Market Efficiency in the Age of Big Data (with Stefan Nagel), November, 2019
Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N assets have cash flows that are a linear function of J firm characteristics, but with uncertain coefficients. Risk-neutral Bayesian investors impose shrinkage (ridge regression) or sparsity (Lasso) when they estimate the J coefficients of the model and use them to price assets. When J is comparable in size to N, returns appear cross-sectionally predictable using firm characteristics to an econometrician who analyzes data from the economy ex post. A factor zoo emerges even without p-hacking and data-mining. Standard in-sample tests of market efficiency reject the no-predictability null with high probability, despite the fact that investors optimally use the information available to them in real time. In contrast, out-of-sample tests retain their economic meaning.
Sentiment and Speculation in a Market with Heterogeneous Beliefs (with Dimitris Papadimitriou), September, 2019 Slides
We present a dynamic model featuring risk-averse investors with heterogeneous beliefs. Individual investors have stable beliefs and risk aversion, but agents who were correct in hindsight become relatively wealthy; their beliefs are overrepresented in market sentiment, so “the market” is bullish following good news and bearish following bad news. Extreme states are far more important for pricing than they would be in a homogeneous economy. Sentiment drives volatility up, and investors demand high risk premia in compensation. In a continuous-time Brownian limit, moderate investors supply liquidity: they trade against market sentiment in the hope of capturing a variance risk premium created by the presence of extremists. In a Poisson limit that features sudden arrivals of information, the price of insurance, which corresponds to a CDS rate, spikes up following bad news and declines during quiet times.
Volatility, Valuation Ratios, and Bubbles: An Empirical Measure of Market Sentiment (with Can Gao), August, 2019 Slides
We define a sentiment indicator based on option prices, valuation ratios and interest rates. The indicator can be interpreted as a lower bound on the expected growth in fundamentals that a rational investor would have to perceive in order to be happy to hold the market. The lower bound was unusually high in the late 1990s, reflecting dividend growth expectations that in our view were unreasonably optimistic. We show that our measure is a leading indicator of detrended volume, and of various other measures associated with financial fragility. Our approach depends on two key ingredients. First, we derive a new valuation-ratio decomposition that is related to the Campbell and Shiller (1988) loglinearization, but which resembles the Gordon growth model more closely and has certain other advantages. Second, we introduce a volatility index that provides a lower bound on the market’s expected log return.
On the Autocorrelation of the Stock Market, December, 2018
I introduce an index of market autocorrelation based on the prices of index options and of forward-start index options and implement it empirically at a six-month horizon. Forward-looking autocorrelation was close to zero before the subprime crisis but has been negative since late 2008, attaining a low of around −20% at the end of 2010 and remaining below −15% subsequently. I speculate that this may reflect market perceptions about the likely reaction, via quantitative easing, of policymakers to future market moves.
The Forward Premium Puzzle in a Two-Country World, March, 2013 NBER Working Paper 17564 Slides
I explore the behavior of asset prices and the exchange rate in a two-country world. When the large country has bad news, the relative price of the small country’s output declines. As a result, the small country’s bonds are risky, and uncovered interest parity fails, with positive excess returns available to investors who borrow at the large country’s interest rate and lend at the small country’s interest rate. I use a diagrammatic approach to derive these and other results in a calibration-free way.
How Much Do Financial Markets Matter? Cole–Obstfeld Revisited, November, 2010
Cole and Obstfeld (1991) asked, “How much do financial markets matter?” Emphasizing the importance of intratemporal relative price adjustment as a risk-sharing mechanism that operates even in the absence of financial asset trade, their answer was: not much. I revisit their question and find that in calibrations featuring rare disasters that generate reasonable risk premia without implausibly high risk aversion, the cost of shutting down trade in financial assets is on the order of 3 to 20 per cent of wealth.
Simple Variance Swaps, January, 2013 NBER Working Paper 16884 Note: this paper is largely subsumed by What is the Expected Return on the Market?
The events of 2008‒9 disrupted volatility derivatives markets and caused the single-name variance swap market to dry up completely; it has never recovered. This paper introduces the simple variance swap, a more robust relative of the variance swap that can be priced and hedged even if the underlying asset’s price can jump, and constructs SVIX, an index based on simple variance swaps that measures market volatility. SVIX is consistently lower than VIX in the time series, which rules out the possibility that the market return and stochastic discount factor are conditionally lognormal. The SVIX index points to an equity premium that—in contrast to the prevailing view in the literature—is extraordinarily volatile and that spiked dramatically at the height of the recent crisis.
What is the Expected Return on a Stock? (with Christian Wagner), Journal of Finance (2019), 74:4:1887‒1929 Slides The Wharton School‒WRDS Best Paper Award in Empirical Finance, WFA 2017 Honorable Mention, AQR Insight Award 2017
We derive a formula for the expected return on a stock in terms of the risk-neutral variance of the market and the stock’s excess risk-neutral variance relative to the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The theory performs well empirically both in and out of sample. Our results suggest that there is considerably more variation in expected returns, over time and across stocks, than has previously been acknowledged.
The Quanto Theory of Exchange Rates (with Lukas Kremens), American Economic Review (2019), 109:3:810‒843 Slides Best Paper Award, IF2017 Annual Conference in International Finance SIX Best Paper Award 2018
We present a new identity that relates expected exchange rate appreciation to a risk-neutral covariance term, and use it to motivate a currency forecasting variable based on the prices of quanto index contracts. We show via panel regressions that the quanto forecast variable is an economically and statistically significant predictor of currency appreciation and of excess returns on currency trades. Out of sample, the quanto variable outperforms predictions based on uncovered interest parity, on purchasing power parity, and on a random walk as a forecaster of differential (dollar-neutral) currency appreciation.
