Assessing the Volatility Spillover of Crypto to Indian Stock Market

 Introduction

The price of Bitcoin has increased by around five times, from 20,000 USD in May 2020 to 1,00,000 USD in May 2025. However, the journey of Bitcoin's value was not smooth. Investing in Bitcoin remains a risky option as it is one of the most controversial financial assets, partly because the policymakers of any country still don't know how to incorporate crypto as part of their mainstream financial system.  With the rising popularity of crypto as an alternative to traditional investment instruments, especially among young investors, it is important to assess the effect of the crypto market's volatility on the Indian financial market. For an emerging market economy like India, assessing volatility spillover is difficult due to complexities in the interdependency of financial markets and the complexity of recent geopolitical scenarios.

The Russia-Ukraine war affected the international financial markets, and this is when the value of Bitcoin started falling. The value of Bitcoin recovered towards the end of 2023, mainly due to the fading effects of war. The increase in the price of Bitcoin during 2024 is a sign of investors' confidence in Bitcoin. During this time, other cryptocurrencies also gained in value, but the increase in the value of Bitcoin is very prominent. The victory of Donald Trump towards the end of 2024 and his association with Elon Musk, a well-known supporter of crypto (1), boosted the investors' confidence. As a result, the value of Bitcoin soared dramatically.

Movement of  Bitcoin price (in Dollars, High and Low) with important events (R code given below)

A simple multivariate GARCH model is used to determine whether the volatility in bitcoin's return affects the volatility of the India Nifty 50 after considering the effect of some of the important Asian Stock markets. The MGARCH models are generally used to understand how the conditional volatility of the dependent variables is affected by the conditional variance matrix of the independent variables. In Table 1, Model 1 gives the MGARCH result of the NIFTY-50 as the dependent variable and Bitcoin and other financial market returns as independent variables. We can see that the volatility of Nifty 50 is significantly and positively affected by the volatility of Bitcoin, at least for the time under consideration (January 2021 to May 2025.

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Result of Multivariate GARCH model

Conclusion :

This article investigated the impact of Bitcoin (BTC/USD) on Indian Stock market namely the NIFTY 50 using time series forecasting with GARCH model to examine volatility and spillover effects. The combined application of the GARCH model provided valuable insights into the volatility behavior and predictive potential across these financial instruments, highlighting the growing relevance of interdisciplinary approaches in financial analysis.
The analysis revealed that the NIFTY 50 exhibited a statistically significant relationship with Bitcoin. This finding suggests that the Indian equity market is more sensitive to cryptocurrency volatility, potentially due to factors like rising crypto adoption, speculative retail participation, and overlapping investor sentiment. India is also an emerging economy with a large number of investors who face no restrictions on Bitcoin investments, allowing for easy entry and exit. 

R Codes:

library(dplyr)

library(ggplot2)

library(lubridate)

install.packages("yahoofinancer")

library(yahoofinancer)

install.packages("gganimate")

library(gganimate)

install.packages("gifski")

library(gifski)

btc <- Ticker$new('BTC-USD')

btc_data <- btc$get_history(start = '2021-01-01', end = '2025-02-05', interval = '1d')

head(btc_data)

btc_data <- btc_data %>%

  mutate(date_only = as.Date(date))

btc_data <- btc_data %>%

  mutate(year = year(date_only))

btc_data$EventDate1 <- ifelse(seq(as.Date("2021-01-01"), as.Date("2025-02-05"), by = "day") %in% as.Date("2022-02-01"), "Russia-Ukraine War", NA)

btc_data$EventDate2 <- ifelse(seq(as.Date("2021-01-01"), as.Date("2025-02-05"), by = "day") %in% as.Date("2023-10-10"), "Recovery From War", NA)

btc_data$EventDate3 <- ifelse(seq(as.Date("2021-01-01"), as.Date("2025-02-05"), by = "day") %in% as.Date("2024-10-10"), "USA Election", NA)

p <- ggplot(btc_data)

p<-p+  geom_line(aes(x= date_only, y=high), color = "red", size = 0.5)

p<-p+  geom_line(aes(x= date_only, y=low), color = "blue", size = 0.5)

p<-p+ xlab("Date") + ylab("Bitcoin") + theme_minimal()+

  transition_reveal(date_only)

p<-p+ geom_vline(data = btc_data %>% filter(!is.na(EventDate1)),

             aes(xintercept = as.Date("2022-02-01")), 

             linetype = "dashed", color = "black", size = 1) +

  geom_text(data = btc_data %>% filter(!is.na(EventDate1)),

            aes(x = as.Date("2022-02-01"), y = max(high, na.rm = TRUE), label = "Russia-Ukraine War"), 

            color = "black", size = 2.7, vjust = -12)

p<-p+ geom_vline(data = btc_data %>% filter(!is.na(EventDate2)),

             aes(xintercept = as.Date("2023-10-10")), 

             linetype = "dashed", color = "black", size = 1) +

  geom_text(data = btc_data %>% filter(!is.na(EventDate2)),

            aes(x = as.Date("2023-10-10"), y = max(high, na.rm = TRUE), label = "Recovery From War"), 

            color = "black", size = 2.7, vjust = -8)

p<-p+ geom_vline(data = btc_data %>% filter(!is.na(EventDate3)),

             aes(xintercept = as.Date("2024-10-10")), 

             linetype = "dashed", color = "black", size = 1) +

  geom_text(data = btc_data %>% filter(!is.na(EventDate3)),

            aes(x = as.Date("2024-10-10"), y = max(high, na.rm = TRUE), label = "USA Election"), 

            color = "black", size = 2.7, vjust = -8)

animate(p, nframes = 230, fps = 8, width = 1600, height = 1000, res = 220, renderer = gifski_renderer("pic1.gif"),loop='FALSE')

References:

1) Elon Musk may have a revolutionary crypto plan. Here's what we know about 'X-Money' - The Economic Times

2)Khalfaoui, R., Hammoudeh, S., & Rehman, M. Z. (2023). Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network. Emerging Markets Review, 54, 101002.

3) Zeng, H., & Ahmed, A. D. (2023). Market integration and volatility spillover across major East Asian stock and Bitcoin markets: an empirical assessment. International Journal of Managerial Finance, 19(4), 772-802.

By
Aryamaan Kain,
M.A. Economics (2023-25), Department of Economics, SBSS, Manav Rachna International Institue of Research and Studies, Faridabad.
To cite this article: 
Aryamaan. (2025, May). Assessing the volatility spillover of crypto to Indian stock market. Eco-Bizz blog, Department of Economics.
https://ecobizzblog.blogspot.com/2025/05/assessing-volatility-spillover-of.html