We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem ...but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that our framework nests as special cases the shrinkage approaches of Jagannathan and Ma (Jagannathan, R., T. Ma. 2003. Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance 58 1651–1684) and Ledoit and Wolf (Ledoit, O., M. Wolf. 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finance 10 603–621, and Ledoit, O., M. Wolf. 2004. A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 365–411) and the 1/ N portfolio studied in DeMiguel et al. (DeMiguel, V., L. Garlappi, R. Uppal. 2009. Optimal versus naive diversification: How inefficient is the 1/ N portfolio strategy? Rev. Financial Stud. 22 1915–1953). We also use our framework to propose several new portfolio strategies. For the proposed portfolios, we provide a moment-shrinkage interpretation and a Bayesian interpretation where the investor has a prior belief on portfolio weights rather than on moments of asset returns. Finally, we compare empirically the out-of-sample performance of the new portfolios we propose to 10 strategies in the literature across five data sets. We find that the norm-constrained portfolios often have a higher Sharpe ratio than the portfolio strategies in Jagannathan and Ma (2003), Ledoit and Wolf (2003, 2004), the 1/ N portfolio, and other strategies in the literature, such as factor portfolios.
We develop a model of portfolio choice to nest the views of Keynes, who advocates concentration in a few familiar assets, and Markowitz, who advocates diversification. We use the concepts of ...ambiguity and ambiguity aversion to formalize the idea of an investor's "familiarity" toward assets. The model shows that for any given level of expected returns, the optimal portfolio depends on two quantities: relative ambiguity across assets and the standard deviation of the expected return estimate for each asset. If both quantities are low, then the optimal portfolio consists of a mix of familiar and unfamiliar assets; moreover, an increase in correlation between assets causes an investor to increase concentration in familiar assets (flight to familiarity). Alternatively, if both quantities are high, then the optimal portfolio contains only the familiar asset(s), as Keynes would have advocated. In the extreme case in which both quantities are very high, no risky asset is held (nonparticipation).
This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.
In a production economy with trade in financial markets motivated by the desire to share labor-income risk and to speculate, we show that speculation increases volatility of asset returns and ...investment growth, increases the equity risk premium, and reduces welfare. Regulatory measures, such as constraints on stock positions, borrowing constraints, and the Tobin tax have similar effects on financial and macroeconomic variables. However, borrowing constraints and the Tobin tax are more successful than constraints on stock positions at improving welfare because they substantially reduce speculative trading without impairing excessively risk-sharing trades.
•Speculation increases asset-return and real-investment volatility, reducing welfare.•Short-sale and borrowing constraints and Tobin tax used to mitigate these effects.•All three regulatory measures have similar effects on financial and macro variables.•But, the borrowing constraint is most effective at improving welfare.•This is because it reduces speculation without substantially impairing risk-sharing.
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.
We investigate how transaction costs change the number of characteristics that are jointly significant for an investor’s optimal portfolio and, hence, how they change the dimension of the ...cross-section of stock returns. We find that transaction costs increase the number of significant characteristics from 6 to 15. The explanation is that, as we show theoretically and empirically, combining characteristics reduces transaction costs because the trades in the underlying stocks required to rebalance different characteristics often cancel out. Thus, transaction costs provide an economic rationale for considering a larger number of characteristics than that in prominent asset-pricing models.
The finance of climate change Calvet, Laurent; Gianfrate, Gianfranco; Uppal, Raman
Journal of corporate finance (Amsterdam, Netherlands),
April 2022, 2022-04-00, Volume:
73
Journal Article
Peer reviewed
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.
Households with familiarity biases tilt their portfolios toward a few risky assets. The resulting mean-variance loss from portfolio underdiversification is equivalent to only a modest reduction of ...about 1 percent per year in a household’s portfolio return. However, once we consider also the effect of familiarity biases on the asset-allocation and intertemporal consumption-savings decisions, the welfare loss is multiplied by a factor of four. In general equilibrium, the suboptimal decisions of households distort also aggregate growth, amplifying further the overall social welfare loss. Our findings demonstrate that financial markets are not a mere sideshow to the real economy and that improving the financial decisions of households can lead to large benefits, not just for individual households, but also for society.
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.
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