We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out...

We present several estimates of measures of risk amongst the most well-known, using both high and low frequency data. The aim of the article is to show which lower frequency measures can be an acceptable substitute to the high precision measures, when transaction data is unavailable for a long...

The explosion of algorithmic trading has been one of the most prominent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies are...

Intraday minute-by-minute data from the Tokyo, Shanghai, and Shenzhen stock exchanges from January 7, 2008, to January 23, 2009, are analyzed to investigate the interaction between the Japanese and Chinese stock markets. We focus on two windows of time during which all three stock exchanges...

High-frequency financial data are useful for studying the statistical properties of asset returns at lower frequencies, and they have been widely used to study various market microstructure related issues. However, most studies to date have been concentrated on markets in developed economies...

In this study, we use the 'heterogeneous autoregressive' (HAR) model and replace all squared returns with a squared range to estimate realized range-based volatility (RRV) forecasts for oil futures prices. Our findings demonstrate that the HAR-RRV models, involving volatility measures with a...

We study the impact of volatility on intraday serial correlation, at time scales of less than 20 minutes, exploiting a data set with all transactions on SPX500 futures from 1993 to 2001. We show that, while realized volatility and intraday serial correlation are linked, this relation is driven...