eMail: ksosunov(at)nes.ru

Education

1997 – 2000 Australian National University, Canberra, Australia
Ph.D. in economics, Thesis submitted July 2000
Ph.D. topic “Essays in the Modeling of Cycles”
Courses taken include:

Macroeconomics, Microeconomics, Dynamic Econometrics, Economic Growth, International Trade, Econometric Modeling

1994 –1996 New Economic School, Moscow, Russia
M.A. in economics. G.P.A. 4.4 (out of 5)
Master’s thesis topic “Testing the Rational Expectations Hypothesis on the
Russian Short-term Treasury Bills Market”

1988 – 1993 Moscow State University, Department of Mathematics, Moscow, Russia
Diploma (equivalent to M.Sc) in mathematics and applied mathematics
Specialized in functional analysis and theory of functions
G.P.A. 4.6 (out of 5)

Fields

Macroeconomics, Business cycle theory, Asset pricing

Publications

A Simple Framework for Analyzing Bulls and Bears market. (joint with A. Pagan. Journal of Applied Econometrics, №2, 2002. Bull and bear markets are a common way of describing cycles in equity prices. To fully describe stock market cycles one would need to know the data generating process (DGP) for equity prices. We begin with a definition of bull and bear markets developed by my co-author in his Walras-Bowley lecture and use the algorithm given there to sort a given time series of equity prices into periods that can be designated as bull and bear markets. The rule to do this is then studied analytically and it is shown that bull and bear market characteristics depend upon the DGP for capital gains. By simulation methods we examine a number of DGP's that are known to fit the data quite well - random walks, GARCH models, and models with duration dependence. We find that a pure random walk provides as good an explanation of bull and bear markets as the more complex statistical models. In the final section of the paper we look at some asset pricing models that appear in the literature from the viewpoint of their success in producing bull and bear markets which resembe those in the data.

Analyzing Indeterminacies in Real Business Cycle Model with Money. Journal of Money, Credit and Banking, May 2000. In this paper, written in response to R. Farmer’s (1997) paper “Money in a real business cycle model”, I show that, although the model constructed in that paper may potentially lead to multiple stationary equilibria (indeterminacy) in the real sector, the parameterization employed by the author does not. Instead, a unique saddle point equilibrium with impulse response functions similar to those of standard real business cycles theories is obtained. By relaxing the assumption of a constant returns to scale production function, a parameterization, similar to the author’s, does indeed create indeterminacy and impulse response functions match the data in similar way to Farmer’s paper.

Unpublished Papers

Real Business Cycles Model with Changing Sentiments (Current version: January 2000). In this paper the modification of the real business cycles model in which risk aversion parameter of agents’ utility function follows bivariate markov chain is developed and estimated using simulated VAR. The model’s ability to replicate properties of US quarterly data is compared with that of the standard real business cycles model. The main finding is that the model with markov switching performs at least well as the standard model. The model with markov switching also matches some features of the data which the standard RBC model is unable to match.

Nominal Rigidities, Real Rigidities and Output Persistence (Current version: September 1999). In this paper I analyzed several dynamic models aimed at explaining price stickiness and persistency of output response to the nominal monetary shock. My findings are: first, neither new-Keynesian models with nominal rigidities nor neoclassical flexible prices model cannot produce persistence in output unless the model has a significant degree of real rigidity. Second, the conventional way of modeling the labor market as a spot market with labor demand equating the real wage to marginal product of labor, and labor supply derived from the consumer’s maximization of a utility function that depends on consumption and leisure does not allow the model to exhibit enough real rigidity to generate persistent output.

Response of Output to the Monetary Shock in a DGE Model with Nominal Rigidities (Current version: July 1999). In this paper I show that a model extensively used in the New Keynesian literature which features monopolistic competition with increasing returns to scale and a staggered-price setting rule results in monetary neutrality when values of the markup and the returns to scale parameter are equal – a fact which is not acknowledged in the literature. I also show that, when the returns to scale parameter is not equal to the markup, output response to a monetary shock is persistent for large values of returns to scale (>1.7) and this number can be lowered to 1.35 by allowing markups to be different in different sectors, thereby permitting the average markup to be countercyclical, i.e. larger during contractions and smaller during expansions.

Testing the Rational expectations Hypothesis on Russian Short-term Treasury Bills market. (Master’s thesis, May, 1996). This paper tests the rational expectations hypothesis using data on the Russian Treasury bills market. The results are that the rational expectations hypothesis holds both in the sense that forecast errors are uncorrelated over time and that agents’ expectations do not differ statistically from those suggested by an estimated ARIMA model. The result is robust in the sense that it holds for two different time-period versions of the model.

Professional Experience

2003 - present Institute for Open Economy
Leading expert

2003 - present New Economic School, Moscow, Russia
Lecturer

2000 – 2003 New Economic School, Moscow, Russia
Assistant professor

Jul 1996 – Feb 97
Jan – Apr 1998
Feb – Apr 1999 Ministry of Finance of the Russian Federation
Department of Macroeconomic Policy, Economic Expert Group

Related Skills

Languages: native Russian

Computer skills: all major spreadsheets, word processors and statistics package, some UNIX shell and Java programming, basic HTML

References: