We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting. First, we show that taking into account multiple levels of the order book when defining order book imbalance leads to higher explanatory power for the contemporaneous price...

The pricing accuracy and pricing performance of local volatility models crucially depends on absence of arbitrage in the implied volatility surface: an input implied volatility surface that is not arbitrage-free invariably results in negative transition probabilities and/ or negative local...

Modeling volatility during a financial crisis where massive shocks are generated presents an ideal environment for investigating the dynamics of volatility during periods of extreme fluctuations for comparison with volatility during more tranquil periods. The objective of this paper is to study...

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IV S). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is...

Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...

We propose a nonparametric method to study which characteristics provide incremental information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large...

We propose a nonparametric method to study which characteristics provide incremental information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large...

We propose an automatic machine-learning system to forecast realized volatility for S&P 100 stocks using 118 features and five machine learning algorithms. A simple average ensemble model combining all learning algorithms delivers extraordinary performance across forecast horizons, and the...

We introduce a functional volatility process for modeling volatility trajectories for high frequency observations in financial markets. The observed volatility of returns results from this underlying process in combination with a multiplicative white noise. The proposed representation enables us...

This paper revisits the fractional cointegrating relationship between ex-ante implied volatility and ex-post realized volatility. We argue that the concept of corridor implied volatility (CIV) should be used instead of the popular model-free option-implied volatility (MFIV) when assessing the...