Do stock market fluctuations lead to currency deflation in the South Asian region? Evidence beyond symmetry

Abstract

This study explores the role of the asymmetrical influence of stock market reaction to the fluctuations in US dollars against the domestic currencies of the South Asian region. The symmetrical Panel based autoregressive distributed lag model (PARDL) model and panel‐based non‐linear autoregressive distributed lag model (NARDL) model with the Pooled Mean Group Approach (PMG) approach were employed to estimate the impact of stock market bullish and bearish behaviour on local currency variabilities of the South Asian region. The results show that during the pre‐economic recession regime, negative fluctuations in stock indexes had a direct association with exchange rate fluctuations for a longer period. However, after the crisis, both negative and positive stock index shocks have formulated an indirect or inverse association with exchange rate fluctuations. These findings pose important practical implications for the short‐term and long‐term shareholders and financial analysts. Hence, it is recommended that shareholders and financial analysts should base their investment in South Asian stock indices on negative and positive shocks since the non‐linear connectivity between both variables also exhibits regime dependence characteristics.

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

“…The ARDL bounds testing methodology presents numerous advantages and is applicable regardless of whether the variables demonstrate I(0) or I(1) characteristics or a combination of both. It remains robust in situations of limited sample sizes, accommodates adjustments for lags in the model, and offers impartial estimates of the long-term model, coupled with reliable t-statistics, even when confronted with endogeneity (Suleman et al 2022). Furthermore, a straightforward linear transformation may be used to convert the ARDL limits testing technique into a dynamic "unrestricted error correction model".…”

“…Whereas, according to Pal and Mitra (2015) and (Suleman et al 2022), Equation ( 4) can be re-written as follows:…”

“…Subsequently, by constraining all calculated coefficients of past level variables to zero (see Pablo-Romero and De Jesús 2016), a test is conducted for the null hypothesis H0: α 1 = α 2 = α 3 = 0, contrasting it with the alternative H1: (Mirza and Kanwal 2017). Moreover, α 1 ECT t−1 (ECT − error correction term) can also be referred to s α 1 SI t−1 as the "adjustment speed" of the model returning to long-term equilibrium (Suleman et al 2022). Suleman et al (2022) suggested that developing a suitable econometric model with an ideal lag structure is essential for accurate parameter identification in the suggested model.…”

“…Moreover, α 1 ECT t−1 (ECT − error correction term) can also be referred to s α 1 SI t−1 as the "adjustment speed" of the model returning to long-term equilibrium (Suleman et al 2022). Suleman et al (2022) suggested that developing a suitable econometric model with an ideal lag structure is essential for accurate parameter identification in the suggested model. By determining the smallest values of AIC (Akaike Information Criteria) and BIC (Bayesian Information Criteria), the optimal lag structure can be determined.…”

“…A key contributing factor to these fluctuations is the impact of energy price variations, including those of oil and natural gas, on European economies, EU-ETS, and the oil price volatility index (Bourghelle et al 2021). Moreover, uncertainties in the global economic conditions post COVID-19 can also contribute to the fluctuations In light of the aforementioned information, when dealing with variables exhibiting different orders of integration, the autoregressive distributive lag model (ARDL) approach emerges as a more practical and resilient method (Suleman et al 2022). The stationary characteristics of returns in the EU-ETS and European financial markets are evident from Figures 1 and 2, respectively.…”