As for a prediction, several researchers have identified several predictors that are useful to predict future stock returns. Those include (but are not limited to) dividend yield and dividend-price ratio (Fama and French 1988; Campell and Shiller 1988), price-earnings ratio (Campell and Shiller 1988; Welch and Goyal 2008), short interest rate (Campbell 1987; Ang and Bekaert 2007), term and default spreads (Campbell 1987; Fama and French 1989), and consumption-wealth ratio (Lettau and Ludvigson 2001). Besides predictors, forecasting techniques also play an important role in determining forecast accuracy. According to Mallikarjuna and Rao (2019), traditional regression techniques generally outperform others including artificial intelligence and frequency domain models in providing accurate forecasts. In terms of stock volatility, academic researchers used to make forecasts by traditional GARCH models using indicators based on the past behavior of stock price and volatility (Gokcan 2000; Emenogu et al. 2020). More recent studies become aware of issues such as parametric assumptions, leverage, asymmetric effects, power transformations, and long memory (e.g., Brooks 2007; Bandi and Reno 2012; Hou 2013). In this paper, we introduce GARCH models for volatility forecasting because we aim to test for the instability of the volatility process, which is primarily built upon those modeling techniques.
In addition to g growing number of papers linking COVID-19 and finance relates to stock markets. The literature in this scope contains works published even before the outbreak of the pandemic but suitable for explaining investor behaviors during the COVID period as well as works completed during the pandemic. In the first group, one may find papers addressing the issues of contagion [33], spillovers between markets during shocks [34] as well as the impact of bad news on the time-varying betas [35]. The second, there are papers related to issues of dependencies between global factors and markets [36] or links between individual stock market reactions and the severity of the outbreak of the pandemic in various countries [37]. Moreover, one can list some other works, e.g., related to pricings of stock during the pandemic. Singh [38] found that investors become more attentive to corporate fundamentals and ESG that support the long-run sustainability of firms during turbulence. Fundamental aspects of investments were also pointed out by Mirza et al. [39] who found that social entrepreneurship investment funds outperformed their counterparts during the outbreak of the pandemic. In the field of stock pricing and price tendencies, Shehzad et al. [40] found that the pandemic has influenced the variance of the US, Germany, and Italy’s stock markets stronger than the global financial crisis. Against this background, Narayan et al. [41] and Phan & Narayan [42] found positive effects of lockdowns, travel bans, and economic stimulus packages on stock markets, and Sharif et al. [43] found that in the US the pandemic outbreak has a greater effect on the geopolitical risk and economic uncertainty than the stock market itself.
Moreover, researchers argued that the stock markets are always affected by major events (Haque & Sarwar, 2013; Waheed, Wei, Sarwar, & Lv, 2018). However, as this virus becomes a global pandemic, it starts affecting the businesses which are reflecting in world stock markets. Some studies have examined the impact of COVID-19 on developed stock return (Al‐Awadhi, Al‐Saifi, Al‐Awadhi, & Alhamadi, 2020; Kowalewski & Śpiewanowski, 2020), which reported that the Hang Seng index and Shanghai stock exchange, United States and European stock markets reflect negative returns. In March, the United States marwas ket hita by circuit brake mechanism, four times in 10 days. Similarly, the United Kingdom stock market index, FTSE, has a decline of more than 12% worse after 1987 (Al‐Awadhi et al., 2020).In Pakistan, the first case of COVID‐19 is reported on February 26, 2020, which has crossed the figure of 13,000, till conducting the study. However, the recovery rate is better as compared to developed countries, like Italy, France, and United States. The impact of this pandemic situation on Pakistan's economy depends on the time taken in taking preventive measures and the intensity of spreading the disease. According to the Asian Development Bank (ADB), this pandemic situation can cost the Pakistani economy approximately $16.38 million to $4.95 billion, nearly 1.57% of the overall GDP. The report also mentioned that this pandemic cost more than 946,000 job losses. In this way, a country that is at the recovery stage, in the last 2 years, is affected badly.
This research developed different modeling with several advantages over predictable statistical analysis approaches. Furthermore, the existing traditional model, such as predictive ML is more useful for inferencing patterns or training data rules. Statistical distribution assumptions or postulated functional models provide a statistical regression model arbitrarily and differ from model to model. In contrast, the ML model is extracted from an algorithm centered on accessible data and requires limited user intervention in model development. Moreover, the adopted modeling techniques can be epitomized using a computational model to consider complicated relationships and configurations via indexes, algorithms, and data structures. Another advantage is that the utilized models could be envisioned in an imaginative manner that suits human understanding. Furthermore, the used data mining-based models are fixable in adapting to the required changes as it their algorithms are developed as an automatic method in a computer framework that can be modified in real-time after upgrading the data sources.