Abstract Our objective is to identify the trading strategy that would allow an investor to take advantage of "excessive" stock price volatility and "sentiment" fluctuations. We construct a general-equilibrium model of sentiment. In it, there are two classes of agents and stock prices are excessively volatile because one class is overconfident about a public signal. As a result, this class of overconfident agents changes its expectations too often, sometimes being excessively optimistic, sometimes being excessively pessimistic. We determine and analyze the trading strategy of the rational investors who are not overconfident about the signal. We find that, because overconfident traders introduce an additional source of risk, rational investors are deterred by their presence and reduce the proportion of wealth invested into equity except when they are extremely optimistic about future growth. Moreover, their optimal portfolio strategy is based not just on a current price divergence but also on their expectation of future sentiment behavior and a prediction concerning the speed of convergence of prices. Thus, the portfolio strategy includes a protection in case there is a deviation from that prediction. We find that long maturity bonds are an essential accompaniment of equity investment, as they serve to hedge this "sentiment risk." JEL Codes: C11, D58, D84, D91

We present an extension of the stochastic volatility equity models by a stochastic Hull-White interest rate component. We place this system of stochastic differential equations in the class of affine jump diffusion- linear quadratic jump-diffusion processes (Duffie, Pan and Singleton [11], Cheng and Scaillet [8]) so that the pricing of European products can be efficiently done within the Fourier cosine expansion pricing framework [12]. We also apply the model to price some hybrid structured derivatives, which combine the different asset classes: equity and interest rate.

This paper introduces a new class of matrix-valued affine jump diffusions that are convenient for modeling multivariate risk factors in many financial and econometric problems. We provide an analytical transform analysis for this class of models, leading to an analytical treatment of a broad class of multivariate valuation and econometric problems. Examples of potential applications include fixed-income problems with stochastically correlated risk factors and default intensities, multivariate option pricing with general volatility and correlation leverage structures, and dynamic portfolio choice with jumps in returns, volatilities or correlations.

In recent years the financial world has focused on accurate pricing of exotic and hybrid products that are based on a combination of underlyings from different asset classes. In this paper we present an extension of the stochastic volatility models by a stochastic Hull-White interest rate component. It is our goal to include this system of stochastic differential equations in the class of affine jump diffusion- linear quadratic jump-diffusion processes (Duffie, Pan and Singleton [11], Cheng and Scaillet [8]) so that the pricing of European products can be efficiently done within the pricing framework of Carr-Madan [7].

Abstract We introduce a new analytical approach to price American options. Using an explicit and intuitive proxy for the exercise rule, we derive tractable pricing formulas using a short-maturity asymptotic expansion. Depending on model parameters, this method can accurately price options with time-to-maturity up to several years. The main advantage of our approach over existing methods lies in its straightforward extension to models with stochastic volatility and stochastic interest rates. We exploit this advantage by providing an analysis of the impact of volatility mean-reversion, volatility of volatility, and correlations on the American put price.

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Abstract We develop a flexible multi-factor stochastic model with three diffusive and three spike regimes, for daily spot and forward electricity. The model captures various stylized features of power prices, including mean reversion and seasonal patterns, and short-lived spikes. We estimate parameters through a practical two-step procedure, that combines pre-calibration of deterministic elements and spikes, and state-space estimation of diffusive factors. We use several results on affine jump diffusions to combine the spike and diffusive components, and to provide convenient closed-form solutions for important power derivatives. We also propose a simple nonparametric model for hourly spot prices, based on hourly profile sampling from historical data. This model can reproduce complicated intraday patterns. We illustrate the performance of the daily and hourly models using data from the Amsterdam Power Exchange.

JOB MARKET PAPER Contagion refers to a situation where return and volatility spread from one market over other fundamentally unrelated markets. I build a general equilibrium model where contagion arises from investors ’ counter-cyclical attention to news. In the model, news provide information on market fundamentals and are used to estimate them. As a negative shock hits one market, investors pay more attention so that news transmits more rapidly to the estimation of the fundamental driving that market. This in turn makes both the estimation of that fundamental and the equilibrium discount rates more volatile. Because equilibrium discount rates determine market prices, cross-market correlations and market volatilities spike. This contagion phenomenon generates a positive co-movement among market volatilities, a well-documented empirical fact that leading asset pricing models cannot explain. Furthermore, the predicted correlation among short term claims is smaller but more volatile than that