This paper studies how government debt variables impact estimates of the classic and new UIP puzzles for quarterly data between 2000 and 2020 of 6 developed countries in relation to the United States. I estimate country-pair VECMs to model...
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ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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This paper studies how government debt variables impact estimates of the classic and new UIP puzzles for quarterly data between 2000 and 2020 of 6 developed countries in relation to the United States. I estimate country-pair VECMs to model cointegration relations between debt variables, price differences, interest rates differences and nominal exchange rate. I compare this framework with one without debt variables following Engel (2016) using quarterly data between 1979 and 2020. In the framework without debt, I don’t find the new UIP puzzle while in the framework with debt, I do find it. Government debt variables are significant and alter the sign of co-movements between difference in interest rates and far-ahead ex-post and ex-ante excess currency returns. The magnitude of the effect is economically relevant. Government debts coefficients cannot be uniquely associated with convenience yield story
In Chapter 1, I document a novel result regarding the uncovered interest rate parity (UIP) puzzle: investing in high interest rate currencies does not yield positive excess returns during recessions. That is, the UIP holds in bad times. This new...
more
ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
Inter-library loan:
No inter-library loan
In Chapter 1, I document a novel result regarding the uncovered interest rate parity (UIP) puzzle: investing in high interest rate currencies does not yield positive excess returns during recessions. That is, the UIP holds in bad times. This new finding is a challenge to existing rational expectations models that address the UIP puzzle. A model featuring investors whose interest rate expectations are distorted by extrapolation bias and time-varying stickiness is able to quantitatively account for this evidence when calibrated to available survey data. The model also generates predictions for bond return predictability, the profitability of time-series momentum in the foreign exchange and fixed income markets, and foreign exchange predictability during the post-2007 period, which are borne out in the data. In Chapter 2 (with Gabriel Cuevas Rodriguez and Danyu Zhang), we document three stylized facts related to equity analysts’ earnings expectations: (1) consensus earnings expectations underreact to news unconditionally, (2) the degree of underreaction declines during high-volatility periods, and (3) the degree of underreaction declines over our sample. To account for these findings, we develop a simple model featuring rational inattention. We show that our model is able to account for the unconditional profitability of momentum, momentum crashes, and the diminishing profitability of momentum over our sample. Based on the predictions of our model, we propose a trading strategy that mixes short-run and long-run momentum signals and show that the resultant mixed momentum strategy outperforms conventional long-run momentum strategies. Finally, we use a machine learning algorithm to estimate the predictable component of earnings surprises and construct a portfolio that is long (short) on stocks with excessively pessimistic (optimistic) earnings expectations. The resultant trading strategy generates an annualized Sharpe ratio of about 1.16 and its returns are not explained by popular factor models.
