We model the behavior of the band and of the exchange rate within the band. The model explains why there are below‐unity slope coefficients in regression tests of PPP, and why these increase toward unity under hyperinflation or with low‐frequency data. Our results are independent of the presence of nontraded goods in the economy.

We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1-N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1-N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

We develop a model for an investor with multiple priors and aversion to ambiguity. We characterize the multiple priors by a "confidence interval" around the estimated expected returns and we model ambiguity aversion via a minimization over the priors. Our model has several attractive features: (1) it has a solid axiomatic foundation; (2) it is flexible enough to allow for different degrees of uncertainty about expected returns for various subsets of assets and also about the return-generating model; and (3) it delivers closed-form expressions for the optimal portfolio. Our empirical analysis suggests that, compared with portfolios from classical and Bayesian models, ambiguity-averse portfolios are more stable over time and deliver a higher out-of sample Sharpe ratio.

We study whether investors can exploit serial dependence in stock returns to improve out-of-sample portfolio performance. We show that a vector-autoregressive (VAR) model captures stock return serial dependence in a statistically significant manner. Analytically, we demonstrate that, unlike contrarian and momentum portfolios, an arbitrage portfolio based on the VAR model attains positive expected returns regardless of the sign of asset return cross-covariances and autocovariances. Empirically, we show, however, that both the arbitrage and mean-variance portfolios based on the VAR model outperform the traditional unconditional portfolios only for transaction costs below ten basis points.

Our objective in this paper is to examine whether one can use option-implied information to improve the selection of mean-variance portfolios with a large number of stocks, and to document which aspects of option-implied information are most useful to improve their out-of-sample performance. Portfolio performance is measured in terms of volatility, Sharpe ratio, and turnover. Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility. Using option-implied correlation does not improve any of the metrics. Using option-implied volatility, risk premium, and skewness to adjust expected returns leads to a substantial improvement in the Sharpe ratio, even after prohibiting short sales and accounting for transaction costs.