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Understanding the Impact of COVID-19 on Financial Markets

View more insights in Social Sciences - Economics - Financial Economics

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View the page of Wasim Ahmad

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Wasim Ahmad investigates how the financial markets react to the coronavirus outbreak by utilizing an event-study approach.

Image courtesy of interviewee. March 18, 2024

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The speaker reflects on their research conducted during the COVID-19 pandemic, particularly analyzing the impact of the pandemic on global financial markets, specifically focusing on "black swan" events—unexpected, significant market disruptions. During the early days of the pandemic in March 2020, the global markets were uncertain and destabilized, prompting the speaker to explore how to assess the associated risks.

The research involved gathering data from credible sources such as the New York Times and The Guardian, utilizing established methodologies like event studies to examine how specific events related to COVID-19 affected stock markets. The speaker emphasized the importance of identifying structural breaks in financial data to understand the market's response to the shock.

The analysis highlighted the significant impact of the pandemic on developed markets, particularly the G7 countries, which experienced severe market declines. In contrast, emerging markets experienced a relatively moderated impact and confusion during the period.

The speaker employed various financial models, including non-linear models and the VIX index, to analyze market behavior and volatility associated with the pandemic. They found evidence of significant market reactions during critical dates, referred to as Black Mondays, where stock market crashes occurred.

Ultimately, the research concludes that the pandemic represented a unique, non-normal shock to the market, revealing differing investor responses based on their behavioral tendencies, such as fear or optimism (termed as "animal spirits"). The findings suggest that while developed markets faced severe declines, emerging markets showed resilience.

The study provided insights for further research, indicating significant implications for market behavior during extraordinary events such as global crises and illustrating the interconnectedness of global markets.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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So here, when I was just looking at that, when we had the,

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COVID-19 spreading across,

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and the global markets were also not very convinced about

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the kind of spread that we saw in March, 2020.

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So at that time, I was just thinking that what to do

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with the given information that I have.

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So one of the important, aspect

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that I looked at was the case of black swan.

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So Black swan is a very important concept in finance

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and why it is considered important

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because such events are not seen on a regular basis.

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So, and it becomes a really important for policy makers,

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for traders, for investors to find out such events

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and what should be the appropriate measure to address that.

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So keeping that in mind, my study,

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I started looking at the data

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and then I thought that I should be figuring out something

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which should not be,

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or we should not have been explored.

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So what I looked from the Covid data that, the,

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the sample period that I had.

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So I was sitting at home during lockdown,

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and then I was pursuing the news

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and I was just following up with developments related

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to COVID-19.

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And then I thought, I, as a researcher,

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what I can contribute to this development that I'm seeing.

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And we, it is very devastating for the global markets

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and economy and for the livelihoods

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and wellbeing of the individuals and the citizens across.

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So it came as, a random idea that

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let's look at what should be the risk side of the shock

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that the financial market is overseeing.

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And, I found, that there are some,

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studies talking about respect theory.

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There are some studies talking about behavioral angle

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that no one had expected that this shock is going

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to persist, persist for a very long period of time.

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So then I, then I explored this idea that let's look at

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in a very generic way that

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how does the COVID-19 outbreak look like?

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And then I went through some new sources.

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I gathered the new details from different sources,

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credible sources, I would say.

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So for example, New York Times has the timelines

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published on the webpage.

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Then I followed the DVC, the guardian newspapers.

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And these, dailies had,

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I would say credible information available.

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So in the empirical literature, if you look at from the qu

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economic side or finance side, people have analyzed

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in the literature different types of mythologies.

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And one of the important mythologies is the event study.

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So I figure it out that if I can identify those events,

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and if I can see that which of those events are going

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to impact the financial markets, then I will be

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Able to say something about that.

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Yes, COVID-19 impacted the global finance and market.

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And in that process, I started looking at the detailed data.

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So I thought I should have the sample of pan trees,

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I should also have the assets.

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And when I went through the online sources, the,

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the journals, then I saw a lot of papers started,

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flooding on this topic.

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And a lot of good journals also had an eye on this topic.

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So I thought I should have the separate perspective,

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and then I should try to look at, luckily,

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when I had the access to data,

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then I downloaded the data on G seven countries

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and the emerging markets.

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And I also gathered data on the asset side,

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which included bonds, crude oil, commodities, bullions,

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and then, mostly I would say, bullions

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and also the Forex that

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how these assets are also reacting to this particular shock,

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which is COVID-19.

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So what happened that in the literature, when you go

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and read them, you find that even a study is a very,

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broad topic.

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And in that, what is a specific is that if you want to see

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that if there is an event occurring, whether

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that event when it occurred, whether it was significant

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to certain, phenomenon, for example,

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financial market down, financial market up

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or financial market, you can say crash.

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That is extreme what we often you.

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So if that event appeared to be significant, then we say

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that it is, this particular event had impacted.

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So there is a whole lot of literature on that.

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But beforehand, I also wanted to check

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that if I'm gathering the details from a New York Times DBC

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and the Guardian, that whether these events are also

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credible, should I go and blindly use those events?

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Then I thought, no, as an academician,

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I should also put some effort.

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So then, then I, then I, I started working on

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the structural break model.

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In the literature in econometric,

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there are studies talking about structural break models

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and mostly ingenious structural break models.

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So there are already the ingenious break models where you

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have the breaks ready,

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and then you just try to see the

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significance of those breaks.

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So if I just mentioned literature, so it started with chow.

