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Economy 
Vol. 12, No. 2, 175-181, 2025 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/economy.v12i2.7726 

© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Safe haven or risky bet? A study of gold prices during Indian stock market volatility 

 
Amalendu Bhunia1  
Amit Das2 

 

 
( Corresponding Author) 

 
1University of Kalyani, West Bengal, India. 
1Email: bhunia.amalendu@gmail.com  
2Surendranath Evening College, Kolkata, India. 
2Email: amitdas8121984@gmail.com  

 
Abstract 

The purpose of this study was to investigate whether gold served as a safe haven or a risky asset 
during periods of heightened volatility in the Indian stock market. Given India’s cultural and 
economic attachment to gold, this study explored the dynamic relationship between gold prices 
and stock market fluctuations, particularly during times of financial uncertainty. Using daily data 
from 2005 to 2023, the research employed GARCH (1,1), EGARCH (1,1), and DCC-GARCH 
models to analyze both the unconditional and time-varying correlations between gold returns and 
Nifty 50 returns. The findings revealed that gold exhibited significant safe-haven characteristics 
during extreme market downturns, offering protection to investors against stock market losses, 
with strong volatility persistence in both markets and significant leverage effects in the stock 
market. The DCC-GARCH model showed that gold exhibited a negative correlation with equities, 
particularly during periods of global financial crisis (2008–09), COVID-19 crash (2020), and 
Russia-Ukraine conflict (2022), confirming its role as a safe haven. The practical implications of 
this study are particularly relevant for investors, portfolio managers, and policymakers. Investors 
can use gold as an effective diversification tool to mitigate stock market risk, while policymakers 
can monitor gold price movements as indicators of investor sentiment and financial stability. The 
study contributed to the understanding of gold’s dual role in the Indian financial system as both a 
safe-haven asset and a speculative instrument, depending on market conditions. 

 
Keywords: Crisis period, GARCH models, gold prices, safe haven, stock market volatility. 

 
Citation | Bhunia, A., & Das, A. (2025). Safe haven or risky bet? A 
study of gold prices during Indian stock market volatility. Economy, 
12(2), 175–181. 10.20448/economy.v12i2.7726. 
History:  
Received: 9 October 2025 
Revised: 27 October 2025 
Accepted: 3 November 2025 
Published: 18 November 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Institutional Review Board Statement: Not applicable. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. This study followed all ethical practices during writing. 
Competing Interests: The authors declare that they have no competing 
interests. 
Authors’ Contributions: Both authors contributed equally to the conception 
and design of the study. Both authors have read and agreed to the published 
version of the manuscript. 

 
Contents 
1. Introduction .................................................................................................................................................................................... 176 
2. Literature Review .......................................................................................................................................................................... 177 
3. Data and Methodology ................................................................................................................................................................. 177 
4. Empirical Results and Analysis ................................................................................................................................................... 178 
5. Conclusion ....................................................................................................................................................................................... 180 
References ............................................................................................................................................................................................ 180 
 

 

 

 

 

 

mailto:bhunia.amalendu@gmail.com
mailto:amitdas8121984@gmail.com
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v12i2.7726
https://orcid.org/0009-0005-3142-1822


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Contribution of this paper to the literature 
This study documents that its originality lies in the application of advanced volatility models (GARCH, 
EGARCH, and DCC-GARCH) to Indian market data. It focuses on the time-varying safe-haven behavior 
of gold during different phases of market stress, thereby providing a nuanced, crisis-specific perspective 
that was absent in prior Indian empirical research. 

 

1. Introduction 
In the complex design of global financial markets, investors constantly seek assets that can preserve value 

during turbulent times. Among the wide array of financial instruments, gold has historically maintained a 
reputation as a “safe haven” asset. This perception is largely rooted in its historical performance, intrinsic value, 
limited supply, and psychological appeal as a store of wealth during times of uncertainty and economic distress 
(Baur & Lucey, 2010). As global markets become increasingly integrated and volatile, the need to understand the 
role of gold in relation to stock markets becomes more relevant than ever, especially in emerging economies like 
India. India is among the world's largest consumers of gold, both in physical and financial forms. Culturally 
embedded in the traditions, rituals, and personal finance of Indian households, gold has been more than just a 
commodity; it serves as a form of informal savings and wealth preservation tool across generations. This 
longstanding relationship with gold sets India apart from many other countries in terms of investor behavior and 
asset preferences (World Gold Council, 2022). As a result, any shifts in global or domestic financial markets may 
significantly influence gold investment behavior in India. 

