Pairs Trading via Nonlinear Autoregressive GARCH Models

2018 ◽  
pp. 276-288 ◽  
Author(s):  
Benchawanaree Chodchuangnirun ◽  
Kongliang Zhu ◽  
Woraphon Yamaka
Keyword(s):  
Garch Models ◽  
Pairs Trading ◽  
Nonlinear Autoregressive

scholarly journals PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS

2011 ◽  
Vol 27 (6) ◽  
pp. 1236-1278 ◽  
Author(s):  
Mika Meitz ◽  
Pentti Saikkonen
Keyword(s):  
Asymptotic Normality ◽  
Autoregressive Models ◽  
Garch Models ◽  
Martingale Difference ◽  
First Order ◽  
First Results ◽  
Consistency And Asymptotic Normality ◽  
Quasi Maximum Likelihood ◽  
Nonlinear Autoregressive ◽  
Nonlinear Autoregressive Models

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

DCC-Garch Models Using Islamic Market and European Market Indices

2017 ◽  
Vol 04 ◽  
pp. 01-20
Author(s):  
Sabbah Gueddoudj ◽  
Keyword(s):  
European Market ◽  
Garch Models

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

Author response for "The nonlinear autoregressive network with exogenous inputs (NARX) neural network to damp power system oscillations"

2020 ◽  
Author(s):  
Luis Felipe Bianchi Carbonera ◽  
Daniel Pinheiro Bernardon ◽  
Douglas de Castro Karnikowski ◽  
Felix Alberto Farret
Keyword(s):  
Neural Network ◽  
Power System ◽  
Author Response ◽  
Power System Oscillations ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network
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