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So chow first gave this idea, then it went to pond.

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Uh, everyone who worked in this field, they understand that

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what is, what I'm talking about.

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And also in 2000, in 1998 by,

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and Peron gave a very interesting model,

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and which very simple, but the challenge was

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that it was linear in nature.

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So, but though linear in nature, this model gave a lot

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of insights that how one can look at the occurrence

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of different abnormal periods, in a data, in a series,

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if you have a time series,

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and if you want to examine

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that which one periods had very high or significant ups

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and down, then you can also look at,

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and then it becomes really important to analyze that,

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how one can identify those structural break events.

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It wa it works on sequential procedure under

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that you just estimate the model through OLS

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or linear model, but you can just put in a cross,

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no, no break against one break, one break against two,

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break two break against, three break.

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Uh, so it goes like, two breaks versus three brick.

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So these are not hypothesis,

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but these tests are based on f statistics, which is,

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some kind of parametric exercise.

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But then I thought I should not be just relying on it

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because COVID-19 seems

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to be more notorious in terms of spread.

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And by that time, the Italy was struggling

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with a very bad face.

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I should, I, I I should mention that

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because it was really a big shock for Italy

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and US government had banned the travel to Europe.

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And that time, the, that time,

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the financial market started accepting those

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shocks into the prices.

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So the pricing of the stock started gathering the details

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of the COVID-19.

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And because of that, if you remember March 12th

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and March 16th and March 23, so it started with March nine

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to 16 and 23,

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the stock market al almost had a black salon event of

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the 1987, it is called Black Monday also.

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And at that time, the stock market had fallen

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by more than in 2020, when, which I'm talking about

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the, the stock market of us had gone down by more than 25%.

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And that was incredible

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because it was never seen in the recent, even,

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during global financial crisis,

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it was not such a very small period event occurring.

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And then you have a significant fall in the stock crisis.

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So that was the case.

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So then I thought, okay,

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I should not be just stopping at a structure break model.

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I should be also looking from the non-linear perspective

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because the data will have the non-linear characteristic.

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So I wanted to accept the non-linearity of the data

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of the COVID-19.

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So I took, I applied also the,

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it's called bar regime switching model.

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And, it was given by Hamilton in 1997 in his,

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very in 19 80, 90 first.

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Then later in 1997, it also came a statistician called

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Nik Added value to this model for them with the,

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given the lag strength that the model accepts.

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And then with that model, you are able

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to identify the break in unanimously without knowing.

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And luckily I found that, in March, 2020, the event

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that I just mentioned, 9, 12, 16

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and 23, I could see a very significant up in the,

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in the probabilities is smooth probabilities of the,

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the most of the, most of the VIX indices that I considered

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during that time for the markets, especially for US, UK

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and other markets quite high.

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And this was an eye-opening for me that, okay, yes,

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now I have the, I have the platform ready

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and now I should go for the next step.

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So what I did was that I estimated all these,

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I took all these structure break.

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I again put it across the timelines of most of the,

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the dailies or the media channels

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that I just mentioned mentioned.

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And, and I found that, I was on track

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with most of the dates, and I also read in the newspaper

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and some online sources that in March, 2020, it appeared

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to be really a big shock for the financial market.

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Then I thought, I have identified the black swan,

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let's talk about animal spirits,

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because now here we have the case

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that the market experience the shop, how do we capture?

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So in prospect theory, which is one

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of the behavioral important behavioral,

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finance theories, it mentions that the investors

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or the individual, when he

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or she reacts to a particular shock, he

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or she can see that shock in, two ways.

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One is that what is called narrow framing, where you see

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the, the shock in isolation.

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And one is about the greed over fear that we,

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you call it tag the animal spirits.

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So animal spirits, if I just, spend,

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I would say 32nd time, so here it is about,

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so it started with the, the, I would say the, the father

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of modern economics called ins, especially in macro,

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some father of modern macroeconomics, he's called

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so British economies, who is well known

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for his contributions.

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So John Mayard kings, John Mayard kings, he gave this idea,

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and John's Mayard kings at

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that time mentioned about the animal spirits,

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but Robert Siller,

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and the, the took a very different turn.

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And then they also added value to the contributions

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of John Mayard kings.

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And then they said that it is not just the animal spirit,

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but it is also the level of confidence

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that investors would have when they are experiencing such

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odd events and whether they want to remain in the game

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or they want to be out

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of the game when they see such events.

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So what is typically happening that, black swan,

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if you just try to link with animal spreads,

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it has two dimensions.

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When the stock markets fall, investors would've two choices.

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Those who have taken a short call,

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they will be making a lot of money.

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Those who have taken the buy call,

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which is called long call, they'll be losing money because,

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and it, it, the loss I'm talking about it is notional,

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not actual notional that you are,

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you are remain in the investment

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and you are, you are not, square of your position,

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but the, the upturn

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and downturn would have,

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it'll have impact on your investment exposure.

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So blackson as such, it is not always a negative side

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of the finance that we always see.

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Blackson also highlights just the event that,

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and if you just go by the normal idea that how,

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the, the occurrence of, I would, I would say the finding

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of black swan, it was always considered

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as only the existence of white swan.

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But somebody explored in Australia that, there exists,

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I would say, it is called black, so on also.

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So then the, then the people thought

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that it is not impossible to find something rare.

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And in finance,

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they adopted this idea considering the existence of

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a rarest to rare event.

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And in that case, it appears to be very relevant,

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but in finance we always say the

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downside is somebody's profit.

