Nonlinearity in High-Frequency Financial Data and Hierarchical Models

Estimation of Continuous–Time Financial Models Using High–Frequency Data

Analysis of High Frequency Financial Data

High frequency data in financial markets: Issues and applications

The development of high frequency data bases allows for empirical investigations of a wide range of issues in the financial markets. In this paper, we set out some of the many important issues connected with the use, analysis, and application of high-frequency data sets. These include the effects of market structure on the availability and interpretation of the data, methodological issues such ...

Time-dependent scaling patterns in high frequency financial data

We measure the influence of different time-scales on the intraday dynamics of financial markets. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures of complexity: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices ...

Unified Discrete-Time and Continuous-Time Models and Statistical Inferences for Merged Low-Frequency and High-Frequency Financial Data

This paper introduces a unified model, which can accommodate both a continuoustime Itô process used to model high-frequency stock prices and a GARCH process employed to model low-frequency stock prices, by embedding a discrete-time GARCH volatility in its continuous-time instantaneous volatility. This model is called a unified GARCH-Itô model. We adopt realized volatility estimators based on hi...