The Journal of Finance

Can Time‐Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?

Published: 5/20/2013,  Volume: 68,  Issue: 3  |  DOI: 10.1111/jofi.12018  |  Cited by: 838

JESSICA A. WACHTER

Why is the equity premium so high, and why are stocks so volatile? Why are stock returns in excess of government bill rates predictable? This paper proposes an answer to these questions based on a time‐varying probability of a consumption disaster. In the model, aggregate consumption follows a normal distribution with low volatility most of the time, but with some probability of a consumption realization far out in the left tail. The possibility of this poor outcome substantially increases the equity premium, while time‐variation in the probability of this outcome drives high stock market volatility and excess return predictability.

Discussion

Published: 8/2001,  Volume: 56,  Issue: 4  |  DOI: 10.1111/0022-1082.00370  |  Cited by: 0

Jessica A. Wachter

Why Is Long‐Horizon Equity Less Risky? A Duration‐Based Explanation of the Value Premium

Published: 1/11/2007,  Volume: 62,  Issue: 1  |  DOI: 10.1111/j.1540-6261.2007.01201.x  |  Cited by: 330

MARTIN LETTAU, JESSICA A. WACHTER

We propose a dynamic risk‐based model that captures the value premium. Firms are modeled as long‐lived assets distinguished by the timing of cash flows. The stochastic discount factor is specified so that shocks to aggregate dividends are priced, but shocks to the discount rate are not. The model implies that growth firms covary more with the discount rate than do value firms, which covary more with cash flows. When calibrated to explain aggregate stock market behavior, the model accounts for the observed value premium, the high Sharpe ratios on value firms, and the poor performance of the CAPM.

Does the Failure of the Expectations Hypothesis Matter for Long‐Term Investors?

Published: 2/2005,  Volume: 60,  Issue: 1  |  DOI: 10.1111/j.1540-6261.2005.00728.x  |  Cited by: 151

ANTONIOS SANGVINATSOS, JESSICA A. WACHTER

We solve the portfolio problem of a long‐run investor when the term structure is Gaussian and when the investor has access to nominal bonds and stock. We apply our method to a three‐factor model that captures the failure of the expectations hypothesis. We extend this model to account for time‐varying expected inflation, and estimate the model with both inflation and term structure data. The estimates imply that the bond portfolio of a long‐run investor looks very different from the portfolio of a mean‐variance optimizer. In particular, time‐varying term premia generate large hedging demands for long‐term bonds.

Do Rare Events Explain CDX Tranche Spreads?

Published: 10/2018,  Volume: 73,  Issue: 5  |  DOI: 10.1111/jofi.12705  |  Cited by: 86

SANG BYUNG SEO, JESSICA A. WACHTER

We investigate whether a model with time‐varying probability of economic disaster can explain prices of collateralized debt obligations. We focus on senior tranches of the CDX, an index of credit default swaps on investment grade firms. These assets do not incur losses until a large fraction of previously stable firms default, and thus are deep out‐of‐the money put options on the overall economy. When calibrated to consumption data and to the equity premium, the model explains the spreads on CDX tranches prior to and during the 2008 to 2009 crisis.

Should Investors Avoid All Actively Managed Mutual Funds? A Study in Bayesian Performance Evaluation

Published: 2/2001,  Volume: 56,  Issue: 1  |  DOI: 10.1111/0022-1082.00319  |  Cited by: 186

Klaas P. Baks, Andrew Metrick, Jessica Wachter

This paper analyzes mutual‐fund performance from an investor's perspective. We study the portfolio‐choice problem for a mean‐variance investor choosing among a risk‐free asset, index funds, and actively managed mutual funds. To solve this problem, we employ a Bayesian method of performance evaluation; a key innovation in our approach is the development of a flexible set of prior beliefs about managerial skill. We then apply our methodology to a sample of 1,437 mutual funds. We find that some extremely skeptical prior beliefs nevertheless lead to economically significant allocations to active managers.