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And upside is, is somebody's, profit,

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but both sides have a profit and loss.

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So let's think about the animal split.

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So animal splits, I was talking about,

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if you connect this particular aspect of black swan,

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for some people, those who are greedy,

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they would always like to buy more and more

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because they know that in future it'll appreciate,

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so market is going to get more liquidity.

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If you're thinking about the,

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so here you have the optimistic side

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that people are expecting

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that price will appreciate in future,

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and this is the best time to enter

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because price has gone down.

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So you have a, you are getting things at a discounted rate,

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but from the, from the pessimist side, it is always,

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the, I would say other side

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of the story wherein the pessimist will always think that,

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okay, it is going to persist for a longer period of time,

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necessarily, I, my money will be stuck in the market.

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So let's utilize it for some other purposes.

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So maybe you'll be opting it for a risk-free investment.

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And this is, where is the idea?

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I thought I should be figuring out

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that whether the investors are looking, at this event

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of the COVID-19 coronavirus spread as an in isolation,

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as a narrow framing, or they're going for animal spreads,

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that it doesn't matter whether the government will come

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for SQ or not, I should be keep on investing

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and looking for better scenario.

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So both existed in case of, in case

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of emerging markets, I come from India.

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I saw that during COVID-19,

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the Indian stock market already had a

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opton, and it was going up.

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We signify every signify that yes, there was a event

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of animal spread in the case of India.

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Now, let's come back to mythologies that I was discussing

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because, we are discussing

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and we, I will be also touching upon the outcomes law

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because that's very important.

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So here, what is, what we did was that I

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identified the structural breaks.

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I also compared that with the existence of different types

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of new sources that we had.

15:35.925 --> 15:37.815
Then I thought I should be now checking

15:38.275 --> 15:40.375
for the significance of these events.

15:41.035 --> 15:42.975
So I had, 10 to 12 events,

15:43.675 --> 15:46.295
and then I took from the February,

15:46.955 --> 15:50.375
and then I had data till April, 2020,

15:50.915 --> 15:54.615
and I explored, I was aware about the event study mythology.

15:54.915 --> 15:57.255
So I thought, let's check for the significant.

15:57.915 --> 16:02.615
So I, I explored the event study,

16:02.915 --> 16:04.495
we call it at ESM methodology.

16:04.795 --> 16:07.415
So even study mythology I explored in

16:07.415 --> 16:10.335
that you have certain requirements, certain pay requisites.

16:10.525 --> 16:11.775
What are those requirements?

16:12.355 --> 16:14.775
One requirement is that you should have clear cut

16:15.095 --> 16:16.695
segregation between normal period

16:17.075 --> 16:19.455
and the shock period that you are examining.

16:19.915 --> 16:23.495
So at that time, I took the data from 2019

16:24.855 --> 16:28.715
and then I, I took the data till April, 2020.

16:29.775 --> 16:31.515
My gut feeling was

16:31.515 --> 16:34.235
that we should be targeting from January, 2020

16:34.745 --> 16:38.555
because from that time onwards, the COVID-19 news started,

16:38.775 --> 16:40.715
coming in news dailies

16:40.715 --> 16:44.195
and it became the important topic to be discussed.

16:44.575 --> 16:45.675
But in December, though,

16:45.675 --> 16:47.915
it started in December from Wuhan in China,

16:48.415 --> 16:50.155
but at that time people had apprehension

16:50.185 --> 16:53.985
that it'll not spread that much, it'll not be going up

16:53.985 --> 16:55.025
to that magnitude.

16:55.565 --> 16:59.865
So then I, then I had also discussion with my co-authors

16:59.865 --> 17:02.785
and they said that list target from January mid,

17:03.285 --> 17:05.465
and we should have the normal period gap.

17:05.605 --> 17:08.505
So if you are going for normal period identification.

17:09.005 --> 17:13.265
So we did that keeping in mind the clear cut demarcation

17:13.335 --> 17:17.705
between the normal period, which is like pre,

17:17.705 --> 17:20.625
COVID-19 period and the covid period.

17:21.205 --> 17:23.625
So for that, we took almost seven days break.

17:24.085 --> 17:28.825
So we calculated till, January, you can say,

17:29.045 --> 17:30.665
the beginning of the January,

17:31.165 --> 17:34.025
and then we started taking into account after that.

17:34.935 --> 17:37.195
So here in even study mythology,

17:37.195 --> 17:40.115
there is a very important term called abnormal return.

17:40.375 --> 17:42.955
So I should be calculating for the abnormal return,

17:43.375 --> 17:45.755
and then I will be in a certain window

17:46.015 --> 17:48.795
of plus minus three days or plus minus one days,

17:49.435 --> 17:50.435
Plus minus. I'm

17:50.435 --> 17:53.445
taking talking about pre of the event

17:54.295 --> 17:55.635
and the post of the event.

17:56.175 --> 17:57.755
So pre period of the event

17:57.755 --> 17:59.675
and post period of the event I'm talking about.

18:00.095 --> 18:03.275
So that is called cumulative ab, ab average later

18:04.195 --> 18:05.355
abnormal return, so sorry,

18:05.575 --> 18:08.835
not average is called CU cumulative abnormal returns.

18:08.895 --> 18:10.795
So CAR we normally mention

18:11.655 --> 18:15.195
and abnormal return ar it is calculated by,

18:16.255 --> 18:20.165
by taking into account the estimated values

18:20.545 --> 18:22.765
or the estimated coefficient that you obtain

18:22.825 --> 18:25.235
for the normal period, okay?

