Xiao Lu - Research

Job market paper: Exchange Rate Predictability and Financial Conditions

Revise & Resubmit: Empirical Economics

Abstract: We model the conditional distribution of future exchange rate returns for nine currencies as a function of real-time financial conditions. We show that the lower and upper quantiles of the exchange rate return distribution exhibit significant in-sample co-movement with financial conditions. Similarly, the conditional moments of the out-of-sample forecast display time-varying patterns, with the variance and kurtosis showing the most pronounced changes during and after the 2008-09 financial crisis. Deteriorating financial conditions are associated with an increase in volatility, particularly for commodity currencies. Overall, we conclude that financial conditions capture tail dependencies in exchange rate returns and contain valuable information for out-of-sample prediction.

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Liquidity and Exchange Rates: An Updated Out-of-Sample Analysis

Abstract: This paper extends Engel and Wu (2023) by providing density forecasts of exchange rates and out-of-sample predictions using more recent data. The government bond liquidity measure is effective for both point and density forecasts of G10 currency returns, enabling linear or nonlinear models to outperform the random walk without drift benchmark in a monthly setting. The analysis also shows that, despite both being periods of financial stress, the 2008 Global Financial Crisis and the COVID-19 pandemic affected exchange rate returns differently in terms of direction, predictability, and volatility. Furthermore, restricting predictive skewness improves the accuracy of monthly exchange rate density forecasts.

Features and Predictors of Canadian GDP Tail Risks

Abstract: I examine the conditional distribution of Canadian GDP growth as a function of several economic and financial variables. The results indicate that Canadian and US financial indicators effectively forecast the conditional distribution of Canadian GDP growth. In addition, the US National Financial Conditions Index (NFCI) significantly predicts its Canadian counterpart (FSI) and exhibits higher out-of-sample accuracy at the one-year-ahead horizon. However, in quantile regressions controlling for both the FSI and NFCI, the FSI more strongly predicts Canadian GDP tail risks (defined as the lower 5th percentile of the conditional GDP growth distribution). Finally, simulation results suggest that periods of low volatility and high GDP growth tend to precede periods of high volatility and low GDP growth in Canada.

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