All
 
|
 
 


Contemporaneous Effects of Non-Synchronous Time Series: VAR Model Problems
 
PIIS042473880004677-3-1
DOI10.31857/S042473880004677-3
Publication type Article
Status Published
Authors
Ruslan Grigoryev 
Occupation: deputy director at Scientific-Research Institute of Kazan Innovative University named after V. G. Timiryasov (IEML)
Affiliation: Scientific-Research Institute of Kazan Innovative University named after V. G. Timiryasov (IEML)
Address: Kazan, Russian Federation
Journal nameEkonomika i matematicheskie metody
EditionVolume 55 Issue 2
Pages118-129
Abstract

 

Time series data synchronism is not a frequently met condition in theoretical descriptions of econometric models and tests based on them. However, due to the fact that financial institutions are distributed in various time zones, researchers started to use incorrectly the classical econometric models created for synchronous time series in the nonsynchronous data sets. The article critically analyzes the application of nonsynchronous time series in VAR model by Christopher A. Sims as the time series start to demonstrate contemporaneous effects for individual variables, which are absent in the synchronous data sets. The novelty of the work is in revealing the incorrectness of using classical VAR(VECM) model with the set of non-synchronous time series. The usage of time series which values are recorded in different moments inside observation causes the classic models to violate the parity of the initial testing conditions, where one of the series gains advantage in rejecting the Granger causality hypothesis in direction to other series. The presence of such a disparity consists in the fact that the classical model allows contemporaneous effects for the time series recorded later inside observation failing to provide such an opportunity for the opponent time series. A possible way to reduce the effect of disparity lies in the usage of several VAR models. The variable with lag 0 of the time series, the moment which occurs later than that of the opponent's series, in the case of SVAR models leads to the violation of Hume’s causality principle. The existence of this variable in the model specification violates the correct assessment of other indicators of the model, while Granger causality for this variable is tested for the direction from the future to the past, which is unacceptable.

Keywordsnon-synchronous trading, non-synchronous, VAR, vector autoregression, VECM, contemporaneous term, instantaneous causality, Granger causality
Received25.05.2019
Publication date25.05.2019
Number of characters33288
Cite  
100 rub.
When subscribing to an article or issue, the user can download PDF, evaluate the publication or contact the author. Need to register.

