THE CONNECTEDNESS BETWEEN THE SENTIMENT INDEX AND STOCK RETURN VOLATILITY UNDER COVID-19: A TIME-VARYING PARAMETER VECTOR AUTOREGRESSION APPROACH

We analyze the connectedness between the sentiment index and the return and volatility of the crude oil, stock and gold markets by employing the time-varying parameter vector autoregression model vis-à-vis the coronavirus disease (COVID-19) epidemic. Our sentiment index is constructed via text mining technology. We also employ a network to visualize and better understand the structure of the connectedness. The results confirm that the sentiment index is the net pairwise directional connectedness receiver, while the infectious disease equity market volatility tracker is the transmitter. Furthermore, the impact of the COVID-19 pandemic on the total connectedness of volatility is unprecedented.

Stress Spillovers among Financial Markets: Evidence from Spain

Using a unique database, this paper examines the interconnection among stress indicators of the Spanish financial markets during the period of January 1999 to April 2021, applying both the connectedness framework and the Time-Varying Parameter Vector Autoregressive connectedness approach. Our results suggest that 15.67% of the total variance of forecast errors was explained by shocks across the six financial market stress indices examined, indicating that the remaining 84.33% of variation was due to idiosyncratic shocks. Nevertheless, we find that stress connectedness varies over time, with a surge during periods of increasing economic and financial instability, mainly driven by high levels of pandemic and economy policy uncertainty and real economy worsening. Financial intermediaries were the main generators of stress during three out of four recent major financial crises in Spain, while their role as stress transmitters to other markets has been reduced since the onset of the COVID-19 health crisis. Our results also indicate that the COVID-19 outbreak represents a relevant event in the transmission of stress among all market segments.

Structural change and economic growth in India: a comparative study of Punjab

Purpose The Green Revolution transformed agriculture in the Indian State of Punjab, with positive spillovers to the rest of India, but recently the state’s economy has fallen dramatically in rankings of per capita state output. Understanding the trajectory of Punjab’s economy has important lessons for all of India. Economic development is typically associated with changes in economic structure, but Punjab has remained relatively reliant on agriculture rather than shifting economic activity to manufacturing and services, where productivity growth might be greater. Design/methodology/approach The authors empirically examine structural change in the Punjab economy in the context of structural change and economic growth across the States of India. The authors calculate structural change indices and map their pattern over time. The authors estimate panel regressions and time-varying parameter regressions, as well as performing productivity change decompositions into within-sector and structural changes. Findings Panel regressions and time-varying-coefficient regressions suggest a significant positive influence of structural change on state-level growth. In addition, growth positively affected structural change across India’s states. The relative lack of structural change in Punjab’s economy is implicated in its relatively poor recent growth performance. Comparisons with a handful of other states reinforce this conclusion: Punjab’s lack of economic diversification is a plausible explanation for its lagging economic performance. Originality/value This paper performs a novel empirical analysis of structural change and growth, simultaneously using three different approaches: panel regressions, time-varying parameter regressions and productivity decompositions. To the best of the authors’ knowledge, it is the only paper we are aware of that combines these three approaches.

FINANCIAL CYCLES IN THE ECONOMY AND IN ECONOMIC RESEARCH: A CASE STUDY IN CHINA

This work explores the relationship between financial cycles in the economy and in economic research. To this aim, we take China as an empirical example, and an intuitive bibliometric analysis of selected terms concerning financial cycles in economic research is performed first. Both in the economy and in economic research, we then conduct singular spectrum analysis to further isolate and describe the specific length and amplitude of financial cycles for China based on quarterly time-series data. Finally, according to the estimated cycles that detrended by Hodrick-Prescott filter for financial and bibliometric variables, the Granger causality test scrutinizes the results of the first two steps. Moreover, a time-varying parameter vector autoregression model is estimated to quantitatively investigate the time-varying interaction between financial and bibliometric variables. Our study shows that financial cycles have a strong effect on the developments in the financial-related literature. In particular, the 2008 global financial crisis’s impulse intensity is significantly higher than in other periods. Surprisingly, discussions on financial cycles in the literature also have an impact on financial activities in real life. These findings contribute to nascent work on the patterns in financial cycles, thus providing a new and effective insight on the interpretation of financial activities.