GLIMS Journal of Management Review
and Transformation
issue front

Rohit Malhotra1

First Published 2 May 2023.
Article Information Volume 2, Issue 1 March 2023
Corresponding Author:

Rohit Malhotra, JK Business School, Gurugram, Haryana 122102, India.

1 JK Business School, Gurugram, Haryana, India

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.


No one in the world, particularly the economic community, could have predicted the manner in which the 2019 COVID pandemic erupted or shook the economies. Developing countries, particularly South Asian economies and the lower middle income (LMI) countries (India, Pakistan, Sri Lanka, Bangladesh, Bhutan, and Nepal), had also experienced similar turmoil. The COVID outbreak and trade-related risks were closely associated with exports, imports, international liquidity, and SDR exchange rates, posing time-varying risks. In this sphere, an analytical approach to generate meaningful trade-related time-varying volatility predictions with respect to India’s position is critical while considering the above-listed variables. The Markov-switching (endogenous switching) VAR model using the exponential moving average volatility model (EWMA) of the four macroeconomic variables was used. The author tried to use the EWMA volatility model to witness the impact of spillovers and shock persistence. The present study earmarked the behaviour of time-varying trade-related risks (for Regime 1, i.e., the Early COVID period versus Regime-2 during the COVID period) in terms of the top six economies (as per December 2021 data) when endogenous factors like international liquidity and exchange rate shocks are implemented.


Markov-switching VAR, EWMA volatility, volatility spillover convergences, unilateral convergences, bi-lateral convergences


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