Financial instruments are subjected to market volatility and volatility helps in evaluating the risk,

return and uncertainty associated with the investment. The investors use volatility as an indicator

of market risk, which signifies potential for loss or gain. In this paper we have made an attempt

regulation changes, increased adoption. For the study purposive sampling has been used to gather

data from nift-50 index and 4 most liquid cryptocurrencies. Microsoft excel has been used for

initial data cleaning and visualizations and correlation metrics are created using python. The study

has revealed that cryptocurrency is more volatile with high return as compared to the low to

moderate volatile Nifty 50 Index.

Cryptocurrency, NIFTY-50, Volatility, Risk & Returns

In the last decade, cryptocurrencies-a new age asset class have emerged and that has had a

profound impact on the financial markets. Among all the digital assets, Bitcoin (BTC), Ethereum

(ETH), Solana (SOL) and XRP that are powered by blockchain technology, have reached a global

For the Indian context, the NIFTY 50 index, containing the 50 most liquid stocks over the NSE,

confidence, which shows relatively stable and predictable behavior with the oversight of

Securities and Exchange Board of India (SEBI). BTC; the first cryptocurrency creates a store of

value. ETH and SOL, the smart contract platforms and XRP a payment network are selected based

on the representation as key segments of digital asset ecosystem. This research on the matter acts

as a timely resource considering increasing crypto investment interest in India especially among

the younger age groups, which, despite the ambiguity, is still rising, and provides insights on how

crypto assets could improve or deteriorate the performance of traditional equity assets. Finally,

this research hopes to bring analogous understanding of digital and emerging equity markets

regulated markets such as the NIFTY 50. The authors suggest that this phenomenon is caused by

continuous trading, the lower liquidity, and investor speculation. Gopalan, R. & Singh, V. (2020)

used econometric models that can capture spillover effects to analyze how shocks to

cryptocurrency markets transmit on the traditional market. Their findings suggested both markets

are influenced by global events, but cryptocurrencies react quicker, and it is mainly attributable to

the decentralized nature of cryptocurrencies and their higher sensitivity to regulatory news. Desai,

examines the risk-return profiles of cryptocurrencies versus

traditional assets using the Sharpe ratio and Value at Risk (VaR). They conclude that despite the

high returns, cryptocurrencies are risky and less attractive when risk adjusted returns are used for

space cause cryptocurrency volatility. The authors then use event study methodology to illustrate

that taxes and KYC norms announcements in the crypto market result in strong subsequent price

corrections, in contrast to slowly reversible regulatory interventions in traditional markets.

data to examine the correlation of investor emotions and behavior to herd in generating various

crypto price movements. Sentiment driven trading is found to play a major role in the high

volatility seen in cryptocurrencies, as against such a contribution from trading on fundamentals in

case of NIFTY 50. Verma, S. & Gupta, N. (2020) investigate how digital assets, despite their very

high volatility, offer diversification benefits. They used regression analysis and portfolio

optimization to demonstrate how crypto assets can even reduce the overall portfolio risk if 5–10%

amplifying of the cryptocurrency volatility through cognitive biases like overconfidence and herd

mentality. The authors demonstrate, via both survey data and empirical analysis, that such

behavioral factors result in rapid price increases, then severe corrections, which is a pattern that is

less pronounced in a more traditional equity market. Zhao, T. & Li, J. (2022),

trading data, look into the degree that algorithm trading contributes to the quick price fluctuations

in cryptocurrencies. The analysis suggests that automated trading provokes excessive volatility

during stressed markets whether it be in crypto or other markets that have low liquidity.

Kapoor, R. & Deshmukh, A. (2021) use the vector autoregression (VAR) models for determining

the extent of volatility spillover from cryptocurrencies to the NIFTY 50, in terms percentage

investigate how liquidity restrictions worsen the volatility in Bitcoin and Ethereum by stress-

effect of low liquidity on price stability using order book data and compare these with the liquidity

nature of traditional equity markets. Sharma, K. & Mukherjee, S. (2021) take a long-run view of

cryptocurrency volatility relating price stability with maturity of the market as well as changing

regulatory environments. In the mature and improving regulatory clarity of the digital asset

markets, volatility levels temper by means of longitudinal data analysis methods used by the

authors.



To study the theoretical foundation and conceptual framework of crypto currencies.

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find the better match for the study authors have chosen NSE Nifty 50 Index and 4 most liquid and

most traded cryptocurrencies (BTC, ETH, SOL, XRP). Five years data (2020-2024) related to daily

closing prices, trading volumes, and returns are sourced from Yahoo Finance, NSE India,

cryptocurrency exchanges and aggregators like Investing.com using purposive sampling methods.

Microsoft excel has been used for initial data cleaning and statistical calculations. Python

programming has been used for data visualization, comparison of volatility patterns, market

dynamics and running advanced calculations.

Figure 1.1 Price Performance Comparison (2020–2024)

and traditional assets. During speculative trading, the volatility of SOL (113.01%) and XRP

(93.31%) is very high while the level of liquidity is low. Smaller cryptos are volatile compared to

ETH (67.34%) and BTC (51.98%). The NIFTY 50 (12.78%) and the broader Traditional Market

Index are less volatile due to regulatory oversight, the existence of institutional participation, and

stable fundamentals.

Figure 1.3: 30-Day Rolling Volatility Trends

30 days rolling volatility trends track volatility as it moves around during short periods. It is a key

insight where cryptos face frequent volatility spikes.

24/7 trading, decentralized governance models, and retail- dominated liquidity, causing periodic

contrast versus NIFTY 50’s 9.38% volatility. Even though they showcase remarkably high returns