Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis
The Granger causality test is essential for detecting lead–lag relationships between time series. Traditionally, one uses a linear version of the test, essentially based on a linear time series regression, itself being based on autocorrelations and cross-correlations of the series. In the present pa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/3/378 |
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| author | Håkon Otneim Geir Drage Berentsen Dag Tjøstheim |
| author_facet | Håkon Otneim Geir Drage Berentsen Dag Tjøstheim |
| author_sort | Håkon Otneim |
| collection | DOAJ |
| description | The Granger causality test is essential for detecting lead–lag relationships between time series. Traditionally, one uses a linear version of the test, essentially based on a linear time series regression, itself being based on autocorrelations and cross-correlations of the series. In the present paper, we employ a local Gaussian approach in an empirical investigation of lead–lag and causality relations. The study is carried out for monthly recorded financial indices for ten countries in Europe, North America, Asia and Australia. The local Gaussian approach makes it possible to examine lead–lag relations locally and separately in the tails and in the center of the return distributions of the series. It is shown that this results in a new and much more detailed picture of these relationships. Typically, the dependence is much stronger in the tails than in the center of the return distributions. It is shown that the ensuing nonlinear Granger causality tests may detect causality where traditional linear tests fail. |
| first_indexed | 2024-03-09T19:51:47Z |
| format | Article |
| id | doaj.art-029df51028e7403484e9b6b0f40542e7 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T19:51:47Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-029df51028e7403484e9b6b0f40542e72023-11-24T01:07:32ZengMDPI AGEntropy1099-43002022-03-0124337810.3390/e24030378Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical AnalysisHåkon Otneim0Geir Drage Berentsen1Dag Tjøstheim2Department of Business and Management Science, Norwegian School of Economics, 5045 Bergen, NorwayDepartment of Business and Management Science, Norwegian School of Economics, 5045 Bergen, NorwayDepartment of Mathematics, University of Bergen, 7803 Bergen, NorwayThe Granger causality test is essential for detecting lead–lag relationships between time series. Traditionally, one uses a linear version of the test, essentially based on a linear time series regression, itself being based on autocorrelations and cross-correlations of the series. In the present paper, we employ a local Gaussian approach in an empirical investigation of lead–lag and causality relations. The study is carried out for monthly recorded financial indices for ten countries in Europe, North America, Asia and Australia. The local Gaussian approach makes it possible to examine lead–lag relations locally and separately in the tails and in the center of the return distributions of the series. It is shown that this results in a new and much more detailed picture of these relationships. Typically, the dependence is much stronger in the tails than in the center of the return distributions. It is shown that the ensuing nonlinear Granger causality tests may detect causality where traditional linear tests fail.https://www.mdpi.com/1099-4300/24/3/378lead–lag relationshipslocal Gaussian approximationlocal Gaussian autocorrelationlocal Gaussian cross-correlationlocal Gaussian partial correlationtest of conditional independence |
| spellingShingle | Håkon Otneim Geir Drage Berentsen Dag Tjøstheim Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis Entropy lead–lag relationships local Gaussian approximation local Gaussian autocorrelation local Gaussian cross-correlation local Gaussian partial correlation test of conditional independence |
| title | Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis |
| title_full | Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis |
| title_fullStr | Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis |
| title_full_unstemmed | Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis |
| title_short | Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis |
| title_sort | local lead lag relationships and nonlinear granger causality an empirical analysis |
| topic | lead–lag relationships local Gaussian approximation local Gaussian autocorrelation local Gaussian cross-correlation local Gaussian partial correlation test of conditional independence |
| url | https://www.mdpi.com/1099-4300/24/3/378 |
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