Using information in returns we identify the stochastic process of consumption. We find that aggregate consumption reacts over multiple quarters to innovations spanned by financial markets, and this persistent component accounts for over a quarter of consumption variation. These shocks are cross-sectionally priced, drive most of the time series variation in stocks, and a small, yet significant, share of volatility of bonds. Nevertheless, we find no support for stochastic volatility of consumption driving time-varying risk premia. Finally, an otherwise standard recursive utility model based on our estimated process explains both equity premium and risk-free rate puzzles with low risk aversion.
Previously circulated as “Consumption Risk of Bonds and Stocks” and “Consumption” Winner of Oustanding Paper Award in Investment, Midwest Finance Association, 2016
We propose a novel approach to measure the cost of aggregate economic fluctuations, that does not require complete specification of investors’ risk preferences or their beliefs. With data on consumption and asset prices, an information-theoretic method is used to recover an information kernel (I-SDF). The I-SDF accurately prices broad cross-sections of assets, thereby offering a reliable candidate for the measurement of the welfare cost of business cycles. Our method enables the estimation of both the unconditional (or, average) cost of fluctuations as well as the cost conditional on each possible economic state. We find that the cost of fluctuations is strongly time-varying and countercyclical and that the cost of business cycle fluctuations is substantial, accounting for a quarter to a third of the cost of all consumption uncertainty.
We show that a non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, delivers smaller out-of-sample pricing errors and a better cross-sectional fit than leading multi factor models. The information SDF (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (20%-37%) and Sharpe ratio (1.1). The I-SDF extracted from a wide cross-section of equity portfolios is highly positively skewed and leptokurtic, and implies that about a third of the observed risk premia represent a compensation for 2.5% tail events. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.
We generalise a stochastic version of the workhorse SIR (Susceptible-Infectious- Removed) epidemiological model to account for spatial dynamics generated by network interactions. Using the London metropolitan area as a salient case study, we show that commuter network externalities account for about 42% of the propagation of COVID-19. We find that the UK lockdown measure reduced total propagation by 44%, with more than one third of the effect coming from the reduction in network externalities. Counterfactual analyses suggest that: i) the lockdown was somehow late, but further delay would have had more extreme consequences; ii) a targeted lockdown of a small number of highly connected geographic regions would have been equally effective, arguably with significantly lower economic costs; iii) targeted lockdowns based on threshold number of cases are not effective, since they fail to account for network externalities.
We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high dimensional problems. For a (potentially misspecified) standalone model, it provides reliable price of risk estimates for both tradable and non-tradable factors, and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification – if a dominant one exists – or provides a Bayesian model averaging (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors, and find that the BMA-SDF outperforms existing models in- and out-of-sample
BayesianFactorZoo R CRAN package (user-friendly R package that implements all the methods in the paper, with extensive documentation and teaching examples) (extended) Presentation Slides An early video presentation by Svetlana Bryzgalova Full replication codes (including usage examples and posterior draws of the published version, 4.56GB) BMA-SDF (time series of BMA-SDF posterior mean for different SR values, based on factors of Table 3, csv format)
Using a structural model, we estimate the liquidity multiplier of an interbank network and banks’ contributions to systemic risk. To provide payment services, banks hold reserves. Their equilibrium holdings can be strategic complements or substitutes. The former arises when payment velocity is high and payments begets payments. The latter prevails when the opportunity cost of liquidity is large, incentivising banks to borrow neighbors’ reserves instead of holding their own. Consequently, the network can amplify or dampen individual shocks. Empirically, network topology explains cross-sectional heterogeneity in banks’ contribution to systemic risks while changes in the equilibrium type drive the time-series variation.
We consider asset pricing models in which the SDF can be factorized into an observable component and a potentially unobservable one. Using a relative entropy minimization approach, we nonparametrically estimate the SDF and its components. Empirically, we find that the SDF has a business-cycle pattern and significant correlations with market crashes and the Fama-French factors. Moreover, we derive novel bounds for the SDF that are tighter and have higher information content than existing ones. We show that commonly used consumption-based SDFs correlate poorly with the estimated one, require high risk aversion to satisfy the bounds and understate market crash risk.
We study the implications of human capital hedging for international portfolio choice. First, we document that, at the household level, the degree of home country bias in equity holdings is increasing in the labor income to financial wealth ratio. Second, we show that a heterogeneous agent model in which households face short selling constraints and labor income risk, calibrated to match both micro and macro labor income and asset returns data, can both rationalize this finding and generate a large aggregate home country bias in portfolio holdings. Third, the empirical evidence supporting the belief that the human capital hedging motive should skew domestic portfolios toward foreign assets, is driven by an econometric misspecification rejected by the data.
Note: This paper is based upon, and replaces, two companion papers: “The International Diversification Puzzle is not Worse Than You Think” and “Human Capital and International Portfolio Choice.”
Probably not. First, allowing the probabilities of the states of the economy to differ from their sample frequencies, the Consumption-CAPM is still rejected in both U.S. and international data. Second, the recorded world disasters are too small to rationalize the puzzle unless one assumes that disasters occur every 6-10 years. Third, if the data were generated by the rare events distribution needed to rationalize the equity premium puzzle, the puzzle itself would be unlikely to arise. Fourth, the rare events hypothesis, by reducing the cross-sectional dispersion of consumption risk, worsens the ability of the Consumption-CAPM to explain the cross-section of returns.
A reduction in inflation can fuel run-ups in housing prices if people suffer from money illusion. For example, investors who decide whether to rent or buy a house by simply comparing monthly rent and mortgage payments do not take into account the fact that inflation lowers future real mortgage costs. We decompose the price-rent ratio into a rational component — meant to capture the “proxy effect” and risk premia — and an implied mispricing. We find that inflation and nominal interest rates explain a large share of the time-series variation of the mispricing, and that the tilt effect is very unlikely to rationalize this finding.
This paper evaluates the central insight of the consumption capital asset pricing model that an asset’s expected return is determined by its equilibrium risk to consumption. Rather than measure risk by the contemporaneous covariance of an asset’s return and consumption growth, we measure risk by the covariance of an asset’s return and consumption growth cumulated over many quarters following the return. While contemporaneous consumption risk explains little of the variation in average returns across the 25 Fama-French portfolios, our measure of ultimate consumption risk at a horizon of three years explains a large fraction of this variation.
Most households’ portfolios are extremely close to the efficient frontier once we explicitly take into account no short-selling constraints, while the null hypothesis of efficiency is rejected for all portfolios if we don’t consider these constraints.
