High frequency market microstructure noise estimates and liquidity measures

High Frequency Market Microstructure Noise Estimates and Liquidity Measures By

Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable characteristics of the underlying stocks and, in particular, to different financial...

Parametric Estimates of High Frequency Market Microstructure Noise as an Unsystematic Risk

Noise is essential for the existence of a liquid market, and if noise traders are not present in the market, the trade volume will drop severely and an important aspect of the market philosophy will be lost. However, these noise traders bring noise to the market, and the existence of noise in prices indicates a temporary deviation in prices from their fundamental values. In particular, high-fre...

Multi-scale representation of high frequency market liquidity

We introduce an event based framework mapping financial data onto a state based discretisation of time series. The mapping is intrinsically multi-scale and naturally accommodates itself with tick-by-tick data. Within this framework, we define an information theoretic quantity that characterises the unlikeliness of price trajectories and, akin to a liquidity measure, detects and predicts stress ...

Machine Learning for Market Microstructure and High Frequency Trading

In this chapter, we overview the uses of machine learning for high frequency trading and market microstructure data and problems. Machine learning is a vibrant subfield of computer science that draws on models and methods from statistics, algorithms, computational complexity, artificial intelligence, control theory, and a variety of other disciplines. Its primary focus is on computationally and...

Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data