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Threshold linkages between volatility and trading volume: evidence from developed and emerging markets

  • Fredj Jawadi EMAIL logo and Loredana Ureche-Rangau
Published/Copyright: April 11, 2013
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Studies in Nonlinear Dynamics and Econometrics
From the journal Studies in Nonlinear Dynamics and Econometrics Volume 17 Issue 3

Abstract

This paper studies volatility dynamics and provides further insights into its relationship with trading volume. In particular, we examine whether trading volume is significantly informative for investors when attempting to apprehend potential changes in volatility dynamics, and hence, in the evolution of market risk. To this end, we apply recent nonlinear modeling tools, namely Switching Transition Regression (STR) models that are robust to asymmetry and nonlinearity as well as TARCH models to check for the nature of transition between volatility regimes. Our findings show that volatility dynamics exhibit nonlinearity and switching regimes for which the transition is smooth rather than abrupt. Furthermore, one regime is associated with low volatility and a weak relationship with trading volume while in the second regime, the causality relationship is stronger and volatility is high. The paper’s novelty is to show that not only does trading volume contribute to explaining market volatility, but also that the change in volatility dynamics is performed through the change in its relationship with trading volume.

Keywords: volatility; trading volume; structural breaks; nonlinearity
Corresponding author: Fredj Jawadi, University of Evry & France Business School - Campus Amiens, 2, rue Facteur Cheval, 91025 Evry, France
  1. 1

    In a parallel approach, the heterogeneity of investors’ opinions and expectations is also used to explain the relationship between volatility and volume; see for example Admati and Pfeiderer (1988), Harris and Raviv (1993), He and Wang (1995) among others.

  2. 2

    For more details about the MDH, its developments and empirical implications, see Epps and Epps (1976), Tauchen and Pitts (1983), Harris (1986, 1987), Gallant, Rossi, and Tauchen (1992), Andersen (1996), Liesenfeld (1998, 2001), Chen, Firth, and Rui (2001), Fleming, Kirby, and Ostdiek (2006) among others.

  3. 3

    The parameters c1 and c2 denote the thresholds associated with regime 1 and regime 2.

  4. 4

    Classification of the markets is based on the MSCI International Equity index definitions and criteria.

  5. 5

    To save space, the results of unit root tests are not reported but are available upon request.

  6. 6

    We would like to thank the editor and two anonymous referees for this suggestion.

  7. 7

    The detailed results of all these misspecification tests are available upon request.

  8. 8

    The optimal lag number of all the VAR models was selected according to the AIC and BIC information criteria.

Appendix 1: VAR estimation results

Whole period: VAR (2).8

VULVU
VU(-1)0.1439120.147574
[8.96419][0.47217]
VU(-2)0.275631–1.603121
[17.3312][–5.17779]
LVU(-1)0.0001770.700220
[0.21409][43.5437]
LVU(-2)0.0009230.272532
[1.12163][17.0194]
C–0.0107320.413400
[–3.09212][6.11791]
R20.1298260.924638
F-statistic139.498011471.76
Log likelihood12276.101158.034
Akaike AIC–6.553324–0.615773
Schwarz SC–6.545009–0.607457

Sample 2: VAR (1).

VULVU
VU(-1)0.1104980.915060
[3.36531][1.95944]
LVU(-1)0.0132890.717772
[8.06105][30.6114]
C–0.1934214.323233
[–7.68799][12.0818]
R20.1067440.544562
F-statistic60.10816601.4321
Log likelihood2980.339301.5907
Akaike AIC–5.901565–0.591855
Schwarz SC–5.886946–0.577236

Appendix 2: Impulse-response functions

Response to Cholesky One S.D. Innovations ±2 S.E.

Response to Cholesky One S.D. Innovations ±2 S.E.

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Published Online: 2013-04-11
Published in Print: 2013-05-01

©2013 by Walter de Gruyter Berlin Boston

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https://doi.org/10.1515/snde-2012-0040
Keywords for this article
volatility; trading volume; structural breaks; nonlinearity

Articles in the same Issue

  1. Masthead
  2. Masthead
  4. The Danish krone-euro exchange rate and Danmark Nationalbank intervention operations
  5. Common large innovations across nonlinear time series
  6. The forward rate premium puzzle: a case of misspecification?1)
  7. A smooth transition long-memory model
  8. Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study
  9. Threshold linkages between volatility and trading volume: evidence from developed and emerging markets
  10. Inventory investment and the business cycle: the usual suspect
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