Local Linear Quantile Regression for Time Series Under Near Epoch Dependence

2020 ◽  
Author(s):  
Xiaohang Ren ◽  
Zudi Lu
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Local Linear

scholarly journals Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Abobaker M. Jaber ◽  
Mohd Tahir Ismail ◽  
Alsaidi M. Altaher
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Empirical Mode Decomposition ◽  
Financial Time Series ◽  
Stock Index ◽  
Financial Time ◽  
Local Linear ◽  
Mode Decomposition ◽  
Closing Price ◽  
Index Time Series

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

Regularized local linear prediction of chaotic time series

1998 ◽  
Vol 112 (3-4) ◽  
pp. 344-360 ◽  
Author(s):  
D. Kugiumtzis ◽  
O.C. Lingjærde ◽  
N. Christophersen
Keyword(s):  
Time Series ◽  
Linear Prediction ◽  
Chaotic Time Series ◽  
Local Linear

High-quantile regression for tail-dependent time series

Biometrika ◽  
2020 ◽  
Author(s):  
Ting Zhang
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Limit Theorems ◽  
Tail Dependence ◽  
Serial Dependence ◽  
Response Distribution ◽  
Tail Region ◽  
Strongly Mixing ◽  
Regression Estimators ◽  
High Quantile

Summary Quantile regression is a popular and powerful method for studying the effect of regressors on quantiles of a response distribution. However, existing results on quantile regression were mainly developed for cases in which the quantile level is fixed, and the data are often assumed to be independent. Motivated by recent applications, we consider the situation where (i) the quantile level is not fixed and can grow with the sample size to capture the tail phenomena, and (ii) the data are no longer independent, but collected as a time series that can exhibit serial dependence in both tail and non-tail regions. To study the asymptotic theory for high-quantile regression estimators in the time series setting, we introduce a tail adversarial stability condition, which had not previously been described, and show that it leads to an interpretable and convenient framework for obtaining limit theorems for time series that exhibit serial dependence in the tail region, but are not necessarily strongly mixing. Numerical experiments are conducted to illustrate the effect of tail dependence on high-quantile regression estimators, for which simply ignoring the tail dependence may yield misleading $p$-values.

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