Is the United States a Lucky Survivor? A Hierarchical Bayesian Approach

Published: 4/16/2025,  Volume: 80,  Issue: 4  |  DOI: 10.1111/jofi.13452  |  Cited by: 4

JULES VAN BINSBERGEN, SOPHIA HUA, JONAS PEETERS, JESSICA WACHTER

Using international data, we quantify the magnitude of survivorship bias in U.S. equity market performance, finding that it explains about one‐third of the equity risk premium in the past century. We model the subjective crash belief of an investor who infers the crash risk in the United States by cross‐learning from other countries. The U.S. crash probability shows a persistent and widening divergence from the implied global average. We attribute the upward bias in the measured equity premium to crashes that did not occur in‐sample and to shocks to valuations resulting from learning about the probability.

DISCUSSION

Published: 5/1983,  Volume: 38,  Issue: 2  |  DOI: 10.1111/j.1540-6261.1983.tb02266.x  |  Cited by: 0

SUSAN M. WACHTER

The International Bank Lending Channel of Monetary Policy Rates and QE: Credit Supply, Reach‐for‐Yield, and Real Effects

Published: 1/25/2019,  Volume: 74,  Issue: 1  |  DOI: 10.1111/jofi.12735  |  Cited by: 212

BERNARDO MORAIS, JOSÉ‐LUIS PEYDRÓ, JESSICA ROLDÁN‐PEÑA, CLAUDIA RUIZ‐ORTEGA

We identify the international credit channel by exploiting Mexican supervisory data sets and foreign monetary policy shocks in a country with a large presence of European and U.S. banks. A softening of foreign monetary policy expands credit supply of foreign banks (e.g., U.K. policy affects credit supply in Mexico via U.K. banks), inducing strong firm‐level real effects. Results support an international risk‐taking channel and spillovers of core countries’ monetary policies to emerging markets, both in the foreign monetary softening part (with higher credit and liquidity risk‐taking by foreign banks) and in the tightening part (with negative local firm‐level real effects).

Nonstandard Errors

Published: 4/17/2024,  Volume: 79,  Issue: 3  |  DOI: 10.1111/jofi.13337  |  Cited by: 107

ALBERT J. MENKVELD, ANNA DREBER, FELIX HOLZMEISTER, JUERGEN HUBER, MAGNUS JOHANNESSON, MICHAEL KIRCHLER, SEBASTIAN NEUSÜß, MICHAEL RAZEN, UTZ WEITZEL, DAVID ABAD‐DÍAZ, MENACHEM (MENI) ABUDY, TOBIAS ADRIAN, YACINE AIT‐SAHALIA, OLIVIER AKMANSOY, JAMIE T. ALCOCK, VITALI ALEXEEV, ARASH ALOOSH, LIVIA AMATO, DIEGO AMAYA, JAMES J. ANGEL, ALEJANDRO T. AVETIKIAN, AMADEUS BACH, EDWIN BAIDOO, GAETAN BAKALLI, LI BAO, ANDREA BARBON, OKSANA BASHCHENKO, PARAMPREET C. BINDRA, GEIR H. BJØNNES, JEFFREY R. BLACK, BERNARD S. BLACK, DIMITAR BOGOEV, SANTIAGO BOHORQUEZ CORREA, OLEG BONDARENKO, CHARLES S. BOS, CIRIL BOSCH‐ROSA, ELIE BOURI, CHRISTIAN BROWNLEES, ANNA CALAMIA, VIET NGA CAO, GUNTHER CAPELLE‐BLANCARD, LAURA M. CAPERA ROMERO, MASSIMILIANO CAPORIN, ALLEN CARRION, TOLGA CASKURLU, BIDISHA CHAKRABARTY, JIAN CHEN, MIKHAIL CHERNOV, WILLIAM CHEUNG, LUDWIG B. CHINCARINI, TARUN CHORDIA, SHEUNG‐CHI CHOW, BENJAMIN CLAPHAM, JEAN‐EDOUARD COLLIARD, CAROLE COMERTON‐FORDE, EDWARD CURRAN, THONG DAO, WALE DARE, RYAN J. DAVIES, RICCARDO DE BLASIS, GIANLUCA F. DE NARD, FANY DECLERCK, OLEG DEEV, HANS DEGRYSE, SOLOMON Y. DEKU, CHRISTOPHE DESAGRE, MATHIJS A. VAN DIJK, CHUKWUMA DIM, THOMAS DIMPFL, YUN JIANG DONG, PHILIP A. DRUMMOND, TOM DUDDA, TEODOR DUEVSKI, ARIADNA DUMITRESCU, TEODOR DYAKOV, ANNE HAUBO DYHRBERG, MICHAŁ DZIELIŃSKI, ASLI EKSI, IZIDIN EL KALAK, SASKIA TER ELLEN, NICOLAS EUGSTER, MARTIN D. D. EVANS, MICHAEL FARRELL, ESTER FELEZ‐VINAS, GERARDO FERRARA, EL MEHDI FERROUHI, ANDREA FLORI, JONATHAN T. FLUHARTY‐JAIDEE, SEAN D. V. FOLEY, KINGSLEY Y. L. FONG, THIERRY FOUCAULT, TATIANA FRANUS, FRANCESCO FRANZONI, BART FRIJNS, MICHAEL FRÖMMEL, SERVANNA M. FU, SASCHA C. FÜLLBRUNN, BAOQING GAN, GE GAO, THOMAS P. GEHRIG, ROLAND GEMAYEL, DIRK GERRITSEN, JAVIER GIL‐BAZO, DUDLEY GILDER, LAWRENCE R. GLOSTEN, THOMAS GOMEZ, ARSENY GORBENKO, JOACHIM GRAMMIG, VINCENT GRÉGOIRE, UFUK GÜÇBILMEZ, BJÖRN HAGSTRÖMER, JULIEN HAMBUCKERS, ERIK HAPNES, JEFFREY H. HARRIS, LAWRENCE HARRIS, SIMON HARTMANN, JEAN‐BAPTISTE HASSE, NIKOLAUS HAUTSCH, XUE‐ZHONG (TONY) HE, DAVIDSON HEATH, SIMON HEDIGER, TERRENCE HENDERSHOTT, ANN MARIE HIBBERT, ERIK HJALMARSSON, SETH A. HOELSCHER, PETER HOFFMANN, CRAIG W. HOLDEN, ALEX R. HORENSTEIN, WENQIAN HUANG, DA HUANG, CHRISTOPHE HURLIN, KONRAD ILCZUK, ALEXEY IVASHCHENKO, SUBRAMANIAN R. IYER, HOSSEIN JAHANSHAHLOO, NAJI JALKH, CHARLES M. JONES, SIMON JURKATIS, PETRI JYLHÄ, ANDREAS T. KAECK, GABRIEL KAISER, ARZÉ KARAM, EGLE KARMAZIENE, BERNHARD KASSNER, MARKKU KAUSTIA, EKATERINA KAZAK, FEARGHAL KEARNEY, VINCENT VAN KERVEL, SAAD A. KHAN, MARTA K. KHOMYN, TONY KLEIN, OLGA KLEIN, ALEXANDER KLOS, MICHAEL KOETTER, ALEKSEY KOLOKOLOV, ROBERT A. KORAJCZYK, ROMAN KOZHAN, JAN P. KRAHNEN, PAUL KUHLE, AMY KWAN, QUENTIN LAJAUNIE, F. Y. ERIC C. LAM, MARIE LAMBERT, HUGUES LANGLOIS, JENS LAUSEN, TOBIAS LAUTER, MARKUS LEIPPOLD, VLADIMIR LEVIN, YIJIE