Emil Siriwardane is an assistant professor of business administration in the Finance Unit. He teaches the Finance II course in the MBA required curriculum.
Professor Siriwardane’s research interests lie in asset pricing, the interplay between macroeconomics and finance, and financial intermediation. In recent work, he has studied how the risk-bearing capacity of large financial institutions impacts the pricing of credit risk in credit default swap markets.
Professor Siriwardane earned his PhD in finance from the Stern School of Business at New York University and a BSE in operations research and financial engineering from Princeton University.
During the financial crisis, financial firm leverage and volatility both rose dramatically. Consequently, institutions are being asked to reduce leverage in order to reduce risk, though the effectiveness depends upon the role of capital structure in volatility. To address this question, we build a statistical model of equity volatility that accounts for leverage. Our approach blends Merton’s insights on capital structure with traditional time-series models of volatility. Using our model we quantify how capital injections impact the risk of financial institutions and estimate firm-specific precautionary capital needs. In addition, the longstanding observation that volatility is more responsive to negative shocks than positive is shown to be less a consequence of actual leverage than it is of risk premiums.
We propose a statistical model of the term structure of U.S. treasury yields tailored for long-term probability-based scenario generation and forecasts. Our model is easy to estimate and is able to simultaneously reproduce the positivity, persistence, and factor structure of the yield curve. Moreover, we incorporate heteroskedasticity and time-varying correlations across yields, both prevalent features of the data. The model also features a regime-switching short-rate model. We evaluate the out-of-sample performance of our model in terms of forecasting ability and coverage properties and find that it improves on the standard Diebold and Li model.
We document a strong and robust relation between the one-year real rate and precautionary savings motives, as measured by the stock market. Our novel proxy for precautionary savings, based on the difference in valuations between low- and high- volatility stocks, explains 37% of variation in the real rate. In addition, the real rate forecasts returns on the low-minus-high volatility portfolio, though it appears unrelated with measures of the quantity of risk. Our results suggest that precautionary savings motives, and thus the real rate, are driven by time-varying attitudes towards risk. We rationalize these findings in a stylized model with segmented investor clienteles and habit formation.
I analyze a rare disasters economy that yields a measure of the risk neutral probability of a macroeconomic disaster, p*t. A large panel of options data provides strong evidence that p*t is the single factor driving option-implied jump risk measures in the cross section of firms. This is a core assumption of the rare disasters paradigm. A number of empirical patterns further support the interpretation of p*t as the risk-neutral likelihood of a disaster. First, standard forecasting regressions reveal that increases in p*t lead to economic downturns. Second, disaster risk is priced in the cross section of U.S. equity returns. A zero-cost equity portfolio with exposure to disasters earns risk-adjusted returns of 7.6% per year. Finally, a calibrated version of the model reasonably matches the (i) sensitivity of the aggregate stock market to changes in the likelihood of a disaster and (ii) loss rates of disaster risky stocks during the 2008 financial crisis.
Using proprietary data on all credit default swap (CDS) transactions in the U.S. from 2010 to 2014, I show that a firm's CDS spreads are driven by capital fluctuations of that firm's net protection sellers. Capital fluctuations of sellers account for 10% of the time-series variation in spread changes, a significant amount given that observable firm and macroeconomic factors account for less than 16% of variation during this span. Sellers of protection are also highly concentrated, with five sellers responsible for nearly half of net selling. This concentration leads to market fragility—losses at the largest sellers have an outsized impact on CDS pricing. These findings suggest a high degree of short-run market segmentation and support theories where capital market frictions play a first-order role in determining market prices.
This appendix contains the following supplements to the main text: (i) additional facts regarding the size and concentration of the CDS market; (ii) some analysis relating buyer and seller capital to the CDS-bond basis; (iii) a discussion and some support evidence as to why capital might be slow moving in the CDS market; (iv) supportive analysis of the impact of the 2011 Japanese tsunami on CDS markets; and (v) a discussion of why the role of dealers as prime brokers does not impact the stylized facts and results presented throughout the paper.
During the financial crisis, financial firm leverage and volatility both rose dramatically. Consequently, institutions are being asked to reduce leverage in order to reduce risk, though the effectiveness depends upon the role of capital structure in volatility. To address this question, we build a statistical model of equity volatility that accounts for leverage. Our approach blends Merton’s insights on capital structure with traditional time-series models of volatility. Using our model we quantify how capital injections impact the risk of financial institutions and estimate firm-specific precautionary capital needs. In addition, the longstanding observation that volatility is more responsive to negative shocks than positive is shown to be less a consequence of actual leverage than it is of risk premiums.
Nickol Hackett, chief investment officer of the Cook County Pension Fund, is responsible for investing the fund’s $9 billion worth of assets on behalf of the employees of Cook County, Illinois. Like many other defined-benefit pensions at the time, the Cook County pension faces a funding shortfall, meaning that the value of its assets is below the value of its future obligations to retirees. Hackett can invest in fixed income securities, public equities, and alternative assets such as hedge funds, real estate, or private equity. What are the costs and benefits of each asset class? Should the funding status of the pension impact the asset allocation process? How should Hackett invest in order to grow the value of the fund’s assets and secure the retirement benefits for thousands of Cook County’s employees?
Siriwardane, Emil Nuwan. "Asset Allocation at the Cook County Pension Fund." Harvard Business School Spreadsheet Supplement 218-704, September 2017.
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