Jason Wu

Dynamic factor Value-at-Risk for large heteroskedastic portfolios

We propose a methodology that can efficiently measure the Value-at-Risk (VaR) of large portfolios with time-varying volatility and correlations by bringing together the established historical simulation framework and recent contributions to the dynamic factor models literature. We find that the proposed methodology performs well relative to widely used VaR methodologies, and is a significant improvement from a computational point of view.

The Financial Sector and the Real Economy During the Financial Crisis: Evidence from the Commercial Paper Market

Shocks to the financial sector led credit spreads to widen to unprecedented levels in many markets during the 2007-2008 financial crisis. The rise in spreads attracted attention because it could signal a disruption in financial markets, which has been widely linked to an increased burden on non-financial firms. This paper disentangles the relative contributions of credit and liquidity risk in explaining the widening of commercial paper spreads. In doing so, we find that liquidity risk was isolated to the financial sector throughout the first two major shocks to the system (August 2007 and March 2008). Indeed, controlling for credit risk, non-financial corporations saw little or no change in the cost of funding during this time period. After the bankruptcy of Lehman Brothers, for the first time, liquidity problems in the commercial paper market spilled out of the financial sector into the spreads of low credit quality non-financial firms. This effect had a disproportionately larger impact on those low credit-quality non-financial firms that placed paper exclusively through financial sector dealers. High credit quality firms remained unaffected throughout. Our interpretation of the results is that markets were able to differentiate not only between safe and imperiled firms in the midst of the crisis, but also to isolate where liquidity effects were most likely to be salient.

The Taylor Rule and Interval Forecast for Exchange Rates

This paper attacks the Meese-Rogoff (exchange rate disconnect) puzzle from a different perspective: out-of-sample interval forecasting. Most studies in the literature focus on point forecasts. In this paper, we apply Robust Semi-parametric (RS) interval forecasting to a group of Taylor rule models. Forecast intervals for twelve OECD exchange rates are generated and modified tests of Giacomini and White (2006) are conducted to compare the performance of Taylor rule models and the random walk. Our contribution is twofold. First, we find that in general, Taylor rule models generate tighter forecast intervals than the random walk, given that their intervals cover out-of-sample exchange rate realizations equally well. This result is more pronounced at longer horizons. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting the distributions of exchange rates. The benchmark Taylor rule model is also found to perform better than the monetary and PPP models. Second, the inference framework proposed in this paper for forecast-interval evaluation, can be applied in a broader context, such as inflation forecasting, not just to the models and interval forecasting methods used in this paper.

Dynamic Factor Value-at-Risk for Large Heteroskedastic Portfolios

We propose a methodology that can efficiently measure the Value-at-Risk (VaR) of large portfolios with time-varying volatility and correlations by bringing together the established historical simulation framework and recent contributions to the dynamic factor models literature. We find that the proposed methodology performs well relative to widely used VaR methodologies, and is a significant improvement from a computational point of view.

The Effects of Supervision on Bank Performance: Evidence from Discontinuous Examination Frequencies

This paper estimates causal effects of supervision on bank performance using discontinuities in the minimum frequency of examinations required by regulation. This frequency is discontinuous at a value of bank assets that varied over time, allowing us to break the endogeneity between supervision and performance and to separate the effects of examinations from confounding effects of other banking policies that are triggered by asset thresholds too. We find that more frequent examinations increase profitability by decreasing loan losses and delinquencies. This suggests that supervisors limit the risks that banks are exposed to and, consequently, limit banks’ losses on risky assets.

Cointegration Test with Stationary Covariates and the CDS-Bond Basis During the Financial Crisis

This paper proposes a residual based cointegration test with improved power. Based on the idea of Hansen (1995) and Elliott & Jansson (2003) in the unit root testing case, stationary covariates are used to improve the power of the residual based Augmented Dickey Fuller (ADF) test. The asymptotic null distribution contains difficult to estimate nuisance parameters for which there is no obvious method of estimation, therefore we propose a bootstrap methodology to obtain test critical values. Local-to-unity asymptotics and Monte Carlo simulations are used to evaluate the power of the test in large and small samples, respectively. These exercises show that the addition of covariates increases power relative to the ADF and Johansen tests, and that the power depends on the long-run correlation between the covariates and the cointegration candidates. The new test is used to test for cointegration between Credit Default Swap (CDS) and corporate bond spreads for a panel of U.S. firms during the 2007-2009 financial crisis. The new test finds stronger evidence for cointegration between the two spreads for more firms, relative to ADF and Johansen tests.