On the Linkages between India VIX and US Financial Stress Index

Abstract

The present study is the first of its kind accounting for linkages among India VIX and US financial stress index by employing vector autoregression model (VAR), Granger causality test, generalized impulse response functions, variance decomposition analysis (VDA) and Diebold and Yilmaz's (2009) spillover index highlighting the impact of cross market variations on each other. The span of monthly data ranges from 2009 to 2015, particularly after the global financial crisis. The results report a unidirectional causality running from the US financial stress to the Indian equity market implied volatility. When a shock is subject to the US financial stress, then the response of implied volatility in the Indian equity market is positive initially, approaching zero after a few months. On an average, 32% of the variations are accounted by cross market shocks whereas rest of the variations are as a result of own market shocks. The contribution of the US financial stress to forecasted error variances in the Indian equity market implied volatility increases over a period of 10 months to 25% approximately. The results have strong implications for the Indian equity market investors.

A revisit to how linkages fuel dependent economic policy initiatives

“…Furthermore, e i is m x1, with ith element equal to one and all other elements equal to zero. The GIRF for a one standard deviation shock to the ith equation in the VAR model on the jth variable at horizon N is defined as (Ewing and Wunnava, 2004; Singh, 2016): …”

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

“…Finally, since financial markets are interconnected and stock market price volatility can also be impacted by the stress of the whole financial market (Das et al, 2019 ; Gupta et al, 2014 ; Singh, 2016 ; Sum, 2014 ). As the result of global economic integration, the stock markets of individual countries are subject to the stress of both the financial market of that country and the global financial market (Das et al, 2019 ; Singh, 2016 ). So, we further consider 2 financial stress indicators: (14) the financial stress index provided by the Office of Financial Research (OFRFSI); (15) the financial stress index provided by St. Louis Fed.…”

“…In addition, studies have found evidence that the stock market can be driven by investors’ psychology (Daniel et al, 2002 ; Tseng, 2006 ) and determined the predictive power of some market sentiment indicators (see, e.g., Gupta et al, 2014 ; Perez-Liston et al, 2014 ; Oliveira et al, 2017 ; Jin et al, 2020 ; Liang et al, 2020a ; Wang et al, 2020a ). Other indicators, such as financial stress indices, have also been proven to have potential forecasting ability (Gupta et al, 2014 ; Singh, 2016 ; Sum, 2014 ). The predictive abilities of these potential predictors tend to change with changes in various external factors, making it more difficult to find stable predictors.…”

“…It affects investors’ subjective judgment of their future returns, which in turn affects their investment behavior and ultimately causes stock market price volatility (Bekaert & Hoerova, 2014 ; Liang et al, 2020a ; Oliveira et al, 2017 ; Perez-Liston et al, 2014 ; Shapiro et al, 2020 ). Financial markets are necessarily interconnected, so stock market price volatility is also subject to the stress of the whole financial market (Das et al, 2019 ; Gupta et al, 2014 ; Singh, 2016 ; Sum, 2014 ), this is also why we examined the predictive power of FSIs on stock market volatility.…”