Nicola Fusari. web-site: EDUCATION. Postdoctoral fellow, Kellogg School of Management Evanston, IL

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1 Nicola Fusari The Johns Hopkins Carey Business School 100 International Dr Baltimore, MD (847) February 21, 2014 Citizenship: Italian web-site: EDUCATION Assistant Professor, The Johns Hopkins Carey Business School Baltimore, MD 2013 to present Postdoctoral fellow, Kellogg School of Management Evanston, IL Visiting Postdoctoral fellow, Kellogg School of Management Evanston, IL PhD in Finance, Swiss Finance Institute at the University of Lugano Lugano, Switzerland Thesis: Essays in Option Pricing Degree in Economics and Banking, cum laude University of Verona, Italy RESEARCH INTERESTS Empirical and theoretical asset pricing, equity and variance risk-premia dynamics, jump risk, volatility dynamics, high-frequency data, derivatives pricing, real options. PUBLICATIONS, WORKING PAPERS, AND WORKS IN PROGRESS Realizing Smiles: Option Pricing with Realized Volatility, with F. Corsi and D. La Vecchia. Journal of Financial Economics, Valuing Modularity as a Real Options, with A. Gamba Management Science, 55(11), November Barrier Option Pricing with Adjusted Transition Probabilities, with G. Barone-Adesi and J. Theal Journal of Derivatives, 16(2), winter 2008.

2 p. 2 Parametric Inference and Dynamic State Recovery from Option Panels, with T. G. Andersen and V. Todorov (submitted). Risk Premia Embedded in Option Panels, with T. G. Andersen and V. Todorov. (submitted) PROFESSIONAL EXPERIENCE Citadel Group August 2010 Teaching assistant: Advanced Option Pricing - Prof. Kathleen Hagerty Chicago, IL ACADEMIC EXPERIENCE Kellogg School Management 2011 Teaching assistant: Advanced Option Pricing - Prof. Torben Andersen (PhD Course) University of Lugano Teaching assistant: Capital Markets - Prof. Francesco Franzoni (Master Course) Teaching assistant: Financial Theory - Prof. Giovanni Barone-Adesi (Bachelor Course) University of Lugano Teaching assistant: Capital Markets - Prof. Giovanni Barone-Adesi (Master Course) Teaching assistant: Financial Theory - Prof. Giovanni Barone-Adesi (Bachelor Course) ATTENDED CONFERENCES - Parametric Inference and Dynamic State Recovery from Option Panels EFA, Copenhagen (August 2012) NBER, Boston (July 2012)* SIAM, Minneapolis (July 2012) North American Econometrics Society, Evanston (June 2012) SoFiE, Oxford (June 2012)* SITE, Stanford (June 2011) - Realizing Smiles: Option Pricing with Realized Volatility SoFiE, Chicago (June 2011) Florence (June 2010) 8th Swiss Doctoral Workshop, Gerzensee (June 2009) - Valuing Modularity as a Real Option Real Option Conference, Braga -Porto- and Santiago -Spain- (June 2009) - Barrier Option Pricing Using Adjusted Transition Probabilities X Workshop on Financial Crises and Quantitative Finance, Milan (January 2009) 7th Swiss Doctoral Workshop, Gerzensee (June 2008) 25th Erasmus Conference, Rottherdam (May 2007)* * = presented by a coauthor.

3 p. 3 REFEREEING EXPERIENCE Computational Economics International Journal of Information Technology & Decision Making International Journal of Production Economics Journal of Banking and Finance Journal of Econometrics Journal of Futures Market Production and Operations Management HONORS Research Grant: Swiss National Fund ( CHF) 2010 Research Grant: Banca del Ceresio ( CHF) 2005 COMPUTING SKILLS Experienced in: Matlab, C/C++, Mathematica, Sas, Sql, MySql. Italian: Mother tongue LANGUAGES English: Fluent REFERENCES Torben Andersen Nathan S. and Mary P. Sharp Distinguished Professor of Finance Kellogg School of Management Northwestern University 2001 Sheridan Road Evanston, IL t-andersen@kellogg.northwestern.edu Giovanni Barone-Adesi Professor of Finance University of Lugano and Swiss Finance Institute Institute of Finance Via G. Buffi 13, 6900 Lugano baroneg@lu.unisi.ch Andrea Gamba Associate Professor in Finance University of Warwick Coventry CV4 7AL, UK andrea.gamba@wbs.ac.uk Viktor Todorov Professor of Finance Kellogg School of Management Northwestern University 2001 Sheridan Road Evanston, IL v-todorov@kellogg.northwestern.edu

