The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

Abstract

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June 06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.

Regime-Switching Fractionally Integrated Asymmetric Power Neural Network Modeling of Nonlinear Contagion for Chaotic Oil and Precious Metal Volatilities

“…Similar to the results of Wen [83], Zhu [76], and Aloui [62], the findings determined the evidence of a dependence structure between the analyzed variables. Further, the findings also confirmed the validity of the works Boubaker and Shaier [29,64,65] and Bildirici [30,54,69,75]. The dependence structure changes between regimes.…”

“…where τ L , τ u ∈ 0, 1 and ν = −1/ log 2 τ L and = 1/ log 2 (2 − τ u ). Similar to the SJC copula [68], the upper and lower tail dependences are gathered by JC copula [68]; the dependence is asymmetric if symmetric τ L = τ u and, in the case of τ L = τ u , the dependence becomes symmetric [29,30,54,63].…”

“…[64,65] found the signal of instability in both tail dependence and copula parameters. By applying TAR-TR-TGARCH-copula, Bildirici [30] studied co-movement among the expectations of the investors, oil price and stock return volatility and determined presence of nonlinear tail dependence among the selected variables. By MSGARCH-MLP methods, Bildirici [54] tested the co-movement and contagion behavior among returns of precious metals, oil price and COVID-19-infected persons for the 30 December 2019, 26 October 2020 period with MS-GARCH-MLP-copula model and determined co-movement and contagion structure among the selected variables.…”

“…The recent literature following the copulae approaches to contagion is extended to regime dependency in the coefficients of tail dependence and copula. Examples include the studies of Chan-Lau et al [27], Forbes and Rigobon [28], Boubaker and Sghaier [29], and Bildirici [30]. As for the application aspect, the proposed method has significant gains by augmenting the forecasting capabilities with neural networks.…”

BRENT HAM PETROL GETİRİLERİNDE KAOTİK DİNAMİKLERİN ARAŞTIRILMASI / Investigation of Chaotic Dynamics In Brent Crude Oil Returns

“…(1992, Wei ve Leuthold (1998), Panas ve Ninni (2000), Kyrtsou vd. (2004), He (2011) ve Bildirici (2018)…”