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.
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.
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.
Emerging Markets Currency Factors and U.S. High Frequency Economic Shocks
With Dalibor Eterovic
Relying on the Structural Vector Autoregression (SVAR) developed by Cieslak and Pang (2021), we identify 4 shocks to the U.S. economy based on the Treasury yield curve and the stock market: two fundamental news shocks (growth and monetary policy) 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, and that EMFX returns increase significantly with positive U.S. growth shocks while decrease with monetary tightening and risk-premium shocks. We then build long-short currency portfolios based on several academically researched style factors and test their relative exposure to the U.S. macro shocks. 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 U.S. high frequency shocks. We show that a simple multi-factor approach for investing in Emerging Markets currencies eliminates the excess returns exposure to U.S. macro shocks.
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
Transaction Costs and Volume Capacity in the Cross-Section of Corporate Bond Returns
Subjective Growth Expectations of Entrepreneurs
With Renxuan Wang (work-in-progress)
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.