• Corpus ID: 251040228

Detecting common bubbles in multivariate mixed causal-noncausal models

@inproceedings{Cubadda2022DetectingCB,
  title={Detecting common bubbles in multivariate mixed causal-noncausal models},
  author={Gianluca Cubadda and Alain Hecq and Elisa Voisin},
  year={2022}
}
This paper proposes methods to investigate whether the bubble patterns observed in individual series are common to various series. We detect the non-linear dynamics using the recent mixed causal and noncausal models. Both a likelihood ratio test and information criteria are investigated, the former having better performances in our Monte Carlo simulations. Implementing our approach on three commodity prices we do not find evidence of commonalities although some series look very similar. 
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SHOWING 1-10 OF 18 REFERENCES

Explosive Bubble Modelling by Noncausal Process

The linear mixed causal and noncausal autoregressive processes provide often a better fit to economic and financial time series than the standard causal linear autoregressive processes. By…

On non-contemporaneous short-run co-movements

Detecting Co-Movements in Noncausal Time Series

This paper introduces the notion of common noncausal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co-movements might not be detected…

Common cycles and the importance of transitory shocks to macroeconomic aggregates (revised version)

Is climate change time reversible?

This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for…

NONCAUSAL VECTOR AUTOREGRESSION

In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the…

Common Volatility in International Equity Markets

In this article, we take advantage of the time-varying structure of stock-returns variances to investigate whether two international stock markets share the same volatility process. We use a test…

Noncausal vector autoregressive process: Representation, identification and semi-parametric estimation

Common Trends and Common Cycles

The existence of a serial correlation common feature among the first differences of a set of I(1) variables implies the existence of a common cycle in the Beveridge-Nelson-Stock-Watson decomposition…

Co-integration and error correction: representation, estimation and testing

The relationship between cointegration and error correction models, first suggested by Granger, is here extended and used to develop estimation procedures, tests, and empirical examples. A vector of…