18:25.455 --> 18:27.675
So that we call it a expected return.

18:28.095 --> 18:31.275
And you already have the abnormal,

18:31.535 --> 18:34.315
you already have the returns of those three

18:34.345 --> 18:36.595
that you are considering for the markets.

18:37.215 --> 18:41.555
So if you just take into account the, the, the long return

18:41.555 --> 18:45.315
that you calculated minus the expected return based on the

18:45.315 --> 18:47.835
estimated regression that you have for the normal period,

18:48.455 --> 18:51.435
and you multiply that by the proficiency of those returns

18:51.435 --> 18:52.995
that you have considered as the

18:53.425 --> 18:55.235
exploratory variable in the regression.

18:55.695 --> 19:00.115
So then you get the, then, then, then, then,

19:00.115 --> 19:01.915
then you get the abnormal return,

19:02.175 --> 19:05.515
and then you could, appears to be negative, which means that

19:06.025 --> 19:09.595
your return is much lower than the expected return.

19:09.615 --> 19:12.555
And this will create a series for you

19:12.615 --> 19:14.155
to analyze different events.

19:14.295 --> 19:17.035
And that we did it in the beginning,

19:17.075 --> 19:19.195
I thought I should be considering a longer period.

19:19.335 --> 19:23.435
So I started with plus minus, five days.

19:23.505 --> 19:25.795
Then I went for plus minus three days,

19:25.795 --> 19:30.075
and I also went for plus minus one days just immediate prior

19:30.075 --> 19:31.835
to the event and post to that event.

19:33.125 --> 19:34.705
And we had very interesting results.

19:35.005 --> 19:37.985
But let me, spend 32nd time for the data

19:37.985 --> 19:40.145
because I did not explain the data clearly.

19:41.145 --> 19:44.765
So data, we considered, the VIX index,

19:44.765 --> 19:47.165
which is also considered as the fear index.

19:48.165 --> 19:49.985
And what HA has happened that

19:50.155 --> 19:51.865
after the occurrence

19:51.865 --> 19:56.025
of different events post 2007 eight global financial crisis,

19:56.365 --> 19:58.585
one index, which has become very popular

19:59.475 --> 20:02.625
among the risk analyzers is the VIX index.

20:02.765 --> 20:04.145
It is ity index.

20:04.605 --> 20:07.185
It is also called an employed ity index

20:07.185 --> 20:09.945
because it, it, the value is derived from the

20:10.455 --> 20:12.265
options market called input.

20:12.765 --> 20:15.545
And COE calculates that

20:15.605 --> 20:18.545
for the SMP 500 index.

20:18.885 --> 20:22.905
And now we have host of VIX indicis available

20:23.125 --> 20:27.585
for Forex, available for, even for the commodities, even

20:27.685 --> 20:31.025
for the crude oil we have now via x indicis.

20:31.365 --> 20:33.345
So indicis I'm talking about.

20:33.345 --> 20:34.545
So I considered that

20:34.895 --> 20:37.185
because it has the forward looking scenario

20:37.185 --> 20:40.145
because it is coming from the options market.

20:40.365 --> 20:44.425
So call and put, so 30 days forward looking scenarios,

20:44.425 --> 20:47.685
you already have incorporated data, which means

20:47.685 --> 20:50.805
that you already have the city which is having a 30 day

20:50.805 --> 20:52.325
forward looking scenario, then it is

20:52.325 --> 20:53.365
going to be very helpful.

20:54.105 --> 20:58.045
So I did that. I, I calculated the,

20:58.045 --> 21:02.145
and I considered the G seven countries, which is Canada, uk,

21:02.405 --> 21:06.825
us and then you have the Germany, Italy, and China.

21:07.045 --> 21:09.305
And then we have the emerging markets.

21:09.505 --> 21:13.785
I considered bra, Russia, India, South Africa, South Korea,

21:13.925 --> 21:15.225
all these countries are part of,

21:15.765 --> 21:20.705
but for my analysis I did, I chose the US market as

21:21.225 --> 21:23.385
priority variable for explaining rest

21:23.685 --> 21:25.225
of the markets are then.

21:25.925 --> 21:28.785
So the rest of the markets were dependent variable

21:29.125 --> 21:30.785
and the US was explanatory variable

21:30.845 --> 21:34.145
for even study calculation, the abnormal return calculation.

21:34.685 --> 21:37.545
So for us, it is always going

21:37.545 --> 21:41.145
to be implicitly covered from the UK and Canada

21:41.145 --> 21:43.345
because all these markets are interlinked.

21:44.045 --> 21:48.585
So we found that the abnormal returns of most of the markets

21:49.255 --> 21:53.465
were negative, especially UK and Canada,

21:54.325 --> 21:57.505
and you can say US is is highly linked

21:57.505 --> 21:59.265
with Canada, and Canada.

22:00.185 --> 22:03.785
Canadian investors are also heavily dependent on US market.

22:04.405 --> 22:06.825
So anyway, implicitly we are able to see

22:06.825 --> 22:09.105
that the US market was down in March

22:09.215 --> 22:11.185
because AR were mostly negative.

22:11.765 --> 22:15.425
And then we also found in case of UK for Germany,

22:15.605 --> 22:18.825
we found on 18 March some of the AR values

22:19.005 --> 22:20.785
and CAR values were negative.

22:20.965 --> 22:22.305
And here the significance

22:22.405 --> 22:25.825
of the event determined based on the TA statistics,

22:26.485 --> 22:27.905
the TA statistics, they appear

22:27.905 --> 22:29.185
to be statistically significant.

22:30.375 --> 22:33.915
And then we say that it is, a significant event

22:34.175 --> 22:36.475
for the market that we have considered.

22:36.975 --> 22:40.075
So we found among asset classes, we found

22:40.075 --> 22:44.395
that bonds started getting the signals from the shock

22:44.705 --> 22:49.515
because then the bonds also had, the negative impact,

22:49.515 --> 22:52.315
though later it was, it became the safe haven.

22:52.455 --> 22:56.155
So majority of the investments started floating in bonds,

22:56.895 --> 22:59.475
and then crude oil became one of the important victims

22:59.815 --> 23:03.115
of the shock and crude oil went down in March a lot.

23:03.575 --> 23:05.395
And then we all can remember

23:05.395 --> 23:08.955
that period when the oil prices were historically down.

23:09.575 --> 23:13.795
And then with these inferences that what we did,

23:14.895 --> 23:18.595
we found that the COVID-19 was not a normal shock

23:19.415 --> 23:23.315
and it gave a lot of, unprecedented, kind

23:23.315 --> 23:25.075
of experiences to investors.

23:26.375 --> 23:29.875
And emerging markets were not that badly affected.

23:30.015 --> 23:33.235
So it started with the US uk so G seven countries.

23:33.855 --> 23:35.915
We can conclude from this study

23:36.065 --> 23:38.275
that G seven countries were having a

23:38.275 --> 23:39.435
very significant impact.

23:40.135 --> 23:43.755
The emerging markets were on the wait and watch.

23:44.415 --> 23:46.005
So I think that gave them,

23:46.065 --> 23:48.885
and then they followed the majors of the developed world,

23:49.145 --> 23:50.685
so mostly the G seven countries.

23:50.685 --> 23:53.645
So because of that, they were able to disentangle,

23:54.015 --> 23:56.005
which we call it as the contagious

23:56.705 --> 23:59.005
or the contagion effect that we should have seen.

23:59.345 --> 24:01.805
We did not see that. So this was the case

24:01.985 --> 24:04.485
and then across, asset class,

24:04.485 --> 24:08.045
because at that time, the markets were down.

24:08.045 --> 24:09.765
So it was obvious that they,

24:09.935 --> 24:12.845
these assets will also have the, the strain

24:13.025 --> 24:16.005
and they will be also experiencing a bad phase.

24:16.425 --> 24:19.405
But the most important, the explanation was that

24:20.105 --> 24:24.275
the market that we saw, that the,

24:24.295 --> 24:27.755
the markets which were not doing good, it, it,

24:27.765 --> 24:30.275
these markets were located in developed, markets.

24:30.535 --> 24:34.515
And this study also opened the, the,

24:34.515 --> 24:38.995
this study also opened the eye for the, some

24:38.995 --> 24:40.755
of the other studies that were conducted.

24:40.815 --> 24:44.835
So in 2021, we found that the ma

24:44.835 --> 24:49.555
study appeared in examining the MAs event,

24:49.895 --> 24:51.235
in finance, which just letters.

24:51.455 --> 24:55.395
And then it, it analyzed the, the case

24:55.535 --> 24:57.635
for the forms at the form level.

24:57.655 --> 24:58.875
The study was conducted

24:59.335 --> 25:02.915
and we found that, what we did in our study,

25:03.675 --> 25:08.155
and black, so on, they mentioned specifically Black Monday,

25:08.725 --> 25:10.675
black Thursday, and again Black Monday

25:11.755 --> 25:15.995
considering Mars nine, Mars 12, Mars 16, and Mars 22.

25:16.175 --> 25:19.715
So that gave me a lot of satisfaction that my work finally

25:20.265 --> 25:22.395
gave the idea to somebody else to,

25:23.175 --> 25:25.915
and that paper is one of the most cited papers also.

25:26.335 --> 25:28.835
And, coming to the animal spirits, we found

25:28.835 --> 25:32.035
that since these events were quite significant,

25:32.175 --> 25:33.715
but not in emerging markets.

25:34.575 --> 25:35.915
So for the US

25:35.935 --> 25:38.755
and UK investors, these events were quite significant

25:38.855 --> 25:41.915
and they might have gone for a either narrow framing

25:42.575 --> 25:44.555
or some kind of animal spreads,

25:44.815 --> 25:47.995
but animal spreads were seen clearly in the case

25:48.015 --> 25:51.395
of emerging markets because they were not that much bothered

25:51.895 --> 25:54.145
because most of the significance

25:54.745 --> 25:56.465
statistics based on the data we found,

25:57.045 --> 26:00.545
and this gave, me some kind of satisfaction

26:00.645 --> 26:04.185
as a researcher that I was able to at least conclude

26:04.215 --> 26:07.105
what I started having the hypothesis that

26:07.165 --> 26:09.545
how these events have impacted the stock market.

26:10.045 --> 26:12.225
And this is, what I would like to say.

26:12.245 --> 26:14.665
And this study will further extended

26:14.925 --> 26:17.545
and I wrote further on the US sectors.

26:18.225 --> 26:20.025
I also examined the Asian markets

26:20.545 --> 26:22.025
considering the same type of mythology.

26:22.485 --> 26:26.025
And I found that most of the results were quite significant

26:26.125 --> 26:27.345
and I was very satisfied.

