# Research Interests:

###### Theoretical an Empirical Asset Pricing

###### Derivative Pricing in Incomplete Markets

###### Risk Management

###### Numerical Methods

# Publications:

“Valuing catastrophe derivatives under limited diversification: A stochastic dominance approach” (with S. Perrakis), ** Journal of Banking and Finance**, 2013.

# Working Papers:

“Is Idiosyncratic Volatility Risk Priced? Evidence from the Physical and Risk-Neutral Distributions”, 2017.

- Winner of Best PhD Student Paper Award, NFA, 2014

We use simultaneous data from equity, index and option markets in order to estimate a single factor market model in which idiosyncratic volatility is allowed to be priced. We model the index dynamics’ P-distribution as a mean-reverting stochastic volatility index model as in Heston (1993), and the equity returns as single factor models with stochastic idiosyncratic volatility terms. We derive theoretically the option-implied underlying assets’ Q-distributions and estimate the parameters of both P- and Q-distributions using a joint likelihood function. We document the existence of a common factor structure in option implied idiosyncratic variances. We show that the average idiosyncratic variance, which proxies for the common factor, is priced in the cross section of equity returns, and that it reduces the pricing error when added to the Fama-French model. We find that the P- and Q- idiosyncratic volatilities differ, and we estimate the price of this idiosyncratic volatility risk, which turns out to be always significantly different from zero for all the stocks in our sample. Further, we show that the idiosyncratic volatility risk premiums are not explained by the usually equity risk factors. Finally, we explore the implications of our results for the estimates of the conditional equity betas.

“Catastrophe Derivatives and Reinsurance Contracts: An Incomplete Market Approach” (with S. Perrakis), 2016.** Journal of Futures Markets**. 2018.

We apply a recently developed new approach to the pricing of catastrophe derivatives to the valuation of a reinsurance contract. Since the payoff of the contract has the form of a vertical spread, our methodology is also applicable to the valuation of such spreads in other markets. We do not assume a fully diversifiable CAT event risk, but we assume, instead, that there exists a class of investors whose diversified portfolio’s return is negatively affected by the occurrence of the CAT event. We derive bounds for a reinsurance contract with a non-convex payoff using recent results from the option pricing literature. We also show that these bounds are tighter than the ones arising from a combination of the bounds on the options forming the spread. We adopt a recursive discrete time approach, as it is more realistic for the class of problems that we examine, and we value numerically the reinsurance contract with real data from hurricane landings in Florida. Last, we show that the limiting pricing kernels defining the bounds for derivative assets of this type are crucially dependent on the shape of the derivative’s payoff function and do not have closed form expression.

“Beta Risk in the Cross-Section of Equities”

(with P. Christoffersen, M. Fournier, and C. Gourieroux), 2017.

We develop a bivariate stochastic volatility model that allows for dynamic market exposure. The expected return on a stock depends on beta’s co-movement with the stochastic discount factor and deviates from the standard security market line when beta risk is priced. When estimating the model on returns and options for a large number of firms we find that allowing for beta risk helps explain the expected returns on low and high beta stocks that are challenging for standard factor models. Overall, we find strong evidence that accounting for beta risk results in better model fit.