Razvan Sufana

Department of Economics

Associate Professor

Office: Vari Hall, 1030
Phone: 416-736-2100 Ext: 66065
Emailrsufana@yorku.ca
Primary websitewww.yorku.ca/rsufana/

I am an Associate Professor in the Department of Economics at York University. My research interests are in the area of financial econometrics. In particular, I have been working on multivariate stochastic volatility models and their applications to derivative pricing and the term structure of interest rates. I obtained my PhD from the University of Toronto.

More...

Degrees

PhD Economics and Finance, University of Toronto
MA Economics, McMaster University
BA Computer Science, West University of Timisoara, Romania


Research Interests

Economics , Financial and Monetary Systems , Derivative Pricing

Selected Publications

Sufana, R. "A Classification of Two Factor Affine Diffusion Term Structure Models," with Christian Gourieroux, Journal of Financial Econometrics, 2006.

All Publications

Journal Articles

Sufana, R. "A Classification of Two Factor Affine Diffusion Term Structure Models," with Christian Gourieroux, Journal of Financial Econometrics, 2006.

Conference Papers

Sufana, R. Econometric Society World Congress, London, U.K., August 2005. Presented "Derivative Pricing with Multivariate Stochastic Volatility: Application to Credit Risk."

Sufana, R. Canadian Econometrics Study Group: Financial Econometrics, Toronto, September 2004 Presented "The Wishart Autoregressive Process of Multivariate Stochastic Volatility".

Forthcoming

Sufana, Razvan. "The Wishart Autoregressive Process of Multivariate Stochastic Volatility," with Christian Gourieroux and Joann Jasiak, Journal of Econometrics, forthcoming.

Other

Discrete Time Wishart Term Structure Models
(with C. Gourieroux)
Journal of Economic Dynamics and Control , 35(6), 2011, pp. 815-824
Abstract: This paper reveals that the class of Affine Term Structure Models (ATSMs) introduced by Duffie and Kan (1996) is larger than previously considered in the literature. In the framework of risk factors following a Wishart autoregressive process, we define the Wishart Term Structure Model (WTSM) as an extension of a subclass of Quadratic Term Structure Models (QTSMs), derive simple parameter restrictions that ensure positive bond yields at all maturities, and observe that the usual constraint on affine processes requiring that the volatility matrix be diagonal up to a path independent linear invertible transformation can be considerably relaxed.
[go to paper]

Derivative Pricing with Wishart Multivariate Stochastic Volatility
(with C. Gourieroux)
Journal of Business and Economic Statistics , 28(3), 2010, pp. 438-451
Abstract: This paper deals with the pricing of derivatives written on several underlying assets or factors satisfying a multivariate model with Wishart stochastic volatility matrix. This multivariate stochastic volatility model leads to a closed-form solution for the conditional Laplace transform, and quasi-explicit solutions for derivative prices written on more than one asset or underlying factor. Two examples are presented: (i) a multiasset extension of the stochastic volatility model introduced by Heston (1993), and (ii) a model for credit risk analysis that extends the model of Merton (1974) to a framework with stochastic firm liability, stochastic volatility, and several firms. A bivariate version of the stochastic volatility model is estimated using stock prices and moment conditions derived from the joint unconditional Laplace transform of the stock returns.
[go to paper]

The Wishart Autoregressive Process of Multivariate Stochastic Volatility
(with C. Gourieroux and J. Jasiak)
Journal of Econometrics , 150(2), 2010, pp. 167-181
Abstract: The Wishart Autoregressive (WAR) process is a dynamic model for time series of multivariate stochastic volatility. The WAR naturally accommodates the positivity and symmetry of volatility matrices and provides closed-form non-linear forecasts. The estimation of the WAR is straightforward, as it relies on standard methods such as the Method of Moments and Maximum Likelihood. For illustration, the WAR is applied to a sequence of intraday realized volatility-covolatility matrices from the Toronto Stock Market (TSX).
[go to paper]

Upcoming Courses

TermCourse NumberSectionTitleType 
Fall 2017 AP/ECON3480 3.0  Introductory Statistics for Economists II LECT  
Fall 2017 AP/ECON4140 3.0  Financial Econometrics LECT  


I am an Associate Professor in the Department of Economics at York University. My research interests are in the area of financial econometrics. In particular, I have been working on multivariate stochastic volatility models and their applications to derivative pricing and the term structure of interest rates. I obtained my PhD from the University of Toronto.

Degrees

PhD Economics and Finance, University of Toronto
MA Economics, McMaster University
BA Computer Science, West University of Timisoara, Romania

Research Interests:

Economics , Financial and Monetary Systems , Derivative Pricing

All Publications

Journal Articles

Sufana, R. "A Classification of Two Factor Affine Diffusion Term Structure Models," with Christian Gourieroux, Journal of Financial Econometrics, 2006.

Conference Papers

Sufana, R. Econometric Society World Congress, London, U.K., August 2005. Presented "Derivative Pricing with Multivariate Stochastic Volatility: Application to Credit Risk."

Sufana, R. Canadian Econometrics Study Group: Financial Econometrics, Toronto, September 2004 Presented "The Wishart Autoregressive Process of Multivariate Stochastic Volatility".

Forthcoming

Sufana, Razvan. "The Wishart Autoregressive Process of Multivariate Stochastic Volatility," with Christian Gourieroux and Joann Jasiak, Journal of Econometrics, forthcoming.

Other

Discrete Time Wishart Term Structure Models
(with C. Gourieroux)
Journal of Economic Dynamics and Control , 35(6), 2011, pp. 815-824
Abstract: This paper reveals that the class of Affine Term Structure Models (ATSMs) introduced by Duffie and Kan (1996) is larger than previously considered in the literature. In the framework of risk factors following a Wishart autoregressive process, we define the Wishart Term Structure Model (WTSM) as an extension of a subclass of Quadratic Term Structure Models (QTSMs), derive simple parameter restrictions that ensure positive bond yields at all maturities, and observe that the usual constraint on affine processes requiring that the volatility matrix be diagonal up to a path independent linear invertible transformation can be considerably relaxed.
[go to paper]

Derivative Pricing with Wishart Multivariate Stochastic Volatility
(with C. Gourieroux)
Journal of Business and Economic Statistics , 28(3), 2010, pp. 438-451
Abstract: This paper deals with the pricing of derivatives written on several underlying assets or factors satisfying a multivariate model with Wishart stochastic volatility matrix. This multivariate stochastic volatility model leads to a closed-form solution for the conditional Laplace transform, and quasi-explicit solutions for derivative prices written on more than one asset or underlying factor. Two examples are presented: (i) a multiasset extension of the stochastic volatility model introduced by Heston (1993), and (ii) a model for credit risk analysis that extends the model of Merton (1974) to a framework with stochastic firm liability, stochastic volatility, and several firms. A bivariate version of the stochastic volatility model is estimated using stock prices and moment conditions derived from the joint unconditional Laplace transform of the stock returns.
[go to paper]

The Wishart Autoregressive Process of Multivariate Stochastic Volatility
(with C. Gourieroux and J. Jasiak)
Journal of Econometrics , 150(2), 2010, pp. 167-181
Abstract: The Wishart Autoregressive (WAR) process is a dynamic model for time series of multivariate stochastic volatility. The WAR naturally accommodates the positivity and symmetry of volatility matrices and provides closed-form non-linear forecasts. The estimation of the WAR is straightforward, as it relies on standard methods such as the Method of Moments and Maximum Likelihood. For illustration, the WAR is applied to a sequence of intraday realized volatility-covolatility matrices from the Toronto Stock Market (TSX).
[go to paper]


Teaching:

Upcoming Courses

TermCourse NumberSectionTitleType 
Fall 2017 AP/ECON3480 3.0  Introductory Statistics for Economists II LECT  
Fall 2017 AP/ECON4140 3.0  Financial Econometrics LECT