FORECASTING HIGH FREQUENCY FINANCIAL DATA: STATISTICAL AND FUZZY LOGIC RBF ANN APPROACH

2012 ◽  
pp. 821-826
Author(s):  
DUSAN MARCEK
Keyword(s):  
Fuzzy Logic ◽  
High Frequency ◽  
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.

High Frequency Modeling of Smart Sensor for Precise Power Quality Assessment Using Fuzzy Logic

2016 ◽  
Vol 25 (06) ◽  
pp. 1650056 ◽  
Author(s):  
G. Sudha ◽  
K. R. Valluvan
Keyword(s):  
Fuzzy Logic ◽  
Quality Assessment ◽  
Power Quality ◽  
High Frequency ◽  
Wide Band ◽  
Electrical Equipment ◽  
Distribution Networks ◽  
Accurate Method ◽  
Energy Difference ◽  
Critical Issue

Power Quality Assessment (PQA) is a critical issue both in transmission and distribution networks. Therefore, it is necessary to precisely classify the disturbances in shortest possible time to prevent the malfunction or increase of losses in the electrical equipment through appropriate remedial techniques. This paper proposes a highly accurate method of PQA through data acquisition using smart sensors, the Rogowski coils (RCs). RCs with wide band width and linear characteristics allow faithful reproduction of high-frequency (HF) signals. In the proposed method, simulated disturbance signals are applied to RC. The output signals are subjected to multilevel wavelet decomposition and then computation of the energy difference in the detailed components between the disturbance signal and the pure sinusoidal waveform is performed to design a fuzzy logic Power Quality Classifier. The classifier is tested by varying the magnitude, frequency and duration of the disturbance and found to be accurate to 98.38%. The classification accuracy depends mainly on the performance of sensors at HFs. Thus, with RCs as sensors instead of conventional instrument transformers, it is found that the precision of power quality classification is greatly improved.

A Study of Persistence of Price Movement Using High Frequency Financial Data

2011 ◽  
pp. 27-46 ◽  
Author(s):  
Dragos Bozdog ◽  
Ionuţ Florescu ◽  
Khaldoun Khashanah ◽  
Jim Wang
Keyword(s):  
High Frequency ◽  
Financial Data ◽  
Price Movement
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