Exchange Rate Floor and Central Bank Balance Sheets: Simple Spillover Tests of the Swiss Franc
With Andreas M. Fischer
Aussenwirtschaft 67.02 (2016): 31-50

This paper examines spillover and spillback effects of unconventional monetary policies conducted by the European Central Bank (ECB) and Swiss National Bank (SNB) on the exchange rate’s distribution. The empirical setup examines the price response of EURCHF risk reversal to a change in ECB and SNB balance sheets, with a distinction for the period of the minimum exchange rate (floor). The analysis finds only weak evidence of spillover effects from the ECB, while the spillback effect from the SNB balance sheet is robust during the floor period.

Working Papers

Emerging Markets Currency Factors and U.S. High Frequency Economic Shocks
With Dalibor Eterovic

Journal of Portfolio Management, Forthcoming

Relying on the structural vector autoregression developed by Cieslak and Pang (2021), we identify four shocks to the U.S. economy based on the U.S. Treasury yield curve and the stock market: two fundamental news shocks (growth and money) and two risk-premium shocks (common and hedging). We find that these shocks explain over 40% of the time-series variation of emerging markets currency (EMFX) returns. Additionally, EMFX returns increase significantly with positive growth shocks and decrease with monetary tightening and risk-premium shocks. We show that growth and common shocks are priced in the cross-section of EMFX, with a positive and negative risk premia, respectively. We then build long-short currency portfolios based on several academically researched style factors and test their performance and relative exposure to the macroeconomic shocks affecting the U.S. economy. We find that only Carry and Macro Momentum long-short portfolios generate positive and significant alphas and excess returns over our sample. However, all single-factor portfolios have sizable exposure to the four shocks. We show that a simple multifactor approach to investing in EMFXs eliminates the exposure of excess returns to all macroeconomic shocks.

ESG Investing in Emerging Markets: Betting on Firm Fundamentals or Riding Investor Preferences?
With Wang Renxuan

Firms with different environmental, social, and governance (ESG) scores can have different costs of capital, either because ESG scores help forecast future cash flows – the fundamental channel – or because investors have non-pecuniary preferences for high-ESG-score assets – the preference channel. This paper examines the relation between ESG scores and the pricing of corporate bonds in emerging markets, an important yet understudied market for ESG-related issues. We identify the existence of a preference channel with a natural experiment – the historical opening of the Chinese onshore bond market – that leads to an increase in the proportion of international investors, who are arguably more ESG-conscious. Consistent with theory, we find that the bond yield of companies with high ESG scores decreases more than that of companies with low ESG scores. By focusing on firms that also have bonds traded in the offshore market, which, as opposed to the onshore market, does not experience any change in regulation, we can control for issuer-time fixed effects in a triple difference design, hence reducing considerably the influence of the fundamental channel.

Watch what they do, not what they say: Estimating regulatory costs from revealed preferences
With Sakai Ando and Kairong Xiao

Review of Financial Studies, R&R

We show that distortion in the size distribution of banks around regulatory thresholds can be used to identify costs of bank regulation. We build a structural model in which banks can strategically bunch their assets below regulatory thresholds to avoid regulations. The resulting distortion in the size distribution of banks reveals the magnitude of regulatory costs. Using U.S. bank data, we estimate the regulatory costs imposed by the Dodd-Frank Act. Although the estimated regulatory costs are substantial, they are significantly lower than those in self-reported estimates by banks.

Fuzzy Bunching
With Kairong Xiao

Existing bunching estimators infer bunching from a sharp spike at the regulatory threshold in the probability density function. Such spikes could be diffused and difficult to measure in small and noisy data. This paper introduces a new fuzzy bunching estimator that infers the extent of bunching from a bulge in the cumulative distribution function. The fuzzy bunching estimator has two advantages in small and noisy data: (1) it is more robust to diffused bunching, and (2) it avoids density estimation. Monte Carlo simulations and applications to well-established bunching settings clarify these advantages.

The Constraints on Portfolio Rebalancing: When Unconventional Monetary Policy Meets Insurance Capital Regulation
With Renxuan Wang

We examine the role of life insurers during episodes of Quantitative Easing (QE). To that end, we develop a new method to back out the duration gaps of life insurance companies based on their holdings and publicly available balance sheet information. We show that static capital regulation in the insurance sector actually could render the QE less effective: those who face higher duration gaps did not rebalance more towards corporate credits, contrary to what the portfolio rebalancing channel predicts

Work in progress

Subjective Growth Expectations of Entrepreneurs
With Renxuan Wang

Using a comprehensive and proprietary data set of 80,000 entrepreneurs' own revenue growth forecasts, we show that entrepreneurs have overly optimistic projections for future revenues, and consistently revise these projections downwards (constant disappointment). We relate these findings to entrepreneurs characteristics and find that entrepreneurs with high previous salary and more time invested in the start-up are less optimistic about future growth of the company. Finally, we use this dataset to confirm the previous findings that team strength is one of the best predictor of external funding.

Transaction Costs and Volume Capacity in the Cross-Section of Corporate Bond Returns