Emil Siriwardane

Emil N.

PhD Candidate
Department of Finance
Stern School of Business
New York University
44 W. 4th St., Fl. 9, Room 197G K
New York, NY 10012
(212) 998 - 0331
esiriwar at stern.nyu.edu
Department of Finance
Stern School of Business, New York University
44 W. 4th St., Fl. 9, Room 175 K
New York, NY 10012

(212) 998 - 0331

Download Curriculum Vitae ›

› Working Papers

Concentrated Capital Losses and the Pricing of Corporate Credit Risk
Using proprietary credit default swap (CDS) data from 2010-14, I show that capital fluctuations for sellers of CDS protection are an important determinant of CDS spread movements. I first establish that markets are dominated by a handful of net protection sellers, with five sellers accounting for nearly half of all net selling. In turn, a reduction in their total capital increases CDS spreads. Capital fluctuations of the largest five sellers account for over 10 percent of the time-series variation in spread changes, a significant amount given that observable firm and macroeconomic factors account for less than 17 percent of variation during this time period. I then demonstrate that the concentration of sellers creates fragility — higher concentration results in more volatile risk premiums. I also employ a number of complementary approaches to address identification, such as using the 2011 Japanese tsunami as an exogenous shock to the risk bearing capacity of CDS traders. My findings are consistent with asset pricing models with limited investment capital, but also suggest that both the level and distribution of capital are crucial for accurately describing price dynamics.   Paper ›
Structural GARCH: The Volatility-Leverage Connection (with Robert F. Engle)
Finalist: 2014 AQR Insight Award  ›
We propose a new model of volatility where financial leverage amplifies equity volatility by what we call the “leverage multiplier.” The exact specification is motivated by standard structural models of credit; however, our parametrization departs from the classic Merton (1974) model and can accommodate environments where the firm's asset volatility is stochastic, asset returns can jump, and asset shocks are non-normal. In addition, our specification nests both a standard GARCH and the Merton model, which allows for a statistical test of how leverage interacts with equity volatility. Empirically, the Structural GARCH model outperforms a standard asymmetric GARCH model for approximately 74 percent of the financial firms we analyze. We then apply the Structural GARCH model to two empirical applications: the leverage effect and systemic risk measurement. As a part of our systemic risk analysis, we define a new measure called “precautionary capital” that uses our model to quantify the advantages of regulation aimed at reducing financial firm leverage.  Slides ›  Paper ›
The Probability of Rare Disasters: Estimation and Implications
Finalist: 2013 Olin Best Finance PhD Award in Honor of Prof. Greenbaum
I analyze a rare disasters economy that yields a measure of the risk neutral (RN) probability of a consumption disaster. A large panel of options data provides strong evidence of a common aggregate RN disaster probability. Empirically, I find the market return sensitivity to RN disaster probability to be consistent with a reasonable calibration of the model. In addition, I show that the RN disaster probability is a robust predictor of business cycle variables as suggested by a full general equilibrium model. I also derive a model-implied measure of firm disaster risk. An equity portfolio consisting of high disaster risk stocks earns excess annualized returns of 11.59%, even after controlling for a plethora of risk-factors. Following with model intuition, the RN probability of disaster positively forecasts returns of the portfolio of high disaster risk stocks. Finally, I use the cross-section of equity returns to estimate moments of disaster recovery rates. New Version Coming Soon

› Works-in-Progress

Long Run Value at Risk (with Robert F. Engle)
We compute long-horizon value at risk by allowing volatility to have a high-frequency and a low-frequency component. The low-frequency component is calibrated to match the implied volatility term structure from options, thereby incorporating additional option market information into VaR forecasts.   NYU Stern V-Lab for Some Examples  ›
CDS Risk Factors and Stress Testing (with Sriram Rajan)
We use proprietary data on CDS positions to estimate individual portfolio exposures to aggregate risk factors, and use our results to conduct stress tests of CDS portfolios. Our approach also delivers a simple way to flag macro-scenarios that would generate simultaneous CDS losses for important sets of counterparties, like banks.
Has Central Clearing Improved Risk Sharing in CDS Markets?
Central clearing of derivatives was a major component of regulation post-crisis. I investigate whether central clearing parties (CCPs) have affected the allocation of credit risk in CDS markets.
Embedded Leverage in Equities (with Robert F. Engle)
Equities, when viewed as options on the underlying assets of a firm, embed implicit leverage for investors. We explore the asset pricing consequences for investors facing explicit leverage constraints.

› Other Notes and Code

Notes on the Econometrics of Empirical Asset Pricing (with Python Code)
Summary of time-series and cross-sectional asset pricing tests, with the asymptotic distribution theory in a general K-factor framework. Plus, formulas for GMM covariance estimators that are robust to autocorrelation and heteroscedasticity. Download pdf › Download Python Code ›