00:00 - So here, when I was just looking at that, when we had the, COVID-19 spreading across, and the global markets were also not very convinced about the kind of spread that we saw in March, 2020. So at that time, I was just thinking that what to do with the given information that I have.

00:18 - So one of the important, aspect that I looked at was the case of black swan. So Black swan is a very important concept in finance and why it is considered important because such events are not seen on a regular basis. So, and it becomes a really important for policy makers, for traders, for investors to find out such events and what should be the appropriate measure to address that. So keeping that in mind, my study, I started looking at the data

00:50 - and then I thought that I should be figuring out something which should not be, or we should not have been explored. So what I looked from the Covid data that, the, the sample period that I had. So I was sitting at home during lockdown, and then I was pursuing the news and I was just following up with developments related to COVID-19. And then I thought, I, as a researcher,

01:16 - what I can contribute to this development that I'm seeing. And we, it is very devastating for the global markets and economy and for the livelihoods and wellbeing of the individuals and the citizens across. So it came as, a random idea that let's look at what should be the risk side of the shock that the financial market is overseeing. And, I found, that there are some, studies talking about respect theory. There are some studies talking about behavioral angle

01:50 - that no one had expected that this shock is going to persist, persist for a very long period of time. So then I, then I explored this idea that let's look at in a very generic way that how does the COVID-19 outbreak look like? And then I went through some new sources. I gathered the new details from different sources, credible sources, I would say. So for example, New York Times has the timelines published on the webpage.

02:19 - Then I followed the DVC, the guardian newspapers. And these, dailies had, I would say credible information available. So in the empirical literature, if you look at from the qu economic side or finance side, people have analyzed in the literature different types of mythologies. And one of the important mythologies is the event study. So I figure it out that if I can identify those events, and if I can see that which of those events are going to impact the financial markets, then I will be

02:58 - Able to say something about that. Yes, COVID-19 impacted the global finance and market. And in that process, I started looking at the detailed data. So I thought I should have the sample of pan trees, I should also have the assets. And when I went through the online sources, the, the journals, then I saw a lot of papers started, flooding on this topic. And a lot of good journals also had an eye on this topic. So I thought I should have the separate perspective,

03:30 - and then I should try to look at, luckily, when I had the access to data, then I downloaded the data on G seven countries and the emerging markets. And I also gathered data on the asset side, which included bonds, crude oil, commodities, bullions, and then, mostly I would say, bullions and also the Forex that how these assets are also reacting to this particular shock, which is COVID-19.

04:00 - So what happened that in the literature, when you go and read them, you find that even a study is a very, broad topic. And in that, what is a specific is that if you want to see that if there is an event occurring, whether that event when it occurred, whether it was significant to certain, phenomenon, for example, financial market down, financial market up or financial market, you can say crash. That is extreme what we often you.

04:29 - So if that event appeared to be significant, then we say that it is, this particular event had impacted. So there is a whole lot of literature on that. But beforehand, I also wanted to check that if I'm gathering the details from a New York Times DBC and the Guardian, that whether these events are also credible, should I go and blindly use those events? Then I thought, no, as an academician, I should also put some effort. So then, then I, then I, I started working on

05:02 - the structural break model. In the literature in econometric, there are studies talking about structural break models and mostly ingenious structural break models. So there are already the ingenious break models where you have the breaks ready, and then you just try to see the significance of those breaks. So if I just mentioned literature, so it started with chow. So chow first gave this idea, then it went to pond.

05:30 - everyone who worked in this field, they understand that what is, what I'm talking about. And also in 2000, in 1998 by, and Peron gave a very interesting model, and which very simple, but the challenge was that it was linear in nature. So, but though linear in nature, this model gave a lot of insights that how one can look at the occurrence of different abnormal periods, in a data, in a series, if you have a time series,

06:03 - and if you want to examine that which one periods had very high or significant ups and down, then you can also look at, and then it becomes really important to analyze that, how one can identify those structural break events. It wa it works on sequential procedure under that you just estimate the model through OLS or linear model, but you can just put in a cross, no, no break against one break, one break against two, break two break against, three break.

06:33 - so it goes like, two breaks versus three brick. So these are not hypothesis, but these tests are based on f statistics, which is, some kind of parametric exercise. But then I thought I should not be just relying on it because COVID-19 seems to be more notorious in terms of spread. And by that time, the Italy was struggling with a very bad face. I should, I, I I should mention that

07:01 - because it was really a big shock for Italy and US government had banned the travel to Europe. And that time, the, that time, the financial market started accepting those shocks into the prices. So the pricing of the stock started gathering the details of the COVID-19. And because of that, if you remember March 12th and March 16th and March 23, so it started with March nine to 16 and 23,

07:29 - the stock market al almost had a black salon event of the 1987, it is called Black Monday also. And at that time, the stock market had fallen by more than in 2020, when, which I'm talking about the, the stock market of us had gone down by more than 25%. And that was incredible because it was never seen in the recent, even, during global financial crisis, it was not such a very small period event occurring. And then you have a significant fall in the stock crisis.

08:07 - So that was the case. So then I thought, okay, I should not be just stopping at a structure break model. I should be also looking from the non-linear perspective because the data will have the non-linear characteristic. So I wanted to accept the non-linearity of the data of the COVID-19. So I took, I applied also the, it's called bar regime switching model. And, it was given by Hamilton in 1997 in his,

08:40 - very in 19 80, 90 first. Then later in 1997, it also came a statistician called Nik Added value to this model for them with the, given the lag strength that the model accepts. And then with that model, you are able to identify the break in unanimously without knowing. And luckily I found that, in March, 2020, the event that I just mentioned, 9, 12, 16 and 23, I could see a very significant up in the, in the probabilities is smooth probabilities of the,

09:16 - the most of the, most of the VIX indices that I considered during that time for the markets, especially for US, UK and other markets quite high. And this was an eye-opening for me that, okay, yes, now I have the, I have the platform ready and now I should go for the next step. So what I did was that I estimated all these, I took all these structure break. I again put it across the timelines of most of the, the dailies or the media channels

09:48 - that I just mentioned mentioned. And, and I found that, I was on track with most of the dates, and I also read in the newspaper and some online sources that in March, 2020, it appeared to be really a big shock for the financial market. Then I thought, I have identified the black swan, let's talk about animal spirits, because now here we have the case that the market experience the shop, how do we capture? So in prospect theory, which is one

10:21 - of the behavioral important behavioral, finance theories, it mentions that the investors or the individual, when he or she reacts to a particular shock, he or she can see that shock in, two ways. One is that what is called narrow framing, where you see the, the shock in isolation. And one is about the greed over fear that we, you call it tag the animal spirits. So animal spirits, if I just, spend,

10:52 - I would say 32nd time, so here it is about, so it started with the, the, I would say the, the father of modern economics called ins, especially in macro, some father of modern macroeconomics, he's called so British economies, who is well known for his contributions. So John Mayard kings, John Mayard kings, he gave this idea, and John's Mayard kings at that time mentioned about the animal spirits, but Robert Siller,

11:23 - and the, the took a very different turn. And then they also added value to the contributions of John Mayard kings. And then they said that it is not just the animal spirit, but it is also the level of confidence that investors would have when they are experiencing such odd events and whether they want to remain in the game or they want to be out of the game when they see such events. So what is typically happening that, black swan,

11:53 - if you just try to link with animal spreads, it has two dimensions. When the stock markets fall, investors would've two choices. Those who have taken a short call, they will be making a lot of money. Those who have taken the buy call, which is called long call, they'll be losing money because, and it, it, the loss I'm talking about it is notional, not actual notional that you are, you are remain in the investment

12:21 - and you are, you are not, square of your position, but the, the upturn and downturn would have, it'll have impact on your investment exposure. So blackson as such, it is not always a negative side of the finance that we always see. Blackson also highlights just the event that, and if you just go by the normal idea that how, the, the occurrence of, I would, I would say the finding of black swan, it was always considered

12:57 - as only the existence of white swan. But somebody explored in Australia that, there exists, I would say, it is called black, so on also. So then the, then the people thought that it is not impossible to find something rare. And in finance, they adopted this idea considering the existence of a rarest to rare event. And in that case, it appears to be very relevant, but in finance we always say the

13:26 - downside is somebody's profit. And upside is, is somebody's, profit, but both sides have a profit and loss. So let's think about the animal split. So animal splits, I was talking about, if you connect this particular aspect of black swan, for some people, those who are greedy, they would always like to buy more and more because they know that in future it'll appreciate, so market is going to get more liquidity.

13:52 - If you're thinking about the, so here you have the optimistic side that people are expecting that price will appreciate in future, and this is the best time to enter because price has gone down. So you have a, you are getting things at a discounted rate, but from the, from the pessimist side, it is always, the, I would say other side of the story wherein the pessimist will always think that,

14:16 - okay, it is going to persist for a longer period of time, necessarily, I, my money will be stuck in the market. So let's utilize it for some other purposes. So maybe you'll be opting it for a risk-free investment. And this is, where is the idea? I thought I should be figuring out that whether the investors are looking, at this event of the COVID-19 coronavirus spread as an in isolation, as a narrow framing, or they're going for animal spreads, that it doesn't matter whether the government will come

14:48 - for SQ or not, I should be keep on investing and looking for better scenario. So both existed in case of, in case of emerging markets, I come from India. I saw that during COVID-19, the Indian stock market already had a opton, and it was going up. We signify every signify that yes, there was a event of animal spread in the case of India. Now, let's come back to mythologies that I was discussing

15:17 - because, we are discussing and we, I will be also touching upon the outcomes law because that's very important. So here, what is, what we did was that I identified the structural breaks. I also compared that with the existence of different types of new sources that we had. Then I thought I should be now checking for the significance of these events. So I had, 10 to 12 events,

15:43 - and then I took from the February, and then I had data till April, 2020, and I explored, I was aware about the event study mythology. So I thought, let's check for the significant. So I, I explored the event study, we call it at ESM methodology. So even study mythology I explored in that you have certain requirements, certain pay requisites. What are those requirements? One requirement is that you should have clear cut

16:15 - segregation between normal period and the shock period that you are examining. So at that time, I took the data from 2019 and then I, I took the data till April, 2020. My gut feeling was that we should be targeting from January, 2020 because from that time onwards, the COVID-19 news started, coming in news dailies and it became the important topic to be discussed. But in December, though,

16:45 - it started in December from Wuhan in China, but at that time people had apprehension that it'll not spread that much, it'll not be going up to that magnitude. So then I, then I had also discussion with my co-authors and they said that list target from January mid, and we should have the normal period gap. So if you are going for normal period identification. So we did that keeping in mind the clear cut demarcation between the normal period, which is like pre,

