High-frequency Data In Finance Research Articles

The Analysis of High-Frequency Finance Data using ROOT

High-frequency financial market data is conceptually distinct from high energy physics (HEP) data. Market data is a time series generated by market participants, while HEP data is a set of independent events generated by collisions between particles. However, there are similarities within the data structure and required tools for data analysis, and both fields share a similar set of problems facing the increasing size of data generated. This paper describes some of the core concepts of financial markets, discusses the data similarities and differences with HEP, and provides an implementation to use ROOT, an open-source data analysis framework in HEP, with financial market data. This implementation makes it possible to take advantage of the rich set of features available in ROOT and extends research in finance.

Market Timing Signals with High-Frequency Finance Data for Practitioners Based on RRV Model and Momentum Effect

Market Timing Signals with High-Frequency Finance Data for Practitioners Based on RRV Model and Momentum Effect

SPECTRAL ANALYSIS OF EXCHANGE RATES

Using spectral analysis is very common in technical areas but rather unusual in economics and finance, where ARIMA and GARCH modeling are much more in use. To show that spectral analysis can be useful in determining hidden periodic components for high-frequency finance data as well, we use the example of foreign exchange rates

Forecasting High-Frequency Futures Returns Using Online Langevin Dynamics

Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency.

Causal relationship testing with applications to exchange rates

This paper undertakes two causality studies with exchange rate applications in a framework of Zero-Non-Zero (ZNZ) patterned Vector Error-Correction Modelling (VECM). The first study shows that money supply is a source of financial and economic influence on the Euro. The second gives evidence of support for Purchasing Power Parity (PPP) using monthly data between Japan and the USA. The results indicate that high-frequency finance data can reveal the existence of long-term PPP. This evidence sheds light on the adjustment mechanisms through which PPP is achieved. Also, the proposed ZNZ patterned VECM modelling allows better insights from this kind of financial time-series analysis.

Extreme co-movements and extreme impacts in high frequency data in finance

Extreme co-movement and extreme impact problems are inherently stochastic control problems, since they will influence the decision taken today and ultimately influence a decision taken in the future. Extreme co-movements among financial assets have been reported in the literature. However, extreme impacts have not been carefully studied yet. In this paper, we use the newly developed methodology to further explore extreme co-movements and extreme impacts in financial market. Particularly, two FX spot rates are studied. Based on the results of our analysis with FX returns, we conclude that there exist extreme co-movements and extreme impacts in FX returns and care has to be taken when we employ portfolio optimization models, especially models without the ability of handling extreme dependencies.

High Frequency Data in Finance: A Study of the Indian Equity Markets

This paper tries to empiricaly characterize the Indian intraday equity markets, using high-frequency data. The National Stock Exchange is one of the busiest exchanges in the world. Parametric modelling of intraday returns of the S \& P CNX Nifty and an equally-weighted market index of the 20 most-traded stocks shows that the behaviour of the most-traded stocks in the NSE is similar to that in the NYSE, or SEAQ, despite the NSE being an emerging marke and having a different market microstructure than the NYSE, or the SEAQ.

THE ET INTERVIEW: PROFESSOR ROBERT F. ENGLE, JANUARY 2003

In the past thirty-five years, time-series econometrics developed from infancy to relative maturity. A large part of that development is due to Robert F. Engle, whose work is distinguished by exceptional creativity in the empirical modeling of dynamic economic and financial phenomena. Engle's footsteps range widely, from early work on band-spectral regression, testing, and exogeneity through more recent work on cointegration, autoregressive conditional heteroskedasticity (ARCH) models, and ultra-high-frequency financial asset return dynamics. The booming field of financial econometrics, which did not exist twenty-five years ago, is built in large part on the volatility models pioneered by Engle, and their many variations and extensions, which have found widespread application in financial risk management, asset pricing, and asset allocation.We began the interview in fall 1998 at Spruce in Chicago, the night before the annual NBER/NSF Time Series Seminar; continued in fall 2000 at Tabla in New York; continued again in summer 2001 at the Conference on Market Microstructure and High-Frequency Data in Finance, Sandbjerg Estate, Denmark; and wrapped up by telephone in January 2003.For their cheerful and effective assistance in transcribing this interview, I thank (without implicating) Sean Campbell, Michele Souli, and Clara Vega.

A dynamical model describing stock market price distributions

High-frequency data in finance have led to a deeper understanding on probability distributions of market prices. Several facts seem to be well established by empirical evidence. Specifically, probability distributions have the following properties: (i) They are not Gaussian and their center is well adjusted by Lévy distributions. (ii) They are long-tailed but have finite moments of any order. (iii) They are self-similar on many time scales. Finally, (iv) at small time scales, price volatility follows a non-diffusive behavior. We extend Merton's ideas on speculative price formation and present a dynamical model resulting in a characteristic function that explains in a natural way all of the above features. The knowledge of such a distribution opens a new and useful way of quantifying financial risk. The results of the model agree – with high degree of accuracy – with empirical data taken from historical records of the Standard & Poor's 500 cash index.