This paper uses information on VIX to improve the empirical performance of GARCH models for pricing options on the S&P 500. In pricing multiple cross-sections of options, the models' performance can clearly be improved by extracting daily spot volatilities from the series of VIX rather than by...

Relatively little is known about the empirical performance of infinite-activity Levy jump models, especially with non-affine volatility dynamics. We use extensive empirical data sets to study how infinite-activity Variance Gamma and Normal Inverse Gaussian jumps with affine and non-affine...

This guide walks readers through backgrounds and potential financial applications of cloud computing. Detailed discussions on practical issues in implementation are included. The authors also provide several case studies of how massive financial computing problems can be processed with cloud...

According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint...

Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price...

Information arrivals may drive investors to require immediacy, generating sudden liquidity demand across multiple price levels in limit order books. We document significant intraday changes in stock limit order book characteristics and liquidity beyond the best levels around scheduled and...

Think about a situation, where a financial institution has multiple option positions, each written on a different underlying asset, and the unexpected arrival of market-wide news shakes the markets. In the case of such a market-wide news arrival, all the volatility models on different...

This paper proposes a new method for predicting jump arrivals in stock markets with high-frequency limit order book data. We introduce a new model architecture, based on Convolutional Long Short-Term Memory with attention, to apply time series representation learning with memory and to focus the...

This paper introduces a non-parametric framework to statistically examine how news events, such as company or macroeconomic announcements, contribute to the pre- and post-event jump dynamics of stock prices under the intraday seasonality of the news and jumps. We demonstrate our framework, which...