12:00

Asset Returns with Self-exciting Jumps: Option Pricing and Time-varying Jump Risk Premium

Andrei Lalu (University of Amsterdam)

We develop a semi-closed form option pricing approach in the context of a parametric model for asset returns with clustered jumps. The stochastic jump intensity in the model self-excites as a result of jumps occurring, so the model can accommodate clusters of jumps, a phenomenon which is empirically relevant when markets are in turmoil. Assuming an arbitrage free pricing kernel, we develop a procedure to filter out the latent state variables and estimate model parameters via the generalized method of moments. The moment conditions are based on the model’s conditional characteristic function. Using a long time-series of S&P 500 options prices we estimate model parameters and conduct inference on the variance-and jump- risk premiums incorporated in the pricing kernel. We find strong evidence in favor of self-excitation in the jump intensity process for the S&P 500 index. Lastly, we find that the in-sample and out-of-sample option pricing performance of our model exceeds that of alternative models with time-varying jump intensity specifications.

12:45

Correlation Aggregation in High Frequency Financial Data

Yang Liu (University of Amsterdam)

Abstract: We present a new approach to correlation structure modelling using highfrequency measures. Several high frequency measures are derived through correlation aggregation in the framework of the generalized autoregressive score model (GAS) of Creal et al. (2011). The high frequency dynamic assumption enables our model to extract more information from intraday data and estimate intraday and daily conditional correlation simultaneously. Our empirical study on high frequency indexes data shows the superiority of our model over the daily GAS model augmented with a realized correlation measure, both in-sample and out-of-sample.