This paper studies the theoretical properties and implementation issues with the risk-neutral density (RND) estimator based on the mixture-of-lognormal (MLN) approach. First, we establish the consistency and asymptotic normality of the MLN method under the correct choice of mixtures and propose...

We extend the classic ''martingale-plus-noise'' model for high-frequency returns to accommodate an error correction mechanism and endogenous pricing errors. It is motivated by (i) novel empirical evidence documenting that microstructure noise exhibits frequently changing patterns of serial...

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional parameters, and show that it can be reliably estimated via the Stochastic Approximation Expectation-Maximization algorithm. Applying our model to high-frequency transaction data, we...

In this paper we examine the relative importance of trading volume, bid-ask spread, order flow, order imbalance, total quote depth, quote depth difference and trading intensity for high-frequency volatility estimation. By using a best subset regression approach, we fi nd that contemporaneous...

We propose a new nonparametric test to determine whether finite-activity jumps are present in a discretely observed price process. For a univariate Itô semimartingale, we introduce the concept of censored increments for observations recursively sampled at exit times with respect to a symmetric...

This paper develops the idea of renewal time sampling, a novel sampling scheme constructed from stopping times of semimartingales. Based on this new sampling scheme we propose a class of volatility estimators named renewal based volatility estimators. In this paper we show that: (1) The spot...

Estimation noise is a well-known issue in empirical portfolio modelling. Estimated weights are known to have huge standard errors and bad predictive quality, which often results in an inferior out-of-sample portfolio performance compared to simple alternatives. Most of the recent literature...