Latest Research Publications

Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are based on clipped data, and the third of which is based on exponentially weighted Huber loss minimization. In particular, in order for the Huber approach to adapt to non-stationary financial returns, we propose to solve a tuning-free weighted Huber loss minimization problem to jointly estimate the volatility and the optimal robustification parameter at each time point. We then inflate this robustification parameter and use it to update the volatility proxy to achieve optimal balance between the bias and variance of the global empirical loss. We also extend this Huber method to construct volatility predictors. Finally, we exploit the proposed robust volatility proxy to compare different volatility predictors on the Bitcoin market data and calibrated synthetic data. It turns out that when the sample size is limited, applying the robust volatility proxy gives more consistent and stable evaluation of volatility forecasts.

After-school tutoring has risen globally despite limited evidence of effectiveness. We implement a randomized after-school tutoring program in rural China where many children are left-behind by parents in care of grandparents. Compared to tutees cared for by parents, those in care of grandparents reported much smaller home-tutoring reductions but larger test-score gains. We interpret our data analysis with a model with tutoring efficacy and substitution between private and public inputs both differing by family background: Increased public tutoring generates larger test-score gains for children who experience greater tutoring efficacy and lesser substitution with household inputs, consistent with our estimates.

This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts’ average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts’ average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts’ forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies.

We study how derivatives (with nonlinear payoffs) affect the underlying asset’s liquidity. In a rational expectations equilibrium, informed investors expect low conditional volatility and sell derivatives to the others. These derivative trades affect different investors’ utility differently, possibly amplifying liquidity risk. As investors delta hedge their derivative positions, price impact in the underlying drops, suggesting improved liquidity, because informed trading is diluted. In contrast, effects on price reversal are ambiguous, depending on investors’ relative delta hedging sensitivity (i.e., the gamma of the derivatives). The model cautions of potential disconnections between illiquidity measures and liquidity risk premium due to derivatives trading.

We present a rational expectations model of credit-driven crises, providing a new perspective to explain why credit booms can lead to severe financial crises and aftermath slow economic recoveries. In our model economy, banks can operate in two types of business. They are sequentially aware of the deterioration of fundamentals of the speculative business and decide whether to continue credit extension in that business or liquidate capital and move into the traditional business. However, because individual banks face uncertainty about how many of their peers have been aware, they rationally choose to extend credit in the speculative business for a longer time than is socially optimal, leading to an over-delayed crisis and consequently more banks being caught by the crisis. This in turn renders the financial crisis more severe and the subsequent economic recovery slower. Extending to a standard textbook macroeconomic growth setting, our model also generates rich dynamics of economic booms, slowdowns, crashes, and recoveries.

We provide the first quantitative evaluation of the impacts and interactions of the US-China trade wars and industrial policy competitions. We extend the model in Caliendo and Parro (2015) by incorporating sectoral external economies of scale. We find that (i) under our baseline calibration of scale economies, the “Made-in-China 2025” (“MIC 2025”) subsidies tend to improve the welfare of both China and the U.S.; (ii) the US gains from Trumpian tariffs if China does not retaliate, and the gain is larger if China had implemented the “MIC 2025” project; (iii) in a non-cooperative tariff game targeting on high-tech industries supported by the “MIC 2025”, both China and the U.S. impose high tariffs and endure welfare losses; and (iv) if it is feasible for the U.S. to subsidize its own high-tech industries, the U.S. would reduce its tariffs on high-tech imports from China and benefit from its own industrial subsidies. These results (i) provide a rationale for trade wars and industrial policy competitions between the U.S. and China and (ii) suggest that industrial subsidies, if properly implemented, may generate less distortion than import tariffs as a means of international competition.

Problem definition: We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Academic/practical relevance: Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. Methodology: We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Results: Moreover, we investigate the structure of optimal pricing policies and prove the suboptimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. Managerial implications: In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effect offers a strong motive for promotions and price fluctuations.

On-demand service platforms are interested in having gig workers use self-set, nonbinding performance goals to improve efforts and performance. To examine the effects of such self-set goal mechanisms, we build a behavioral model, derive theoretical results and testable hypotheses, and conduct a field experiment using a large gig platform for food delivery. Our model analysis finds that individual workers’ optimal self-set goals may exhibit a spectrum of difficulty levels, ranging from trivial to impossible, depending on workers’ reference-dependent utility coefficients and self-control cost. Moreover, workers’ efforts are higher with properly set goals rather than no-goals. Consistently, our experimental data show significant treatment effects of self-goal setting, and a causal tree algorithm identifies subgroups who are mostly motivated by self-set goals. Furthermore, our study compares two common types of performance metrics for goal setting: the number of completed orders and total revenue. Our model suggests different cases of effort and performance improvement for the two goal types. The experimental data suggests that both goal types improve efforts equally but lead to different attainment rates. Specifically, the goal attainment rate is lower for the revenue-goal treatment than for the order-quantity-goal treatment. Further analysis reveals that this disparity is due to workers setting excessively high revenue goals. Our study demonstrates the efficacy and limitations of self-goal-setting mechanisms and yields two important managerial implications. First, the implementation of self-goal-setting mechanisms could improve gig workers’ efforts and performance. Second, encouraging order-quantity goals instead of revenue goals could help gig workers achieve higher attainment rates.

International Financial Reporting Standard (IFRS) 9 is of practical relevance to banks because it requires intense monitoring of borrowers to record timely loan losses. Using data from 50 countries, we find that accounting-driven bank monitoring due to IFRS 9 adoption reduces firms’ reliance on bank debt relative to public debt. This finding is consistent with firms experiencing more costly bank monitoring after a shift in regulatory reporting that requires banks to monitor borrowers more intensely. In further analyses, we find that the negative effect of IFRS 9 adoption on bank debt reliance is more pronounced with more stringent regulatory supervision of banks, consistent with regulatory stringency exacerbating costly bank monitoring for firms. We also find that the negative effect is stronger when firms can more easily switch from bank debt to public debt financing, consistent with the relevance of switching costs in firms’ decisions to avoid costly bank monitoring.