Jonas N. Eriksen

Assistant Professor

CREATES, Department of Economics and Business Economics, Aarhus University

About me

I am an Assistant Professor at the Department of Economics and Business Economics, Aarhus University and research fellow at the Center for Research in Econometric Analysis of Time Series (CREATES) and the Danish Finance Institute (DFI).

Research interests

My research interests are within asset pricing and financial economics. I am interested in the behavior of risk premia over time and across asset classes with a particular emphasis on the relation to the macroeconomy. My research areas can be summarized as follows:

Below you can find further information about my past and ongoing professional activities. For a list of publications, please refer to the research section. Further information about my teaching activities can be found in the teaching section. My full CV is available here.

Research

Below you can find a list of my research including supplementary links.

  Published version     Working paper version  

Publications
2019 Cross-sectional return dispersion and currency momentum. Jonas N. Eriksen. Journal of Empirical Finance 53, 2019, pp. 91-108.
I assess the relation between cross-sectional return dispersion in foreign exchange (FX) markets and currency momentum. I find that cross-sectional dispersion is priced in the cross-section of currency momentum returns and that an unexpected increase in cross-sectional dispersion is associated with positive (negative) excess returns to winner (loser) currencies. This mechanism can be related to monetary policy conditions. The empirical findings are robust to the inclusion of traditional currency risk factors, liquidity and market volatility variables, and transaction costs. Finally, the explanatory ability of cross-sectional dispersion extends to broader cross-sections of currency portfolios and to individual currencies.
2019 Negative house price co-movements and US recessions. Charlotte Christiansen, Jonas N. Eriksen, Stig V. Møller. Regional Science and Urban Economics 77, 2019, pp. 382-394.
We investigate the relation between large negative house price co-movements in the cross-section of US cities and the national business cycle. The occurrences of large negative house price co-movements across cities cluster over time and these clusters are closely linked to NBER recession dates. A simple co-movement measure that aggregates large negative city-level house price returns reliably predicts future recession periods. Weighting cities according to population or GDP when constructing the negative co-movement variable yields the largest forecasting power, indicating that larger cities that contribute more to the national GDP are more influential in terms of correctly signaling future recessions. Moreover, large negative house price co-movements contribute above and beyond traditional recession predictors, suggesting an important role for city-level housing information as an early warning indicator.
2017 Expected Business Conditions and Bond Risk Premia. Jonas N. Eriksen. Journal of Financial and Quantitative Analysis 52(4), 2017, pp. 1667-1703.
In this article, I study the predictability of bond risk premia by means of expectations to future business conditions using survey forecasts from the Survey of Professional Forecasters. I show that expected business conditions consistently affect excess bond returns and that the inclusion of expected business conditions in standard predictive regressions improve forecast performance relative to models using information derived from the current term structure or macroeconomic variables. The results are confirmed in a real-time out-of- sample exercise, where the predictive accuracy of the models is evaluated both statistically and from the perspective of a mean-variance investor that trades in the bond market.
2014 Forecasting US Recessions: The role of sentiment. Charlotte Christiansen, Jonas N. Eriksen, Stig V. Møller. Journal of Banking and Finance 49, 2014, pp. 459-468.
We study the role of sentiment variables as predictors for US recessions. We combine sentiment variables with either classical recession predictors or common factors based on a large panel of macroeconomic and financial variables. Sentiment variables hold vast predictive power for US recessions in excess of both the classical recession predictors and the common factors. The strong importance of the sentiment variables is documented both in-sample and out-of-sample.
Working papers
2019 Asset pricing and FOMC press conferences. Jonas N. Eriksen, Niels S. Grønborg. Working Paper.
A press conference (PC) organized by the Federal Open Market Committee (FOMC) followed half of the scheduled announcements from 2011 to 2018. We show that risky and safe assets behave very differently on days with and without PCs. Risky (safe) assets earn higher returns on PC (non-PC) days that are strongly and positively related to their market betas. Moreover, stock-bond correlations are positive (negative) on PC (non-PC) days and market betas on equities compress towards one on PC days, but remains unchanged on non-PC days. We discuss implications, interpretations, and possible explanations for our empirical findings.
Conferences: NEM 2019, QFFE 2019, INFINITI 2019
2019 Predicting bond return predictability. Daniel Borup, Jonas N. Eriksen, Mads M. Kjær, Martin Thyrsgaard. Working Paper.
We study risk premia in the U.S. Treasury bond market and provide new evidence on predictable state-dependent forecast performance using standard bond excess return predictors identified in the literature. To facilitate our empirical analysis, we develop a new statistical test for equal conditional and unconditional predictive ability among two or more forecasting methods that circumvents issues with multiple testing adjustments. Our empirical results indicate that the expectations hypothesis (EH) provides a reasonable anchor in times with low (high) economic activity (uncertainty), but not in other states of the world. A simple decision rule for selecting the set of models with indistinguishable predicted performance leads to substantial gains in forecast accuracy both from a statistical point of view and when evaluated using a mean-variance investor that trades in the bond market.
2018 Market betas and FOMC announcements. Simon Bodilsen, Jonas N. Eriksen, Niels S. Grønborg. Working Paper.
We show that stock market betas behave differently on days with scheduled FOMC announcement contra non-announcement days. While prior studies often find that betas are unaffected by monetary policy announcements, we find, using high frequency intra-day return data, that post-ranking betas of high-risk assets tend to decrease and that betas of low-risk assets tend to increase on announcement days. That is, betas shrink towards one on announcement days and offers another channel to explain the strong relation between betas and average returns observed on announcement days.
2017 Real-time macro fundamentals and global asset returns. Jonas N. Eriksen, Maik Schmeling, Christian Wagner. Working Paper.
We provide empirical evidence that key macro fundamentals predict excess returns to global equities, currencies, and fixed income in real time. We employ OECD vintage data on industrial production, consumer prices, unemployment, retail sales, and the trade balance for 16 developed countries to assess return predictability in a pure out- of-sample setting. Trading on the time series variation of these macro fundamentals generates significant excess returns and high Sharpe Ratios across asset classes. Moreover, the returns to our global macro strategies capture a sizable fraction of carry and momentum returns in international asset markets, suggesting that carry and momentum across asset classes can be linked to global macro fundamentals.
Dissertation

Teaching

I am currently teaching the following MSc. course at the Department of Economics and Business Economics, Aarhus University.

I have previously taught the following courses

  • Asset Pricing (F2017,F2018)
  • Fixed Income Securities (F2015,F2016)
  • International Business Finance (S2016,S2017)