10h30 - 10h55
Idiosyncratic Jump Risk Matters: Evidence from Equity Returns and Options
This paper sheds new light on the relationship between idiosyncratic risk and equity returns by exploiting the richness of option data. To this end, we develop a jump-diffusion model in which a firm’s systematic and idiosyncratic risk have both a normal and a tail component. We show that the contribution of idiosyncratic risk arises exclusively from the jump risk component.
10h55 - 11h20
Solving Optimal Portfolio Choice Problems with Forward Dynamic Programming
We develop a forward dynamic programming algorithm to solve optimal portfolio choice problems with CRRA utility function and finite horizon. The method is based on simulations and thus offers great flexibility for modeling the returns. It is a “forward” method in that the choice of decision for any scenario is effectively done from time 0 to the end of horizon, in opposition to traditional D.P. The resulting algorithm is an application of artificial intelligence techniques in Financial Engineering, and thus establishes a bridge between the two fields. Furthermore, there are indications that the method could naturally be extended to include other features such as transaction costs and inter-temporal consumption.
11h20 - 11h45
Dynamic programming and parallel computing for valuing two-dimensional financial derivatives
We propose a dynamic program coupled with finite elements for valuing two-dimensional American-style options. To speed-up our procedure, we use parallel computing at every step of the recursion. Our model is flexible because it accommodates a large family of option con tracts signed on two underlying assets that move according to a lognormal vector process. The same procedure can be adapted to accommodate a larger family of derivative contracts and state-process dynamics.
Our numerical experiments show convergence and efficiency, positioning our method as a viable alternative to traditional methodologies based on trees, finite differences, and Monte Carlo simulation.
11h45 - 12h10
Extracting Latent States from High Frequency Option Prices
We propose the realized option variance as a new observable variable to integrate high frequency option prices in the inference of option pricing models. Using simulation and empirical studies, this paper documents the incremental information offered by this realized measure. Our empirical results show that the information contained in the realized option variance improves the inference of model variables such as the instantaneous variance and variance jumps of the S&P 500 index. Parameter estimates indicate that the risk premium breakdown between jump and diffusive risks is affected by the omission of this information.