17:17 - COVID-19 period and the covid period. So for that, we took almost seven days break. So we calculated till, January, you can say, the beginning of the January, and then we started taking into account after that. So here in even study mythology, there is a very important term called abnormal return. So I should be calculating for the abnormal return, and then I will be in a certain window of plus minus three days or plus minus one days,

17:49 - Plus minus. I'm taking talking about pre of the event and the post of the event. So pre period of the event and post period of the event I'm talking about. So that is called cumulative ab, ab average later abnormal return, so sorry, not average is called CU cumulative abnormal returns. So CAR we normally mention and abnormal return ar it is calculated by,

18:16 - by taking into account the estimated values or the estimated coefficient that you obtain for the normal period, okay? So that we call it a expected return. And you already have the abnormal, you already have the returns of those three that you are considering for the markets. So if you just take into account the, the, the long return that you calculated minus the expected return based on the estimated regression that you have for the normal period,

18:48 - and you multiply that by the proficiency of those returns that you have considered as the exploratory variable in the regression. So then you get the, then, then, then, then, then you get the abnormal return, and then you could, appears to be negative, which means that your return is much lower than the expected return. And this will create a series for you to analyze different events. And that we did it in the beginning,

19:17 - I thought I should be considering a longer period. So I started with plus minus, five days. Then I went for plus minus three days, and I also went for plus minus one days just immediate prior to the event and post to that event. And we had very interesting results. But let me, spend 32nd time for the data because I did not explain the data clearly. So data, we considered, the VIX index, which is also considered as the fear index.

19:48 - And what HA has happened that after the occurrence of different events post 2007 eight global financial crisis, one index, which has become very popular among the risk analyzers is the VIX index. It is ity index. It is also called an employed ity index because it, it, the value is derived from the options market called input. And COE calculates that

20:15 - for the SMP 500 index. And now we have host of VIX indicis available for Forex, available for, even for the commodities, even for the crude oil we have now via x indicis. So indicis I'm talking about. So I considered that because it has the forward looking scenario because it is coming from the options market. So call and put, so 30 days forward looking scenarios, you already have incorporated data, which means

20:47 - that you already have the city which is having a 30 day forward looking scenario, then it is going to be very helpful. So I did that. I, I calculated the, and I considered the G seven countries, which is Canada, uk, us and then you have the Germany, Italy, and China. And then we have the emerging markets. I considered bra, Russia, India, South Africa, South Korea, all these countries are part of, but for my analysis I did, I chose the US market as

21:21 - priority variable for explaining rest of the markets are then. So the rest of the markets were dependent variable and the US was explanatory variable for even study calculation, the abnormal return calculation. So for us, it is always going to be implicitly covered from the UK and Canada because all these markets are interlinked. So we found that the abnormal returns of most of the markets were negative, especially UK and Canada,

21:54 - and you can say US is is highly linked with Canada, and Canada. Canadian investors are also heavily dependent on US market. So anyway, implicitly we are able to see that the US market was down in March because AR were mostly negative. And then we also found in case of UK for Germany, we found on 18 March some of the AR values and CAR values were negative. And here the significance

22:22 - of the event determined based on the TA statistics, the TA statistics, they appear to be statistically significant. And then we say that it is, a significant event for the market that we have considered. So we found among asset classes, we found that bonds started getting the signals from the shock because then the bonds also had, the negative impact, though later it was, it became the safe haven. So majority of the investments started floating in bonds,

22:56 - and then crude oil became one of the important victims of the shock and crude oil went down in March a lot. And then we all can remember that period when the oil prices were historically down. And then with these inferences that what we did, we found that the COVID-19 was not a normal shock and it gave a lot of, unprecedented, kind of experiences to investors. And emerging markets were not that badly affected. So it started with the US uk so G seven countries.

23:33 - We can conclude from this study that G seven countries were having a very significant impact. The emerging markets were on the wait and watch. So I think that gave them, and then they followed the majors of the developed world, so mostly the G seven countries. So because of that, they were able to disentangle, which we call it as the contagious or the contagion effect that we should have seen.

23:59 - We did not see that. So this was the case and then across, asset class, because at that time, the markets were down. So it was obvious that they, these assets will also have the, the strain and they will be also experiencing a bad phase. But the most important, the explanation was that the market that we saw, that the, the markets which were not doing good, it, it, these markets were located in developed, markets.

24:30 - And this study also opened the, the, this study also opened the eye for the, some of the other studies that were conducted. So in 2021, we found that the ma study appeared in examining the MAs event, in finance, which just letters. And then it, it analyzed the, the case for the forms at the form level. The study was conducted and we found that, what we did in our study,

25:03 - and black, so on, they mentioned specifically Black Monday, black Thursday, and again Black Monday considering Mars nine, Mars 12, Mars 16, and Mars 22. So that gave me a lot of satisfaction that my work finally gave the idea to somebody else to, and that paper is one of the most cited papers also. And, coming to the animal spirits, we found that since these events were quite significant, but not in emerging markets. So for the US

25:35 - and UK investors, these events were quite significant and they might have gone for a either narrow framing or some kind of animal spreads, but animal spreads were seen clearly in the case of emerging markets because they were not that much bothered because most of the significance statistics based on the data we found, and this gave, me some kind of satisfaction as a researcher that I was able to at least conclude what I started having the hypothesis that

26:07 - how these events have impacted the stock market. And this is, what I would like to say. And this study will further extended and I wrote further on the US sectors. I also examined the Asian markets considering the same type of mythology. And I found that most of the results were quite significant and I was very satisfied.

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