Integrated Volatility and UHF-GARCH Models: A Comparison Using High Frequency Financial Data

2004 ◽  
Author(s):  
Alain Coen ◽  
Francois-Eric Racicot
Keyword(s):  
High Frequency ◽  
Financial Data ◽  
Garch Models ◽  
Integrated Volatility

FORECASTING HIGH-FREQUENCY FINANCIAL DATA VOLATILITY VIA NONPARAMETRIC ALGORITHMS: EVIDENCE FROM TAIWAN'S FINANCIAL MARKETS

2006 ◽  
Vol 02 (03) ◽  
pp. 345-359 ◽  
Author(s):  
WO-CHIANG LEE
Keyword(s):  
Computational Intelligence ◽  
High Frequency ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Financial Data ◽  
Percentage Error ◽  
Integrated Volatility ◽  
Squared Error ◽  
Performance Indexes ◽  
Forecasting Performance

This paper uses two computational intelligence algorithms, namely, artificial neural networks (ANN) and genetic programming (GP), for forecasting the volatility of high-frequency TAIEX financial data with four different horizons and compares the out-sample forecasting performance with the GARCH(1,1), EGRACH(1,1) and GJR-GARCH(1,1) models. Based on intraday integrated volatility, the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Theil's U and the VaR backtest are used as performance indexes. Our empirical results reveal that the GP and ANN perform reasonably well in forecasting out-sample volatility compared to other parametric volatility forecasting models for most of the performance indexes. Our results also suggest that nonparametric computational intelligence algorithms are powerful for modeling the volatility of high-frequency intraday financial data.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

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