Emil Siriwardane

Emil N.
Siriwardane

PhD Candidate
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
esiriwar@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
esiriwar@stern.nyu.edu

Download Curriculum Vitae ›

› Working Papers

The Probability of Rare Disasters: Estimation and Implications
Olin Best Finance PhD Award in Honor of Prof. Greenbaum - Finalist
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.  Working Paper ›  Online Appendix ›   Slides ›
Structural GARCH: The Volatility-Leverage Connection
(with Robert F. Engle)
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 is, as we show, flexible enough to capture environments where the firm's asset volatility is stochastic and asset shocks are non-normal. As a result, our model also provides estimates of asset returns and asset volatility. In addition, our specification nests both a standard GARCH and the classical Merton model, which allows for a simple statistical test of how leverage interacts with equity volatility. We then apply the Structural GARCH model to two applications: the leverage effect and systemic risk measurement.  Slides ›

› Works-in-Progress

Long Run Value at Risk (with Robert F. Engle)
Risk Sharing in Credit Default Swap Markets

› Other Notes and Code

Notes on the Econometrics of Empirical Asset Pricing (with Python Code)
I provide a summary of time-series (TS) and cross-sectional (CS) asset pricing tests. For both TS and CS tests, I present the asymptotic distribution theory in a general K-factor framework. In addition, I show how to estimate asymptotic covariance estimators for both sets of tests that are robust to autocorrelation and heteroscedasticity using GMM. Download pdf › Download Python Code ›