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Timing exchange rates using order flow: The case of the Loonie

Journal of Banking & Finance 2010 34(12), 2917-2928
This paper examines the relation between the Canadian dollar/US dollar (CAD) exchange rate and foreign exchange order flow employing a novel data set on CAD order flow over the period 1994–2005. We investigate empirically the predictive information content and the determinants of order flow. The results suggest that order flow has strong out-of-sample predictive power for CAD returns, yielding significant market timing ability and tangible economic gains in a stylized dynamic asset allocation context. In terms of its determinants, order flow appears to reflect not only the menu of macroeconomic variables typically suggested in the literature but is also closely related to commodity price fluctuations, as expected from a ‘commodity currency’.

Currency Premia and Global Imbalances

Review of Financial Studies 2016 29(8), 2161-2193
We show that a global imbalance risk factor that captures the spread in countries’ external imbalances and their propensity to issue external liabilities in foreign currency explains the cross-sectional variation in currency excess returns. The economic intuition is simple: net debtor countries offer a currency risk premium to compensate investors willing to finance negative external imbalances because their currencies depreciate in bad times. This mechanism is consistent with exchange rate theory based on capital flows in imperfect financial markets. We also find that the global imbalance factor is priced in cross-sections of other major asset markets. Received February 11, 2014; accepted January 25, 2016 by Editor Geert Bekaert.

The cost of foreign-currency lending

Journal of Banking & Finance 2022 136, 106398 open access
Lending to corporates in foreign currencies can expose banks to substantial currency risk. Using global syndicated loan data, we find that a one-standard-deviation increase in exchange rate volatility increases loan spreads somewhere in the range between 5.5 and 16.1 basis points for loans made in a currency different from the lenders’. This implies excess interest of approximately 1 to 3 USD million for loans of average size and duration. We also show that this finding is mostly attributed to credit constraints and deviations from perfect competition in international lending markets, and that borrowers can lower the extra cost by forming strong lending relationships with their banks.

Currency momentum strategies

Journal of Financial Economics 2012 106(3), 660-684
We provide a broad empirical investigation of momentum strategies in the foreign exchange market. We find a significant cross-sectional spread in excess returns of up to 10% per annum (p.a.) between past winner and loser currencies. This spread in excess returns is not explained by traditional risk factors, it is partially explained by transaction costs and shows behavior consistent with investor under- and overreaction. Moreover, cross-sectional currency momentum has very different properties from the widely studied carry trade and is not highly correlated with returns of benchmark technical trading rules. However, there seem to be very effective limits to arbitrage that prevent momentum returns from being easily exploitable in currency markets.

Currency Value

Review of Financial Studies 2017 30(2), 416-441 open access
We assess the properties of currency value strategies based on real exchange rates. We find that real exchange rates have predictive power for the cross-section of currency excess returns. However, adjusting real exchange rates for key country-specific fundamentals (productivity, the quality of export goods, net foreign assets, and output gaps) better isolates information related to the currency risk premium. In turn, the resultant measure of currency value displays considerably stronger predictive power for currency excess returns. Finally, the predictive information content in our currency value measure is distinct from that embedded in popular currency strategies, such as carry and momentum. Received June 26, 2015; accepted June 8, 2016 by Editor Stefan Nagel.

Foreign Exchange Volume

Review of Financial Studies 2022 35(5), 2386-2427 open access
We investigate the information contained in foreign exchange (FX) volume using a novel data set from the over-the-counter market. We find volume helps predict next-day currency returns and is economically valuable for currency investors. Predictability implies a stronger return reversal for currency pairs with abnormally low volume and is driven by the component of volume unrelated to volatility, liquidity, and order flow. We rationalize these findings via a simple model, in which FX volume helps reveal the degree of asymmetric information in currency markets. Testing this prediction shows that asymmetric information is uniform across currency pairs but varies across instruments.

Information Flows in Foreign Exchange Markets: Dissecting Customer Currency Trades

Journal of Finance 2016 71(2), 601-634 open access
ABSTRACT We study the information in order flows in the world's largest over‐the‐counter market, the foreign exchange (FX) market. The analysis draws on a data set covering a broad cross‐section of currencies and different customer segments of FX end‐users. The results suggest that order flows are highly informative about future exchange rates and provide significant economic value. We also find that different customer groups can share risk with each other effectively through the intermediation of a large dealer, and differ markedly in their predictive ability, trading styles, and risk exposure.

Carry Trades and Global Foreign Exchange Volatility

Journal of Finance 2012 67(2), 681-718
ABSTRACT We investigate the relation between global foreign exchange (FX) volatility risk and the cross section of excess returns arising from popular strategies that borrow in low interest rate currencies and invest in high interest rate currencies, so‐called “carry trades.” We find that high interest rate currencies are negatively related to innovations in global FX volatility, and thus deliver low returns in times of unexpected high volatility, when low interest rate currencies provide a hedge by yielding positive returns. Furthermore, we show that volatility risk dominates liquidity risk and our volatility risk proxy also performs well for pricing returns of other portfolios.

Nonstandard Errors

Albert J. Menkveld; Anna Dreber; Felix Holzmeister; Jürgen Huber; Magnus Johannesson; Michael Kirchler; SEBASTIAN NEUSÜß; Michael Razen; Utz Weitzel; DAVID ABAD-DÍAZ; Menachem Abudy; Tobias Adrian; Yacine Aït-Sahalia; Olivier Akmansoy; Jamie 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 Christopher Bindra; Geir Høidal Bjønnes; Jeffrey R. Black; Bernard S. Black; DIMITAR BOGOEV; SANTIAGO BOHORQUEZ CORREA; Oleg Bondarenko; CHARLES S. BOS; Ciril Bosch-Rosa; ELIE BOURI; Christian T. 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 L. 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 Foley; Kingsley Y. L. Fong; Thierry Foucault; TATIANA FRANUS; Francesco A. Franzoni; Bart Frijns; MICHAEL FRÖMMEL; SERVANNA M. FU; Sascha Füllbrunn; BAOQING GAN; GE GAO; Thomas 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; Ľuboš Pástor; VINAY PA℡; 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 Renò; 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
Journal of Finance 2024 79(3), 2339-2390 open access
ABSTRACT 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.