New Delhi, Jan 2 (Mayank?Nigam) Policymakers have to control the inflation without harming the economy and financial markets, according to Dr Soumya Kanti Ghosh, Group Chief Economic Adviser, State Bank of India. Higher cost of capital and thereby lower operating margins impact the growth as well as the competitive landscape favouring established market players more than the new entrants. Financial markets in 2022 have remained volatile and edgy with the central banks globally in unison in a rate hike cycle. In fact, this is in complete contrast to post global financial crisis in 2008 when all central banks had cut rates in unison, but central banks in respective countries decided to take an exit from easy monetary policy separately, India included.
With the prospect of a global slowdown in 2023, it could be eminently possible that central banks in respective countries start to unwind rates even in unison as inflation comes off the boil and slowdown starts to bite. Meanwhile, we believe that equity and bonds become less correlated when the economic cycle slows. Challenges for investors also increase when both bond prices, as well as equity prices, fall together. Allocation to fixed income in the current year has been a challenging area as the low yield on government bonds lowers its ability to offset losses incurred by investors during bear markets. Equity markets factor in news, positive or negative, to reasonably value the stocks. Investors tend to choose asset allocation in equity markets by comparing with yields derived from short-duration as well as long-duration government securities. Indian markets have remained volatile in 2022. However, the market capitalization of BSE has increased by 137% in December 2022, in comparison with March 2020, the highest among the prominent equity markets. Returns in the Indian Equity market (BSE SENSEX) were 4.4 % on YTD basis with lesser volatility in comparison with other prominent equity markets.
A granular look at the data reveals that both in terms returns and volatility, Indian markets logged in the best performance on a relative scale. To understand the factors that explain market volatility in the Indian context, we did a 2 stage analysis. In the first step, we estimated the volatility of BSE SENSEX returns with the help of a GARCH model and we confirmed the statistical robustness of the results. Firstly, results of the GARCH model suggest that BSE SENSEX returns are not driven by its lag, indicating Indian markets are forward-looking, but are impacted by negative news. Secondly, the market liquidity and movements in VIX index are significantly explaining the movements of the returns. Increase in market liquidity has positive impact on BSE SENSEX returns, while VIX index which gauges the market sentiments especially fear of market participants in the form of 30 day projection of volatility, affects BSE Sensex returns negatively. 1 unit (Rs. Billion) increase in Net liquidity increases the BSE SENSEX returns by 0.00004 unit. 1 unit increase in VIX index decreases the BSE SENSEX returns by 0.01449 unit. Results are significant at 1% level and are also robust. In second step of our analysis, using estimated volatility of BSE SENSEX returns through GARCH model, we measure the impact of federal funds rate, repo rate, and spread of government bond yields on the volatility of BSE SENSEX returns with the help of ARDL model. Federal Funds rate and Repo rate are estimated to be negatively impacting the BSE Sensex volatility, indicating in a rate hike cycle, market volatility declines as risk gets adequately priced. 1 % increase in FFR and repo rate decreases the Volatility of BSE SENSEX returns by 0.244 and 0.346 unit respectively. Results are found to be significant at 1% level.
But most importantly, 1% increase in difference (spread) between yields of 5 year government bond yield with respect to 1 year government bond yield is found to increase volatility of BSE SENSEX returns by 4.26 unit. This clearly indicates that the RBI may look at the mid segment of the yield curve more specifically in terms of signaling. Interestingly, the spread between 1 year and 6, 7 years are found to be statistically insignificant, implying the strong market preference to look at 5 year rates as signaling device. As expected, the yield spread between 1 year and 10 year is found to be statistically significant. 1% increase in difference (spread) between yields of 3 year, 4 year, and 10 year government bond yields with respect to 1 year government bond yield decreases volatility of BSE SENSEX returns by 0.98, 2.73, and 0.87 unit respectively. Results are found to be significant. Robustness of the aforementioned results has been checked with Threshold GARCH model predicted variance as Dependent Variable.