4 p. 4 PUBLICATIONS IN PEER REVIEWED JOURNALS Realizing Smiles: Option Pricing with Realized Volatility, with F. Corsi and D. La Vecchia. Journal of Financial Economics, forthcoming, We develop a discrete-time stochastic volatility option pricing model, which exploits the information contained in high frequency data. The Realized Volatility (RV) is used as a proxy of the unobservable log-returns volatility. We model its dynamics by a simple but effective long-memory process: The Leverage Heterogeneous Auto-Regressive Gamma (HARGL) process. The discrete-time specification and the use of the RV allow to easily estimate the model using observed historical data. Assuming a standard exponentially affine stochastic discount factor, we obtain a fully analytic change of measure. An extensive empirical analysis of S&P 500 index options illustrates that our approach significantly outperforms competing time-varying (i.e. GARCH-type) and stochastic volatility pricing models. The pricing improvement can be ascribed to: (i) The direct use of the RV, which provides a precise and fast adapting measure of the unobserved underlying volatility; (ii) The specification of our model, which is able to accurately reproduce the implied volatility term structure. Valuing Modularity as a Real Options, with A. Gamba Management Science, 55(11), November We provide a general valuation approach for capital budgeting decisions involving the modularization in the design of a system. Within the framework developed by Baldwin and Clark (2000), we implement a valuation approach using a numerical procedure based on the Least Squares Monte Carlo method proposed by Longstaff and Schwartz (2001). The approach is accurate, general and flexible. Barrier Option Pricing with Adjusted Transition Probabilities, with G. Barone-Adesi and J. Theal Journal of Derivatives, 16(2), winter In the existing literature on barrier options much effort has been exerted to ensure convergence through placing the barrier in close proximity to, or directly onto, the nodes of the tree lattice. For a variety of barrier option types we show that such a procedure may not be a necessary prerequisite to achieving accurate option price approximations. Using the Kamrad and Ritchken (1991) trinomial tree model we show that with a suitable transition probability adjustment our probability adjusted model exhibits convergence to the barrier option price. We study the convergence properties of several option types including exponential barrier options, single linear time-varying barrier options, double linear timevarying barriers options and Bermuda options. For options whose strike price is close to the barrier we are able to obtain numerical results where other models and techniques typically fail. Furthermore, we show that it is possible to calculate accurate option price approximations with minimal effort for options with complicated barriers that defeat standard techniques. In no single case does our method require a repositioning of the pricing lattice nodes. WORKING PAPERS Parametric Inference and Dynamic State Recovery from Option Panels, with T. Andersen and V. Todorov (submitted). We develop a new parametric estimation procedure for option panels observed with error which relies on asymptotic approximations assuming an ever increasing set of observed option prices in the moneynessmaturity (cross-sectional) dimension, but with a fixed time span. We develop consistent estimators of the parameter vector and the dynamic realization of the state vector that governs the option price dynamics. The estimators converge stably to a mixed-gaussian law and we develop feasible estimators for the limiting variance. We provide semiparametric tests for the option price dynamics based on the

5 p. 5 distance between the spot volatility extracted from the options and the one obtained nonparametrically from high-frequency data on the underlying asset. We further construct new formal tests of the model fit for specific regions of the volatility surface and for the stability of the risk-neutral dynamics over a given period of time. A large-scale Monte Carlo study indicates that the inference procedures work well for empirically realistic model specifications and sample sizes. In an empirical application to S&P 500 index options we extend the popular double-jump stochastic volatility model to allow for time-varying risk premia of extreme events, i.e., jumps, as well as a more flexible relation between the risk premia and the level of risk. We show that both extensions provide a significantly improved characterization, both statistically and economically, of observed option prices. Risk Premia Implied from Option Panels, with T. Andersen and V. Todorov. We study the dynamic relation between aggregate stock market risks and risk premia via an exploration of the time series of equity-index option surfaces. The analysis is based on estimating a general parametric asset pricing model for the risk-neutral equity market dynamics using a panel of options on the S&P 500 index, while remaining fully nonparametric about the actual evolution of market risks. We find that the risk-neutral jump intensity, which controls the pricing of left tail risk, cannot be spanned by the market volatility (and its components), so an additional factor is required to account for its dynamics. This tail factor has no incremental predictive power for future equity return volatility or jumps beyond what is captured by the current and past level of volatility. In contrast, the novel factor is critical in predicting the future market excess returns over horizons up to one year, and it explains a large fraction of the future variance risk premium. We contrast our findings with those implied by structural asset pricing models that seek to rationalize the predictive power of option data. Relative to those studies, our findings suggest a wider wedge between the dynamics of equity market risks and the corresponding risk premia with the latter typically displaying a far more persistent reaction following market crises. WORK IN PROGRESS Volatility Dynamics and the Term Structure of the Variance Risk Premium, with M.T. Gonzalez-Perez. We analyze the term structure of the variance risk premium (VRP). We find that in normal times the VRP is higher over longer horizons, but in times of crisis investors require higher compensation over shorter horizons. We show that the VRP term structure dynamics is driven by its correlation with two factors that capture economic uncertainty over different frequencies. The first factor is the difference between BBB and AAA corporate bond yields, which is related to the business cycle. The second factor is the difference between the bank prime loan rate (BPLR) and the three month T-Bill yield, which reacts to short-lived economic uncertainty. We further show that both the short-term VRP and the VRP term structure predict future excess returns. Our empirical study combines the information contained in the Chicago Board Options Exchange (CBOE) volatility index (VIX) constructed over different horizons and high frequency observations on the S&P 500 index futures.