LI, HUI LI, CHEE YOONG LIEW, THOMAS LINDNER, OLIVER LINTON, JIACHENG LIU, ANQI LIU, GUILLERMO LLORENTE, MATTHIJS LOF, ARIEL LOHR, FRANCIS LONGSTAFF, ALEJANDRO LOPEZ‐LIRA, SHAWN MANKAD, NICOLA MANO, ALEXIS MARCHAL, CHARLES MARTINEAU, FRANCESCO MAZZOLA, DEBRAH MELOSO, MICHAEL G. MI, ROXANA MIHET, VIJAY MOHAN, SOPHIE MOINAS, DAVID MOORE, LIANGYI MU, DMITRIY MURAVYEV, DERMOT MURPHY, GABOR NESZVEDA, CHRISTIAN NEUMEIER, ULF NIELSSON, MAHENDRARAJAH NIMALENDRAN, SVEN NOLTE, LARS L. NORDEN, PETER O'NEILL, KHALED OBAID, BERNT A. ØDEGAARD, PER ÖSTBERG, EMILIANO PAGNOTTA, MARCUS PAINTER, STEFAN PALAN, IMON J. PALIT, ANDREAS PARK, ROBERTO PASCUAL, PAOLO PASQUARIELLO, LUBOS PASTOR, VINAY PATEL, ANDREW J. PATTON, NEIL D. PEARSON, LORIANA PELIZZON, MICHELE PELLI, MATTHIAS PELSTER, CHRISTOPHE PÉRIGNON, CAMERON PFIFFER, RICHARD PHILIP, TOMÁŠ PLÍHAL, PUNEET PRAKASH, OLIVER‐ALEXANDER PRESS, TINA PRODROMOU, MARCEL PROKOPCZUK, TALIS PUTNINS, YA QIAN, GAURAV RAIZADA, DAVID RAKOWSKI, ANGELO RANALDO, LUCA REGIS, STEFAN REITZ, THOMAS RENAULT, REX W. RENJIE, ROBERTO RENO, STEVEN J. RIDDIOUGH, KALLE RINNE, PAUL RINTAMÄKI, RYAN RIORDAN, THOMAS RITTMANNSBERGER, IÑAKI RODRÍGUEZ LONGARELA, DOMINIK ROESCH, LAVINIA ROGNONE, BRIAN ROSEMAN, IOANID ROŞU, SAURABH ROY, NICOLAS RUDOLF, STEPHEN R. RUSH, KHALADDIN RZAYEV, ALEKSANDRA A. RZEŹNIK, ANTHONY SANFORD, HARIKUMAR SANKARAN, ASANI SARKAR, LUCIO SARNO, OLIVIER SCAILLET, STEFAN SCHARNOWSKI, KLAUS R. SCHENK‐HOPPÉ, ANDREA SCHERTLER, MICHAEL SCHNEIDER, FLORIAN SCHROEDER, NORMAN SCHÜRHOFF, PHILIPP SCHUSTER, MARCO A. SCHWARZ, MARK S. SEASHOLES, NORMAN J. SEEGER, OR SHACHAR, ANDRIY SHKILKO, JESSICA SHUI, MARIO SIKIC, GIORGIA SIMION, LEE A. SMALES, PAUL SÖDERLIND, ELVIRA SOJLI, KONSTANTIN SOKOLOV, JANTJE SÖNKSEN, LAIMA SPOKEVICIUTE, DENITSA STEFANOVA, MARTI G. SUBRAHMANYAM, BARNABAS SZASZI, OLEKSANDR TALAVERA, YUEHUA TANG, NICK TAYLOR, WING WAH THAM, ERIK THEISSEN, JULIAN THIMME, IAN TONKS, HAI TRAN, LUCA TRAPIN, ANDERS B. TROLLE, M. ANDREEA VADUVA, GIORGIO VALENTE, ROBERT A. VAN NESS, AURELIO VASQUEZ, THANOS VEROUSIS, PATRICK VERWIJMEREN, ANDERS VILHELMSSON, GRIGORY VILKOV, VLADIMIR VLADIMIROV, SEBASTIAN VOGEL, STEFAN VOIGT, WOLF WAGNER, THOMAS WALTHER, PATRICK WEISS, MICHEL VAN DER WEL, INGRID M. WERNER, P. JOAKIM WESTERHOLM, CHRISTIAN WESTHEIDE, HANS C. WIKA, EVERT WIPPLINGER, MICHAEL WOLF, CHRISTIAN C. P. WOLFF, LEONARD WOLK, WING‐KEUNG WONG, JAN WRAMPELMEYER, ZHEN‐XING WU, SHUO XIA, DACHENG XIU, KE XU, CAIHONG XU, PRADEEP K. YADAV, JOSÉ YAGÜE, CHENG YAN, ANTTI YANG, WOONGSUN YOO, WENJIA YU, YIHE YU, SHIHAO YU, BART Z. YUESHEN, DARYA YUFEROVA, MARCIN ZAMOJSKI, ABALFAZL ZAREEI, STEFAN M. ZEISBERGER, LU ZHANG, S. SARAH ZHANG, XIAOYU ZHANG, LU ZHAO, ZHUO ZHONG, Z. IVY ZHOU, CHEN ZHOU, XINGYU S. ZHU, MARIUS ZOICAN, REMCO ZWINKELS

In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.