Notes on the Yield Curve (with Steve Ross), Journal of Financial Economics (2019), 134:689‒702
We study the properties of the yield curve under the assumptions that (i) the fixed-income market is complete and (ii) the state vector that drives interest rates follows a finite discrete-time Markov chain. We focus in particular on the relationship between the behavior of the long end of the yield curve and the recovered time discount factor and marginal utilities of a pseudo-representative agent; and on the relationship between the “trappedness” of an economy and the convergence of yields at the long end.
Options and the Gamma Knife, Journal of Derivatives (2018), 25:4:71‒79 Local version
This paper, a (very) slightly modified version of the one below, was solicited and republished by a sister journal of JPM.
Options and the Gamma Knife, Journal of Portfolio Management (2018), 44:6:47‒55 Local version
I survey work of Steve Ross (1976) and of Douglas Breeden and Robert Litzenberger (1978) that first showed how to use options to synthesize more complex securities. Their results made it possible to infer the risk-neutral measure associated with a traded asset, and underpinned the development of the VIX index. The other main result of Ross (1976), which shows how to infer joint risk-neutral distributions from option prices, has been much less influential. I explain why, and propose an alternative approach to the problem. This paper is dedicated to Steve Ross, and was written for a special issue of the Journal of Portfolio Management in memory of him.
What is the Expected Return on the Market?, Quarterly Journal of Economics (2017), 132:1:367‒433 Online Appendix
Data: Description of dataSVIX2.xlsepbound.xlscrashprob.xls Slides
I derive a lower bound on the equity premium in terms of a volatility index, SVIX, that can be calculated from index option prices. The bound implies that the equity premium is extremely volatile and that it rose above 20% at the height of the crisis in 2008. The time-series average of the lower bound is about 5%, suggesting that the bound may be approximately tight. I run predictive regressions and find that this hypothesis is not rejected by the data, so I use the SVIX index as a proxy for the equity premium and argue that the high equity premia available at times of stress largely reflect high expected returns over the very short run. I also provide a measure of the probability of a market crash, and introduce simple variance swaps, tradable contracts based on SVIX that are robust alternatives to variance swaps.
Averting Catastrophes: The Strange Economics of Scylla and Charybdis (with Robert Pindyck), American Economic Review (2015), 105:10:2947‒2985
Faced with numerous potential catastrophes—nuclear and bioterrorism, mega-viruses, climate change, and others—which should society attempt to avert? A policy to avert one catastrophe considered in isolation might be evaluated in cost-benefit terms. But because society faces multiple catastrophes, simple cost-benefit analysis fails: even if the benefit of averting each one exceeds the cost, we should not necessarily avert them all. We explore the policy interdependence of catastrophic events, and develop a rule for determining which catastrophes should be averted and which should not.
The Lucas Orchard, Econometrica (2013), 81:1:55‒111 Supplemental Material
This paper investigates the behavior of asset prices in an endowment economy in which a representative agent with power utility consumes the dividends of multiple assets. The assets are Lucas trees; a collection of Lucas trees is a Lucas orchard. The model generates return correlations that vary endogenously, spiking at times of disaster. Since disasters spread across assets, the model generates large risk premia even for assets with stable cashflows. Very small assets may comove endogenously and hence earn positive risk premia even if their cashflows are independent of the rest of the economy. I provide conditions under which the variation in a small asset’s price-dividend ratio can be attributed almost entirely to variation in its risk premium.
Consumption-Based Asset Pricing with Higher Cumulants, Review of Economic Studies (2013), 80:2:745‒773 Online Appendix
I extend the Epstein–Zin-lognormal consumption-based asset-pricing model to allow for general i.i.d. consumption growth. Information about the higher moments—equivalently, cumulants—of consumption growth is encoded in the cumulant-generating function. I use the framework to analyze economies with rare disasters, and argue that the importance of such disasters is a double-edged sword: parameters that govern the frequency and sizes of rare disasters are critically important for asset pricing, but extremely hard to calibrate. I show how to sidestep this issue by using observable asset prices to make inferences without having to estimate higher moments of the underlying consumption process. Extensions of the model allow consumption to diverge from dividends, and for non-i.i.d. consumption growth.
On the Valuation of Long-Dated Assets, Journal of Political Economy (2012), 120:2:346‒358 Online Appendix Reprinted in Christian Gollier ed. The Economics of Risk and Uncertainty, Edward Elgar: Cheltenham, UK, 2018
I show that the pricing of a broad class of long-dated assets is driven by the possibility of extraordinarily bad news. This result does not depend on any assumptions about the existence of disasters, nor does it only apply to assets that hedge bad outcomes; indeed, it even applies to long-dated claims on the market in a lognormal world if the market’s Sharpe ratio is higher than its volatility, as appears to be the case in practice.
Disasters Implied by Equity Index Options (with David Backus and Mikhail Chernov), Journal of Finance (2011), 66:6:1969‒2012
We use equity index options to quantify the distribution of consumption growth disasters. The challenge lies in connecting the risk-neutral distribution of equity returns implied by options to the true distribution of consumption growth estimated from macroeconomic data. We attack the problem from three perspectives. First, we compare pricing kernels constructed from macro-finance and option-pricing models. Second, we compare option prices derived from a macro-finance model to those we observe. Third, we compare the distribution of consumption growth derived from option prices using a macro-finance model to estimates based on macroeconomic data. All three perspectives suggest that options imply smaller probabilities of extreme outcomes than have been estimated from international macroeconomic data. The third comparison yields a viable alternative calibration of the distribution of consumption growth that matches the equity premium, option prices, and the sample moments of US consumption growth.
Disasters and the Welfare Cost of Uncertainty, American Economic Review (2008), Papers & Proceedings, 98:2:74‒78