Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis

Murty, Sarika; Victor, Vijay; Fekete-Farkas, Mária

doi:10.3390/jrfm15070317

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…Since its creation, BTC has experienced numerous price fluctuations but continues to exhibit a long-term bullish trend (Murty et al 2022). The price of Bitcoin surpassed $1 in February 2011.…”

Section: Literature Reviewmentioning

confidence: 99%

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Bank Crisis Boosts Bitcoin Price

Petti,

Sergio

2024

JRFM

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Bitcoin (BTC) represents an emerging asset class, offering investors an alternative avenue for diversification across various units of exchange. The recent global banking crisis of 9 March 2023 has provided an opportunity to reflect on how Bitcoin’s perception as a speculative asset may be evolving. This paper analyzes the volatility behavior of BTC in comparison to gold and the traditional financial market using GARCH models. Additionally, we have developed and incorporated a bank index within our volatility analysis framework, aiming to isolate the impact of financial crises while minimizing idiosyncratic risk. The aim of this work is to understand Bitcoin’s perception among investors and, more importantly, to determine whether BTC can be considered a new asset class. Our findings show that in terms of volatility and price, BTC and gold have responded in very similar ways. Counterintuitively, the financial market seems not to have experienced high volatility and significant price swings in response to the March 9th crisis. This suggests a consumer tendency to seek refuge in both Bitcoin and gold.

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Section: Literature Reviewmentioning

confidence: 99%

“…The price of Bitcoin surpassed $1 in February 2011. However, in June 2011, Bitcoin experienced its first bubble, rising to $31 before sinking below $10 again (Murty et al 2022). Subsequently, in April 2013, Bitcoin reached $200, and by the end of November that year, it was above $1000.…”

Section: Literature Reviewmentioning

confidence: 99%

Bank Crisis Boosts Bitcoin Price

Petti,

Sergio

2024

JRFM

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“…Information like this is crucial as it will help investors make the best decisions when trading stocks in order to maintain profitability in the long term. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, which is part of the Box-Jenkins family, has been widely employed to develop an accurate forecasting model in financial field, particularly in volatility forecasting [20][21][22][23]. The variance of the current error term is a function of the variances of prior error terms, as predicted by the GARCH model.…”

Section: Introductionmentioning

confidence: 99%

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

Sadon,

Ismail,

Khamis

et al. 2024

PLoS ONE

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Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

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“…The period from 2013 to 2022 has witnessed significant disruptions in the global economy due to various events such as the COVID-19 pandemic and the Russia-Ukraine war. These disruptions have had widespread effects on various asset classes, including cryptocurrencies (Murty et al, 2022; Varma et al, 2021). Therefore, it is crucial to examine the influence of the pandemic on cryptocurrency volatility dynamics.…”

Section: Introductionmentioning

confidence: 99%

Volatility Spillover and Directionality in Cryptocurrency and Metal Markets

Dutta,

Kayal,

Balasubramnaian

2023

Journal of Emerging Market Finance

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This article investigates the dynamic relationship between cryptocurrencies and metals, examining the existence and direction of volatility spillovers. While previous studies have explored the relationships between different cryptocurrencies and between base metals and gold, there is a notable gap in understanding the volatility spillover nexus among cryptocurrencies. This study makes a significant contribution by employing the Time-Varying-Parameter-Vector-Autoregressive (TVP-VAR) total connectedness measure to assess the strength of association between these assets. Our analysis employs 10-year daily returns data for three cryptocurrencies (Bitcoin, Litecoin, and Ethereum) and two metals (Gold and Copper). As we witness major economic events worldwide, this study is particularly relevant, as it provides insights into potential hedging opportunities. To comprehend the risk contagion patterns, various measures of partial and dynamic connectedness are computed, supporting the earlier TVP-VAR analysis. The findings indicate that Litecoin and Ethereum exhibit a high level of connectedness, while Bitcoin remains relatively less connected. Among the metals, Gold and Copper demonstrate similar levels of connectedness in certain cases. Notably, there is a significant risk contagion between Litecoin and metals. These results hold essential implications for policy-makers and portfolio managers with different time horizons, offering valuable insights into risk contagion within the cryptocurrency and metal markets. JEL Codes: C32; G15; G17; G41

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Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis

Cited by 11 publications

References 38 publications

Bank Crisis Boosts Bitcoin Price

Bank Crisis Boosts Bitcoin Price

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

Volatility Spillover and Directionality in Cryptocurrency and Metal Markets

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