Design and Presentation of a Stock Market Volatility Model with Emphasis on the COVID-19 Financial Crisis

Abstract

Objective: The present article addresses the design and presentation of a stock market volatility model with an emphasis on the COVID-19 financial crisis.

Methodology: For this purpose, a qualitative method and thematic analysis model were employed.

Findings: In the first stage, familiarization with the data occurred, followed by the creation of initial codes in the second stage. At this stage, 40 initial codes were extracted from texts and articles. The third stage, known as selective coding, aimed to categorize various codes into selective codes and organize all summarized coded data, resulting in six selective codes. In the fourth stage, sub-themes were formed, involving two sub-stages: reviewing and refining, and shaping the sub-themes. The first sub-stage included a review at the level of summarized coded data, and the second sub-stage considered the validity of sub-themes in relation to the data set, resulting in 15 sub-themes. Ultimately, the model's presentation yielded a Holsti coefficient (PAO) or "percentage agreement observed" of 0.830, which is significant. Considering the criticisms of Holsti's method, Scott's Pi was also calculated, yielding a value of 0.73. The fourth measure of qualitative research validity, Cohen's Kappa, was also calculated, resulting in a value of 0.73. Finally, Krippendorff's Alpha was used, with an estimated value of 0.88 in this study.

Conclusion: The study provides a comprehensive model illustrating the significant impact of COVID-19 on stock market volatility, emphasizing the role of macroeconomic variables, monetary and fiscal policies, and investor behavior. The findings highlight the need for effective policy interventions and strategic investment diversification to mitigate market disruptions during global crises.