IMPACT OF COVID 19 ON NIFTY 50 VOLATILITY

COVID 19 has severely affected global financial markets and it has long lasting impact on Nifty 50. The increasing daily new and total COVID 19 cases lead to anxiety among investors and there was panic selling because of lockdown and after government incentives and plan to revive the market, Nifty 50 touched it’s all time high. We can clearly imagine the amount of volatility COVID 19 created in the market. VIX touched all time high of 66.04 from 13.68 points. This research paper aims to study the magnitude of impact of COVID 19 on Nifty 50 volatility using GARCH model.


…………………………………………………………………………………………………….... Introduction:-
The COVID-19 pandemic has created economic crises. The spread of the virus and globally enforced lockdowns have negatively impacted the overall demand and supply of products and services. Supply chain of whole world was severely impacted. Covid-19 pandemic has impacted prices of stocks, gold, commodities or any other product This has severely impacted the global financial markets. It has sent markets around the world crashing to levels not witnessed since the Global Financial Crisis of 2008.
The government took precautionary measures to curb the virus spread. The lockdown restriction implemented by government of India halted most of the economic activities. Due to lockdown GDP figures for Q1 (April to June) FY21 showed a contraction of 24% as compared to the same period the year before. In order to protect margins and remain afloat companies had to take necessary decisions which had short term and will have long term impact on their revenue as well as profits.
Volatile markets are usually characterized by wide price fluctuations and heavy trading in the asset. Due to wide spread of the virus financial markets all around the world saw a downturn as investor confidence was shattered. The repercussions of the coronavirus are expected to be felt for many more months, possibly years. COVID-19 relief package or stimulus package provided by government and global roll out of vaccine helped in recovery of financial markets. There is a 'V shaped' recovery in financial markets.

Research Methodology:-
Autoregressive conditional heteroscedasticity (ARCH) model introduced by (Engle, 1982), is a statistical model that is used to describe variance of the current error time as a function of the actual sizes of the previous time periods error term. This model can also be used to find volatility in financial market. ARCH model assumes that positive and negative shocks have the same impact on volatility, but financial markets respond in different way to positive and negative shocks. ARCH model is restrictive as it becomes complicated for higher order ARCH models.
Generalized Autoregressive Conditionally Heteroscedastic model (GARCH model) was developed by Tim Bollerslev in 1986. GARCH is the first and basic symmetric model and is defined as the linear function of past squared residuals and the lagged conditional variances. Since the development of GARCH models, a number of extensions and variants were developed and can be classified as symmetric and asymmetric GARCH models.
The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model and if an autoregressive moving average (ARMA) model is followed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity model (GARCH). GARCH models also tends to be more parsimonious meaning that it accomplishes a better prediction with less variables, and avoids overfitting. GARCH is useful in modelling volatility in prices because price spikes are often persistent and are caused by elements outside of human control. And therefore, GARCH becomes useful in forecasting the covariance of returns and volatility in financial market.

Data Analysis
For research purpose the data of Nifty 50 was collected from NSE website form 1 st January, 2020 to 31 st December, 2020. Number of daily new cases and total number of cases was studied for 1 st January, 2020 to 31 st December, 2020.
The mean return for the period is 0.07% with median 0.22%. The Kurtosis value of 11.04 indicate return are leptokurtic with skewness of -1.42, the distribution has left tail. In order to study whether series is stationary or not stationary, Dickey Fuller test was conducted. The test concluded that the series is stationary. Therefore, we reject null hypothesis of a unit root in the series at 1%. In, ARCH model p values are less than 1%, which implies presence of ARCH effect in the residuals of the series at 1% level. Which implies variance is not constant, GARCH (1,1) and GJR GARCH models should be used for further analysis and situation of asymmetry. Regression analysis state that there is significant impact of daily new and total COVID 19 cases on Nifty 50 returns. GARCH (1,1) model is used to study volatility in Nifty 50. There is ARCH effect with substantial volatility clustering and indication of persistent of volatility. The variance equation also shows volatility in Nifty 50 returns. It can be conclude that increasing daily new and total COVID 19 cases will result in higher volatility.   We saw a U shape curve on Nifty 50 returns chart as when COVID-19 started and initial lockdown brought country to standstill market was volatile and crashed, but steadily as lockdown measures were eased and stimulus packages were announced market rallied to its all time high. Availability of vaccine also boosted consumer confidence. Since, COVID-19 has global impact it becomes necessary for government to provide support to companies and implement or change rules and regulations to control such huge volatility so that impact of COVID-19 is minimised. Relief plans started to lift financial markets around the world, but investors were cautious and were waiting for the outbreak to be controlled before they once again investing in stock.
Investors were liquidating assets to meet money requirement fearing that the virus will destroy their wealth, traders were opting to sell their shares to minimize loss. This increased volatility in the market.
We can see Bipolarisation of market as few stocks in Nifty 50 recovered to prices of before pandemic very fast and are touching all time high whereas few stocks are still not able to even cross the price before pandemic started.
The study suggests that during pandemic there is high volatility in stock market which can lead to huge price movement in either direction so investor must be aware of what factor is causing volatility in the market and how will it impact their portfolio. Investor must be aware of global trends as today markets are highly integrated and volatility in market can be transmitted.