scholarly journals Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

2019 ◽  
Vol 12 (2) ◽  
pp. 52 ◽  
Author(s):  
Toan Luu Duc Huynh
Keyword(s):  
Granger Causality ◽  
Autoregressive Model ◽  
Spillover Effect ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Quantitative Evidence ◽  
Structural Vector Autoregressive Model ◽  
Student’S T ◽  
Moving Patterns ◽  
Structural Vector

This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Student’s-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to ‘bad news’ and moving patterns in order to make timely decisions on three types (buy, hold, and sell).

scholarly journals A Spatial Panel Structural Vector Autoregressive Model with Interactive Effects and Its Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 883
Author(s):  
Yaqing Liu ◽  
Hongbing Ouyang ◽  
Xiaolu Wei
Keyword(s):  
Autoregressive Model ◽  
Common Factors ◽  
Interactive Effects ◽  
Vector Autoregressive Model ◽  
Finite Sample ◽  
Vector Autoregressive ◽  
Spatial Panel ◽  
The Common ◽  
Structural Vector Autoregressive Model ◽  
Structural Vector

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is unreasonable. Therefore, incorporating time effects, spatial effects, and time-individual effects, this paper develops a more general spatial panel structural vector autoregressive model with interactive effects (ISpSVAR) that can reflect the different effects of common factors on different spatial units. Additionally, based on whether or not the common factors can be observed, this paper proposes procedures to estimate ISpSVAR separately and studies the finite sample properties of estimators by Monte Carlo simulation. The simulation results show the effectiveness of the proposed ISpSVAR model and its estimation procedures.

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