Spatial quantile estimation of multivariate threshold time series models

2017 ◽  
Vol 486 ◽  
pp. 772-781 ◽  
Author(s):  
Jiancheng Jiang ◽  
Xuejun Jiang ◽  
Jingzhi Li ◽  
Yi Liu ◽  
Wanfeng Yan
Keyword(s):  
Time Series ◽  
Quantile Estimation ◽  
Time Series Models ◽  
Threshold Time ◽  
Threshold Time Series

scholarly journals A review of threshold time series models in finance

2011 ◽  
Vol 4 (2) ◽  
pp. 167-181 ◽  
Author(s):  
Cathy W. S. Chen ◽  
Feng-Chi Liu ◽  
Mike K. P. So
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Threshold Time ◽  
Threshold Time Series

EMPIRICAL ANALYSIS OF BITCOIN PRICES USING THRESHOLD TIME SERIES MODELS

2018 ◽  
Vol 13 (04) ◽  
pp. 1850017
Author(s):  
RODOLFO ANGELO MAGTANGGOL III DE GUZMAN ◽  
MIKE K. P. SO
Keyword(s):  
Empirical Analysis ◽  
Threshold Model ◽  
Time Series Models ◽  
Network Characteristics ◽  
Threshold Autoregression ◽  
Block Number ◽  
Threshold Time ◽  
History Of ◽  
Threshold Time Series ◽  
The Empirical Analysis

This paper proposes the use of threshold heteroskedastic models which integrate threshold nonlinearity [Tong, H (1978). On a Threshold Model, pp. 575–586. Netherlands: Sijthoff & Noordhoff; Tong, H and KS Lim (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 3, 245–292.] and GARCH-type conditional variance for modeling Bitcoin returns to provide an understanding on the huge volatility that Bitcoin has been famous for. Specifically, the model attempts to identify different regimes throughout the history of Bitcoin using the different available Bitcoin network characteristics, such as cost per transaction, number of transactions per block, number of active addresses and number of transactions. Estimation and diagnostic checks are performed using Markov chain Monte Carlo methods. In the empirical analysis, we show that our model is able to identify periods of crashes as one of these regimes, which is also a period of declining returns and declining number of active users. We also find that the number of users and the number of transactions determine the magnitude or persistence of a crash period.

GA-BASED TIME SERIES MODELS WITH THRESHOLD IN TWO DOMAINS

2009 ◽  
Vol 18 (04) ◽  
pp. 801-823
Author(s):  
MING SU ◽  
GARY G. YEN ◽  
R. R. RHINEHART
Keyword(s):  
Time Series ◽  
Arrival Time ◽  
Financial Time Series ◽  
Wave Pattern ◽  
Sine Wave ◽  
Proposed Model ◽  
Threshold Time ◽  
Partition Space ◽  
Threshold Time Series ◽  
Model Structures

A new threshold time series model is proposed whose submodels are extended from AR to SARIMA and whose domains having thresholds are extended to two. By these two extensions, the newly proposed models offer more flexibility to piecewisely approximate nonstationary time series by a finite number of local stationary models. A genetic algorithm is applied to simultaneously search for appropriate model structures, estimate the optimal model coefficients, as well as partition space by finding appropriate thresholds. The resulting model is applied to a synthetic multi-frequency sine wave and two financial time series with improved modeling quality. The proposed model is also applied to seismogram analysis in order to recognize earthquake wave pattern related to locate arrival time of different waves.

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