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.
Using proprietary credit default swap (CDS) data, I investigate how capital shocks at protection sellers impact pricing in the CDS market. Seller capital shocks—measured as CDS portfolio margin payments—account for 12% of the time-series variation in weekly spread changes, a significant amount given that standard credit factors account for 18% during my sample. In addition, seller shocks possess information for spreads that is independent of institution-wide measures of constraints. These findings imply a high degree of market segmentation and suggest that frictions within specialized financial institutions prevent capital from flowing into the market at shorter horizons.
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.
Andrea L. Eisfeldt, Bernard Herskovic, Sriram Rajan and Emil Siriwardane
Over-the-counter (OTC) markets for financial assets are dominated by a relatively small number of core intermediaries and a large number of peripheral customers. In this paper, we develop a model of trade in a core-periphery network and estimate its key structural parameters using proprietary credit default swap data from the Depository Trust & Clearing Corporation (DTCC). Using our calibrated model, we provide quantitative estimates of: (1) the effect of network frictions on the level of OTC derivatives prices; (2) the key determinants of cross sectional dispersion in bilateral prices; and (3) how prices and risk-sharing change in response to the failure of a dealer.
We propose a new measure of the economy’s risk appetite based on the valuation of volatile stocks. Unlike proxies for risk appetite derived from aggregates, our measure is strongly correlated with safe asset prices and future economic activity. When our measure is high, safe bonds fall in value and risky assets rally, forecasting a boom in investment. Risk appetite is closely linked to investors’ expectations of future risk and rises following positive macroeconomic outcomes. Periods of elevated risk appetite are predictably followed by upward revisions in expectations of risk, suggesting that these expectations may not be rational.
Pflueger, Carolin E., Emil Siriwardane, and Adi Sunderam. "A Measure of Risk Appetite for the Macroeconomy." Harvard Business School Working Paper, No. 17-040, November 2016. (Revised November 2018. NBER Working Paper Series, No. 24529, April 2018)
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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.
On June 21, 2016, Tesla Motors, Inc. announced its offer to acquire SolarCity, bringing CEO Elon Musk one step closer to completing his goal of moving the world from a hydrocarbon-based economy to a solar-electric one. Markets and analysts were mixed in their reaction to the announcement; some thought the deal would be a distraction to Tesla management at a critical time; others thought it was a “bailout” of SolarCity. Following weeks of due diligence, Tesla and SolarCity finalized their merger agreement and worked to justify the transaction. Joan Banister, a financial advisor, must prepare to address her clients’ concerns about their various financial positions in Tesla and SolarCity.
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. (Revised August 2018.)
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