Separate Noise and Jumps From Tick Data: An Endogenous Thresholding Approach

2021 ◽  
Author(s):  
Xiaolu Zhao ◽  
Seok Young Hong ◽  
Oliver B. Linton
Keyword(s):  
Tick Data

The Asset Pricing Effects of UK Market Liquidity Shocks: Evidence from Tick Data

2014 ◽  
Author(s):  
Jason Foran ◽  
Mark C. Hutchinson ◽  
Niall O'Sullivan
Keyword(s):  
Asset Pricing ◽  
Market Liquidity ◽  
Liquidity Shocks ◽  
Tick Data

Long correlations and fractional difference analysis applied to the study of memory effects in high-frequency (tick) data

2015 ◽  
Vol 15 (8) ◽  
pp. 1365-1374 ◽  
Author(s):  
Maria Pia Beccar Varela ◽  
Francis Biney ◽  
Ionut Florescu
Keyword(s):  
High Frequency ◽  
Memory Effects ◽  
Fractional Difference ◽  
Difference Analysis ◽  
Tick Data

Parameter-Mapping Sonification of Tick-Data

2019 ◽  
pp. 237-252
Author(s):  
David Worrall
Keyword(s):  
Parameter Mapping ◽  
Tick Data

Volatility and Microstructure Noise

2014 ◽  
Author(s):  
Yacine Aïıt-Sahalia ◽  
Jean Jacod
Keyword(s):  
Microstructure Noise ◽  
Underlying Process ◽  
One Dimensional ◽  
Integrated Volatility ◽  
Tick Data ◽  
Efficient Price ◽  
The Difference

This chapter covers the various problems arising in the estimation of the integrated volatility when the observations are contaminated by a noise. The approach used is quite partial, and fundamentally phenomenological, in contrast with a microeconomical approach. That is, the authors assumed the existence of an underlying (nonobservable) efficient price, and what is called noise below is by definition the difference between the observed price and the efficient price. Henceforth, it certainly does not apply to tick-by-tick data, even if these were regularly spaced in time (which they are not). In the whole chapter, with the exception of one section, the underlying process X is one-dimensional.

Volatility forecasting in a tick data model

2007 ◽  
pp. 295-299
Author(s):  
L.C.G. Rogers
Keyword(s):  
Data Model ◽  
Volatility Forecasting ◽  
Tick Data

Analyzing stock market tick data using piecewise nonlinear model

2002 ◽  
Vol 22 (3) ◽  
pp. 249-255 ◽  
Author(s):  
K Oh
Keyword(s):  
Stock Market ◽  
Nonlinear Model ◽  
Tick Data

DEFINITION OF DISTANCE FOR MARKED POINT PROCESS DATA AND ITS APPLICATION TO RECURRENCE PLOT-BASED ANALYSIS OF EXCHANGE TICK DATA OF FOREIGN CURRENCIES

2010 ◽  
Vol 20 (11) ◽  
pp. 3699-3708 ◽  
Author(s):  
SATOSHI SUZUKI ◽  
YOSHITO HIRATA ◽  
KAZUYUKI AIHARA
Keyword(s):  
Point Process ◽  
Foreign Exchange ◽  
Marked Point ◽  
Recurrence Plot ◽  
Marked Point Process ◽  
Recurrence Plots ◽  
Process Data ◽  
Tick Data ◽  
Point Process Data ◽  
Definition Of

Recurrence plots are effective in analyzing nonstationary time series. Further, it is desirable to make the recurrence plot-based analysis applicable to marked point process data such as foreign exchange tick data. In this paper, we define a distance for marked point process data and establish the basis for further analyses. We also show that foreign exchange tick data have serial dependence using recurrence plots and the random shuffle surrogate method.

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

The use of the Hurst exponent to investigate the quality of forecasting methods of ultra-high-frequency data of exchange rates

2019 ◽  
Vol 65 (2) ◽  
pp. 200-223
Author(s):  
Robert Szóstakowski
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Foreign Exchange Market ◽  
Fractal Theory ◽  
Data Series ◽  
Time Data ◽  
Forecasting Accuracy ◽  
Forecasting Error ◽  
Forecasting Methods ◽  
Tick Data

Over the last century a variety of methods have been used for forecasting financial time data series with different results. This article explains why most of them failed to provide reasonable results based on fractal theory using one day tick data series from the foreign exchange market. Forecasting AMAPE errors and forecasting accuracy ratios were calculated for statistical and machine learning methods for currency time series which were divided into sub-segments according to Hurst ratio. This research proves that the forecasting error decreases and the forecasting accuracy increases for all of the forecasting methods when the Hurt ratio increases. The approach which was used in the article can be successfully applied to time series forecasting by indicating periods with the optimal values of the Hurst exponent.

Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

2014 ◽  
Vol 18 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Yoshiyuki Yabuuchi ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Fuzzy Time Series ◽  
Economic Systems ◽  
Random Data ◽  
Human Behaviors ◽  
Section Data ◽  
Tick Data ◽  
Economic Analyses ◽  
Fuzzy Random

Economic analyses are typical methods based on timeseries data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random timeseries data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.

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