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Volatility Spillover and Connectedness Between SME and Main Markets of India and China

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  • Pradeep Kumar Behera

    (School of Humanities, Social Sciences, and Management Indian Institute of Technology (IIT) Bhubaneswar)

  • Naresh Chandra Sahu

    (School of Humanities, Social Sciences, and Management Indian Institute of Technology (IIT) Bhubaneswar)

  • Abhisek Mahanta

    (School of Humanities, Social Sciences, and Management Indian Institute of Technology (IIT) Bhubaneswar)

Abstract

The study aims at investigating the volatility and connectedness aspect of the SME and main market indices of India and China i.e., SENSEX, BSE SME IPO, SZSE Composite and SME 300. GARCH and TARCH models are used to determine the symmetric and asymmetric volatility within the indices respectively using daily data from January 2013 to March 2024. The DCC-GARCH model is applied to analyse the inter-country and intra-country volatility spillover. While, TVP-VAR model measure the connectedness between the indices. The empirical findings reveal that the SME index of India gives higher returns than China. On the volatility front, the SME of India and China have same degree of volatility. In the long term, there is a significant the spread of volatility between the SENSEX and SME index in India. The findings of the study show the high degree of long-term dependence and interconnectedness of SME markets of India and China. Further, it is found that the main market and SME market indexes of India are the net receivers of volatility, and the index of the Chinese market is the net transmitter. The Total volatility spillover index between main and SME is low in India as Compare with China. This study can help to mutually beneficial economic stability, and risk management. Also, it can help investors to take better investment decision.

Suggested Citation

  • Pradeep Kumar Behera & Naresh Chandra Sahu & Abhisek Mahanta, 2025. "Volatility Spillover and Connectedness Between SME and Main Markets of India and China," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(4), pages 1407-1429, December.
    • Handle: RePEc:kap:apfinm:v:32:y:2025:i:4:d:10.1007_s10690-024-09492-5
    DOI: 10.1007/s10690-024-09492-5
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    JEL classification:

    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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