Stefano Giglio

Professor of Finance, Yale School of Management

NBER Faculty Research Fellow

CEPR Research Affiliate

[Download CV]

[Official page]

Working Papers

The Collateral Rule: An Empirical Analysis of the CDS Market, with Agostino Capponi, Allen Cheng and Richard Haynes, November 2017

We study the empirical determinants of collateral requirements in the cleared credit default swap (CDS) market: how margins depend on portfolio risks and market conditions, and what the implications are for theoretical models of collateral equilibrium. We construct a novel data set containing CDS portfolios and margins posted by all participants to the main CDS clearinghouse, ICE Clear Credit, covering 60% of the U.S. market. We provide direct empirical evidence that margins are much more conservatively set than what a Value-at-Risk (VaR) rule would imply, and are unequally implemented across participants. We show that more extreme tail risk measures have a higher explanatory power for observed collateral requirements than VaR, consistent with endogenous collateral theories such as Fostel and Geanakoplos (2015) where extreme events dominate in determining collateral. The dependence of collateral requirements on extreme tail risks induces potential nonlinearities in margin spirals, dampening small shocks and amplifying large ones. We also confirm empirically the main channel through which collateral-feedback effects operate in many theoretical models of equilibriums with financial frictions, such as Brunnermeier and Pedersen (2009), highlighting the prominent role of aggregate volatility and funding costs.

How do investors perceive the risks from macroeconomic and financial uncertainty? Evidence from 19 option markets, with Ian Dew-Becker and Bryan Kelly, October 2017

This paper studies the pricing of shocks to implied and realized volatility using options in 19 different markets, covering financials, metals, energies, and agricultural products. The markets are directly related to the state of the macroeconomy and financial markets, and investors can use the options to separately hedge shocks to real uncertainty and to the realization of volatility. Historically, realized volatility has earned a robustly negative risk premium, indicating that high macroeconomic volatility is associated with high marginal utility. However, models are driven by forward-looking conditional variances, which can be proxied by implied volatility. Over the same period, the cost of hedging shocks to implied volatility in commodity markets has been negative: portfolios with returns that are positively correlated with shocks to implied volatility have earned positive average returns. That result is inconsistent with the view that periods of high uncertainty, as measured by forward-looking implied volatility, are “bad” states of the world with high marginal utility. The result is, however, potentially consistent with models in which uncertainty is high in periods of high innovation.

Taming the Factor Zoo, with Guanhao Feng and Dacheng Xiu, August 2017

The asset pricing literature has produced hundreds of potential risk factors. Organizing this “zoo of factors” and distinguishing between useful, useless, and redundant factors require econometric techniques that can deal with the curse of dimensionality. We propose a model-selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high-dimensional set of existing factors explains. Our methodology explicitly accounts for potential model-selection mistakes, unlike the standard approaches that assume perfect variable selection, which rarely occurs in finite sample and produces a bias due to the omitted variables. We apply our procedure to a set of factors recently discovered in the literature. We show that several factors – such as profitability and investments – have statistically significant explanatory power beyond the hundreds of factors proposed in the past. In addition, we show that our risk price estimates and their significance are stable, whereas the model selected by simple LASSO is not.

Inference on Risk Premia in the Presence of Omitted Factors, with Dacheng Xiu, May 2017

Best Paper Prize at the 2017 European Financial Association conference

[MATLAB code] [AFA Slides]

We propose a three-pass method to estimate the risk premia of observable factors in a linear asset pricing model, which is valid even when the observed factors are just a subset of the true factors that drive asset prices or they are measured with error. We show that the risk premium of a factor can be identified in a linear factor model regardless of the rotation of the other control factors as long as they together span the space of true factors. Motivated by this rotation invariance result, our approach uses principal components to recover the factor space and combines the estimated principal components with each observed factor to obtain a consistent estimate of its risk premium. Our methodology also accounts for potential measurement error in the observed factors and detects when such factors are spurious or even useless. The methodology exploits the blessings of dimensionality, and we therefore apply it to a large panel of equity portfolios to estimate risk premia for several workhorse linear models. The estimates are robust to the choice of test portfolios within equities as well as across many asset classes.

Uncertainty Shocks as Second-Moment News Shocks, with David Berger and Ian Dew-Becker, January 2017

(Replaces: "Contractionary Volatility or Volatile Contractions"?)

[Option-implied volatility data]

This paper provides new empirical evidence on the relationship between aggregate uncertainty and the macroeconomy. We identify uncertainty shocks using methods from the literature on news shocks, following the observation that second-moment news is a shock to uncertainty. According to a wide range of VAR specifications, shocks to uncertainty have no significant effect on the economy, even though shocks to realized stock market volatility are contractionary. In other words, realized volatility, rather than uncertainty about the future, is associated with contractions. Furthermore, investors have historically paid large premia to hedge shocks to realized volatility, but the premia associated with shocks to uncertainty have not been statistically different from zero. We argue that these facts are consistent with the predictions of a simple model in which aggregate technology shocks are negatively skewed. So volatility matters, but it is the realization of volatility, rather than uncertainty about the future, that seems to be associated with declines.

