The paper advances the log-generalized gamma distribution as a suitable generator of conditional skewness. Based on the NYSE composite daily returns an asMA-asQGARCH model along with skewness dynamics is estimated. The results indicate a skewness that varies between sizeable negative skewness...

The analysis of diffusion processes in financial models is crucially dependent on the form of the drift and diffusion coefficient functions. A methodology is proposed for estimating and testing coefficient functions for ergodic diffusions that are not directly observable. It is based on...

This paper offers an improvement to the trade-to-trade model for event studies. While the trade-to-trade model of Maynes and Rumsey (1993) addresses the problem of thin trading by eliminating periods in which no trading is recorded, the proposed improvement addresses the influence of zero-value...

Through the use of regime-switching models, recent empirical research has essentially demonstrated that the dynamics of stock returns depend on the state of one stock market. The present paper extends this analytical framework by allowing the dynamics of returns to depend on the joint-states of...

We consider a Volume Weighted Average Price (VWAP) trading algorithm in which instead of following the static curve passively, the algo may adjust its participation rate in each interval. We propose a framework in which the adjustment only makes use of the expected value of the price...

In this article we introduce a linear quadratic volatility model with co-jumps and show how to cal- ibrate this model to a rich dataset. We apply GMM and more specifically match the moments of realized power and multi-power variations, which are obtained from high-frequency stock market data....

We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an...

Market liquidity is a latent and dynamic variable. Building on Cont et al. (2014), we propose a dynamical price impact model at high-frequency, in which price impact is a product of daily, diurnal, and autoregressive stochastic intraday com- ponents. The model is estimated using a Kalman filter...

We propose a novel way to assess information processing in a complex environment of market fragmentation. We take a different angle from the price discovery literature, and investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We show...

In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Ito semimartingales, and discuss how to measure liquidity risk using high frequency financial data. In particular, we investigate the impact of non-stationary microstructure...