Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term...

The integer-valued moving average model is advanced to model the number of transactions in intra-day data of stocks. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment...

This paper deals with dynamic term structure models (DTSMs) and proposes a new way to handle the limitation of the classical affine models. In particular, the paper expands the flexibility of the DTSMs by applying generalized Brownian motions with dependent increments as the governing force of...

The paper undertakes a non-parametric analysis of the very high frequency movements in stock market volatility using very finely sampled data on the Samp;P VIX index compiled by the CBOE. The data suggest that stock market volatility is best described as a pure jump process without a continuous...

We propose a tree-structured heterogeneous autoregressive (tree-HAR) process as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors' dependent regime shifts in the...

This paper presents two classes of tick-by-tick covariance estimators adapted to the case of rounding in the price time stamps to a frequency lower than the typical arrival rate of tick prices. We investigate, through Monte Carlo simulations, the behavior of such estimators under realistic...

The econometric literature of high frequency data usually relies on moment estimators which are derived from assuming local constancy of volatility and related quantities. We here show that this first order approximation is not always valid if used naively. We find that such approximations...

We develop a portfolio risk model that uses high-frequency data to forecast the loss surface, which is the set of loss distributions at future time horizons. Our model uses a fully automated, semi-parametric fitting procedure that has its basis in extreme value statistics. We take account of...

Time reversal invariance can be sumarised as follows: no difference can be measured if a sequence of events is run forward or backward in time. Because price time series are dominated by a randomness that hides possible structures and orders, the existence of time reversal invariance requires...

Complex interactions between fundamentals and liquidity during unstable periods in financial markets are succinctly modeled with coordination games. We propose a flexible framework to estimate such a model and use the efficient method of moments as estimation procedure. We illustrate the model...