Climate Change and Long-Run Discount Rates: Evidence from Real Estate, with Matteo Maggiori, Johannes Stroebel, and Andreas Weber, July 2015

Revise and Resubmit at the Journal of Political Economy

The optimal investment to mitigate climate change crucially depends on the discount rate used to evaluate the investment's uncertain future benefits. The appropriate discount rate is a function of the horizon over which these benefits accrue and the riskiness of the investment. In this paper, we estimate the term structure of discount rates for an important risky asset class, real estate, up to the very long horizons relevant for investments in climate change abatement. We show that this term structure is steeply downward-sloping, reaching 2.6% at horizons beyond 100 years. We explore the implications of these new data within both a general asset pricing framework that decomposes risks and returns by horizon and a structural model calibrated to match a variety of asset classes. Our analysis demonstrates that applying average rates of return that are observed for traded assets to investments in climate change abatement is misleading. We also show that the discount rates for investments in climate change abatement that reduce aggregate risk, as in disaster-risk models, are bounded above by our estimated term structure for risky housing, and should be below 2.6% for long-run benefits. This upper bound rules out many discount rates suggested in the literature and used by policymakers. Our framework also distinguishes between the various mechanisms the environmental literature has proposed for generating downward-sloping discount rates.

Credit Default Swap Spreads and Systemic Financial Risk, April 2014

[Online Appendix]

Runner-up, Ieke van den Burg Prize for Research on Systemic Risk 2015

This paper measures the joint default risk of financial institutions by exploiting information about counterparty risk in credit default swaps (CDS). A CDS contract written by a bank to insure against the default of another bank is exposed to the risk that both banks default. From CDS spreads we can then learn about the joint default risk of pairs of banks. From bond prices we can learn the individual default probabilities. Since knowing individual and pairwise probabilities is not sufficient to fully characterize multiple default risk, I derive the tightest bounds on the probability that many banks fail simultaneously.


11. Excess Volatility: Beyond Discount Rates, with Bryan Kelly

Quarterly Journal of Economics, forthcoming

Finalist, AQR Insight Award, 2016

Napa Conference Best Paper Award, 2016

10. An Intertemporal CAPM with Stochastic Volatility, with John Campbell, Christopher Polk and Bob Turley

Journal of Financial Economics, forthcoming

[Online Appendix]

9. The Price of Variance Risk, with Ian Dew-Becker, Anh Le and Marius Rodriguez

Journal of Financial Economics (2017), 123(2): 225-250

JFE Lead article

[Online Appendix]

8. Asset Pricing in the Frequency Domain: Theory and Empirics, with Ian Dew-Becker

Review of Financial Studies (2016), 29(8): 2029-2068

[Online Appendix] [Coverage: VoxEU]

7. No-Bubble Condition: Model-Free Tests in Housing Markets, with Matteo Maggiori and Johannes Stroebel

Econometrica (2016), 84(3): 1047-1091

[Online Appendix] [Coverage: VoxEU]

6. Systemic Risk and the Macroeconomy: An Empirical Evaluation, with Bryan Kelly and Seth Pruitt

Journal of Financial Economics (2016), 119(3): 457-471

Fama-DFA Prize for the Best Paper Published in the Journal of Financial Economics (Asset Pricing), 2016

JFE Lead article

Q-Group Roger F. Murray Prize (3rd prize), 2015

[Online Appendix] [Data (US and International Measures)] [Coverage:; VoxEU; BFI]

5. Very Long-Run Discount Rates, with Matteo Maggiori and Johannes Stroebel

Quarterly Journal of Economics (2015), 130(1): 1-53

QJE Lead Article

QJE Editor's Choice article

Jacob Gold & Associates Best Paper Prize, ASU Sonoran Winter Finance Conference, 2014

NYU Glucksman Institute Faculty Research Prize for the Best Paper in Finance, 2015

[Online Appendix] [Rents Worksheet] [Data Summary] [NBER WP Version]

[Coverage: Forbes ; Economist, May 2014 ; National Review ; NBER Digest ; VoxEU; Economist, April 2015]

4. No News is News: Do Markets Underreact to Nothing?, with Kelly Shue

Review of Financial Studies, (2014), 27(12): 3389-3440

RFS Lead Article

RFS Editor's Choice article

Winner of the UBS Global Asset Management Award for Research in Investments, FRA Meeting 2012

[Coverage on Capital Ideas]

3. Hard Times, with John Campbell and Christopher Polk

Review of Asset Pricing Studies (2013), 3(1): 95-132

[Online Appendix]

2. Intangible Capital, Relative Asset Shortages, and Bubbles, with Tiago Severo

Journal of Monetary Economics (2012), 59: 303-317

[Online Appendix]

1. Forced Sales and House Prices, with John Campbell and Parag Pathak

American Economic Review (2011), 101(5): 2108–31

[Online Appendix] [Coverage at SeekingAlpha]