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 studies the ways in which financial intermediaries influence capital markets, how perceptions of risk impact business cycles, and more recently, how public pensions make investment decisions.
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.
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 studies the ways in which financial intermediaries influence capital markets, how perceptions of risk impact business cycles, and more recently, how public pensions make investment decisions.
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.
We propose a novel measure of risk perceptions: the price of volatile stocks (PVS), defined as the book-to-market ratio of low-volatility stocks minus the book-to-market ratio of high-volatility stocks. PVS is high when perceived risk directly measured from surveys and option prices is low. When perceived risk is high according to our measure, safe asset prices are high, risky asset prices are low, the cost of capital for risky firms is high, and real investment is forecast to decline. Perceived risk as measured by PVS falls after positive macroeconomic news. These declines are predictably followed by upward revisions in investor risk perceptions. Our results suggest that risk perceptions embedded in stock prices are an important driver of the business cycle and are not fully rational.
Pflueger, Carolin E., Emil Siriwardane, and Adi Sunderam. "Financial Market Risk Perceptions and the Macroeconomy." Harvard Business School Working Paper, No. 17-040, November 2016. (Revised September 2019. Conditionally accepted at Quarterly Journal of Economics. NBER Working Paper Series, No. 26290, September 2019)
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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.
We document large variation in net-of-fee performance across public pension funds investing in the same private equity fund. In aggregate, these differences imply that the pensions in our sample would have earned $45 billion more – equivalent to $8.50 more per $100 invested – had they each received the best observed terms in their respective funds. There are also large pension-effects in the sense that some pensions systematically pay more fees than others when investing in the same fund. With better terms, the 95th percentile pension would have earned $14.91 more per $100 invested compared to $1.12 for the 5th percentile pension. Pension characteristics such as commitment size, overall size, relationships with fund managers, and governance account for a modest amount of the pension effects, meaning similar pensions consistently pay different fees.
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.
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.
Siriwardane, Emil, and Luis M. Viceira. "Blackstone Alternative Asset Management in 2018." Harvard Business School Teaching Note 219-092, January 2019. (Revised March 2020.)
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In early 2018, Blackstone announced that John McCormick would succeed Tom Hill as President and Chief Executive Officer of Blackstone Alternative Asset Management (BAAM), the largest fund-of-hedge funds in the world by a sizeable margin. As new CEO, McCormick must decide on strategic growth options for the firm as its traditional FOF business is maturing and growth in its nearly $80 billion in assets under management (AUM) is plateauing. In the recent past, BAAM had expanded into other investing businesses, making direct investments, seeding early-stage hedge funds, taking general partner stakes in investment management firms, and offering alternative investment products to retail investors. To determine the appropriate path forward, McCormick must assess whether BAAM has a competitive edge in each of these platforms, as well as if further expansion could create internal conflicts of interest within BAAM and, more broadly, Blackstone.
Siriwardane, Emil N., and E. Scott Mayfield. "Tesla-SolarCity." Harvard Business School Teaching Note 219-032, December 2018. (Revised March 2019.)
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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 Teaching Note 219-074, December 2018. (Revised March 2020.)
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Siriwardane, Emil Nuwan. "Asset Allocation at the Cook County Pension Fund." Harvard Business School Spreadsheet Supplement 219-729, December 2018.
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Siriwardane, Emil Nuwan. "Asset Allocation at the Cook County Pension Fund." Harvard Business School Spreadsheet Supplement 218-704, September 2017. (Revised March 2019.)
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