Number of purchasers: 2, views: 1548

Readers community rating: votes 0 

1. Bessler D.A., Yang J. (2003). The Structure of Interdependence in International Stock Markets. Journal of International Money and Finance, 22, 2, 261—287.
2. Billio M., Caporin M. (2010). Market Linkages, Variance Spillovers, and Correlation Stability: Empirical Evidence of Financial Contagion. Computational Statistics & Data Analysis. 54, 11, 2443—2458.
3. Brooks C. (2008). Introductory Econometrics for Finance. Cambridge: Cambridge University Press.
4. Durdyev R., Peresetsky A. (2014). Autocorrelation in the Global Stochastic Trend. Applied Econometrics, 35, 3, 39—58 (in Russian).
5. Engle R.F., Granger C.W.J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica: Journal of the Econometric Society, 39, 3, 106—135 (in Russian).
6. Engle R.F., Granger C.W.J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica: Journal of the Econometric Society, 55, 2, 251—276.
7. Eun C.S., Shim S. (1989). International Transmission of Stock Market Movements. Journal of Financial and Quantitative Analysis, 24, 2, 241—256.
8. Forbes K.J., Rigobon R. (2002). No Contagion, Only Interdependence: Measuring Stock Market Comovements. The Journal of Finance, 57, 5, 2223—2261.
9. Gebka B., Serwa D. (2007). Intra-and Inter-Regional Spillovers between Emerging Capital Markets Around the World. Research in International Business and Finance, 21, 2, 203—221.
10. Gjerde O., S?ttem F. (1995). Linkages Among European and World Stock Markets. The European Journal of Finance, 1, 2, 165—179.
11. Granger C.W.J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica: Journal of the Econometric Society, 37, 3, 424—438.
12. Granger C.W.J. (1981). Some Properties of Time Series Data and their Use in Econometric Model Specification. Journal of Econometrics, 16, 1, 121—130.
13. Granger C.W.J. (1983). Co-Integrated Variables and Error-Correcting Models. Discussion Paper 83-13. Department of Economics, University of California at San Diego.
14. Granger C.W.J. (1986). Developments in the Study of Cointegrated Economic Variables. Oxford Bulletin of Economics and Statistics, 48, 3, 213—228.
15. Granger C.W.J. (1988). Some Recent Development in a Concept of Causality. Journal of Econometrics, 39, 1—2, 199—211.
16. Grigoryev R. (2010). The Interdependence between Stock Markets of BRIC and Developed Countries and the Impact of Oil Prices on this Interdependence. PhD thesis, University of Portsmouth.
17. Grigoryev R. (2018a). Prime Meridian: Consequences for Modeling Financial Nonsynchronous Time Series. Terra Economicus, 16, 3, 16—34 (in Russian).
18. Grigoryev R. (2018b). Non-Synchronous Time Series is the Main Reason of US Stock Exchanges Leadership in Classic Econometric Models. Actual Problems of Economics and Law, 12, 2, 241—255 (in Russian).
19. Grigoryev R.A. (2018ñ) A replication of Grigoryev's research in the article by Resnik and Shoesmith: identical hypothesis, method of data preparation, results. Working paper # WP/2018/326. Moscow: CEMI RAS (in Russian)
20. Grigoryev R., Jaffry S., Marchenko G. (2012a). Investigation of the Consequences of Ignoring Daily Data Non-Synchronism in Cross-Market Linkages: BRIC and Developed Countries. Applied Econometrics, 2, 92—112 (in Russian).
21. Grigoryev R., Jaffry S., Marchenko G. (2012b). The Role of the Timeline in Granger Causality Test in the Presence of Daily Data Non-Synchronism. Applied Econometrics, 27, 3, 3—19 (in Russian).
22. Grigoryeva L., Ortega J., Peresetsky A.A. (2018). Volatility Forecasting Using Global Stochastic Financial Trends Extracted from Non-Synchronous Data. Econometrics and Statistics, 5, 67—82.
23. Hoover K. (2008). Causality in Economics and Econometrics. In: Durlauf S.N., Blume L. (eds) “The new Palgrave dictionary of economics”. (Vol. 1). Basingstoke: Palgrave Macmillan.
24. Hume D. (1960). Treatise of Human Nature. Oxford: Clarendon Press.`
25. Johansen S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, 6, 1551—1580.
26. Kasa K. (1992). Common Stochastic Trends in International Stock Markets. Journal of Monetary Economics, 29, 1, 95—124.
27. Koch P.D., Koch T.W. (1991). Evolution in Dynamic Linkages Across Daily National Stock Indexes. Journal of International Money and Finance, 10, 2, 231—251.
28. Korhonen I., Peresetsky A. (2013). Extracting Global Stochastic Trend from Non-Synchronous Data. Bank of Finland. BOFIT Discussion Papers, no. 15/2013.
29. Malliaris A.G., Urrutia J.L. (1992). The International Crash of October 1987: Causality tests. Journal of Financial and Quantitative Analysis, 27, 3, 353—364.
30. Peiro A., Quesada J., Jimenez E.U. (1993). Transmission of Information between Stock Markets. Institut Valencia d'Investigacions Economiques.
31. Peresetsky A.A., Yakubov R.I. (2017). Autocorrelation in an Unobservable Global Trend: Does It Help to Forecast Market Returns? International Journal of Computational Economics and Econometrics, 7, 1—2, 152—169.
32. Resnick B.G., Shoesmith G.L. (2011). Information Transmission in the World Money Markets. European Financial Management, 17, 1, 183—200.
33. Resnick B.G., Shoesmith G.L. (2017). A Note on Modeling World Equity Markets with Nonsynchronous Data. Journal of International Financial Markets, Institutions and Money, 51, 125—132.
34. Sims C.A. (1980). Macroeconomics and Reality. Econometrica: Journal of the Econometric Society, 48, 1, 1—48.

Система Orphus

Loading...
Up