The Indian stock market, comprising major indices such as the BSE Sensex and NSE Nifty 50, is known for its 
rapid growth, diversity, and exposure to both domestic and international shocks. Events such as the Global 
Financial Crisis of 2008, the COVID-19 pandemic, the Russia-Ukraine conflict, and frequent changes in global 
interest rates have all triggered sharp fluctuations in the Indian equity market. During such turbulent times, risk-
averse investors reassess their portfolios, reallocating capital to perceived safer assets like gold. For example, 
during the COVID-19 pandemic in 2020, while Indian stock indices plunged in March due to panic and 
uncertainty, gold prices surged sharply, reinforcing its reputation as a safe haven. However, the behavior of gold is 
not always consistent. There have been periods when gold has shown high volatility itself, reducing its efficacy as a 
safety asset. This has led to a growing academic and practical interest in evaluating whether gold is truly a safe 
haven or merely a risky bet during times of financial instability in India.  

The concept of a “safe haven” asset is distinct from that of a “hedge.” As Baur and Lucey (2010) clarify, while a 
hedge is negatively correlated with another asset on average, a safe haven is negatively correlated specifically 
during times of extreme market stress. In other words, a safe haven provides protection during adverse market 
conditions but may not necessarily offer negative correlation during stable periods. Gold, due to its liquidity, lack 
of default risk, and historical performance, is considered one of the few assets with potential safe-haven 
characteristics. Various international studies have sought to validate gold’s role as a safe haven. For instance, Baur 
and McDermott (2010) found that gold acts as a safe haven in major economies like the United States, United 
Kingdom, and Germany. However, they also pointed out that gold's safe-haven role varies across time and markets. 
In emerging economies, including India, the relationship between gold and equity returns may be influenced by 
additional factors such as currency fluctuations, inflation expectations, government import duties, and global 
commodity market dynamics (Sadorsky, 2014). 

India’s relationship with gold is multifaceted. Beyond its financial attributes, gold is deeply intertwined with 
social customs and is considered a symbol of wealth and security. This dual nature of gold, both as a consumption 
good and an investment vehicle, complicates its behavior in financial models. Moreover, India’s gold market is 
heavily influenced by policy measures such as gold import duties, the Goods and Services Tax (GST), and the 
Reserve Bank of India’s Gold Monetization Scheme (Choudhry, Hassan, & Shabi, 2015). All these factors impact 
the investment decisions of retail and institutional investors. Additionally, India's high dependence on imported 
gold exposes domestic prices to global gold price trends and USD-INR exchange rate fluctuations. Hence, any 
attempt to evaluate gold’s safe-haven status in India must consider these external influences, alongside domestic 
market volatility. Empirical studies focusing on India have yielded mixed results. Jain and Ghosh (2013) observed 
that gold acted as a hedge but did not exhibit strong safe-haven properties in the Indian context. Conversely, 
Kumar (2014) found evidence supporting gold’s role as a refuge during periods of intense equity market stress. 
These conflicting results indicate the need for a more nuanced and data-driven exploration using advanced time-
series econometric tools such as GARCH and DCC-GARCH models, which can account for time-varying 
correlations and volatility spillovers. 

The COVID-19 pandemic dramatically reshaped global and Indian financial markets. During 2020, investors 
flocked to gold as uncertainty loomed, and global central banks unleashed record levels of stimulus. However, the 
situation reversed in 2021–2022 as global interest rates began rising, inflation surged, and equity markets regained 
momentum. These rapid changes have reignited debates over the reliability of gold as a safe haven. In India, retail 
participation in the stock market has grown significantly over the past five years, facilitated by digital trading 
platforms, low brokerage costs, and financial literacy campaigns. Simultaneously, gold investment has evolved 
beyond traditional jewelry purchases, with increasing adoption of Gold Exchange-Traded Funds (ETFs), 
Sovereign Gold Bonds (SGBs), and digital gold products. These shifts make it timely and relevant to reassess 
gold’s role during equity market volatility from both a behavioral and empirical perspective. 

This study is grounded in modern portfolio theory (MPT), which emphasizes diversification to minimize risk. 
According to MPT, assets that are uncorrelated or negatively correlated, during times of crisis, can help investors 
achieve optimal risk-return tradeoffs. Gold is recommended for inclusion in diversified portfolios due to its 
perceived counter-cyclical nature. Additionally, behavioral finance theory is also relevant, as investor psychology, 
fear, and herd behavior drive movements toward safe-haven assets in volatile times (Tversky & Kahneman, 1974). 
By testing these assumptions in the Indian context, this study aims to add empirical weight to theoretical 
propositions and provide practical insights for investors, policymakers, and financial advisors. 
 



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2. Literature Review  
The role of gold as a safe-haven asset has garnered widespread academic interest, especially during periods of 

financial turmoil. A substantial body of literature has sought to assess whether gold provides a cushion against 
equity market volatility, acts as a hedge in normal times, or simply performs as another risky asset in a dynamic 
market environment. This review synthesizes the global and India-specific empirical evidence on the subject, while 
identifying methodological gaps that justify further exploration in the Indian context. 

The foundational work of Baur and Lucey (2010) distinguished between a hedge, which is negatively correlated 
with another asset on average, and a safe haven, which is uncorrelated or negatively correlated specifically during 
periods of market stress. Their empirical analysis across major developed markets found that gold acted as both a 
hedge and a safe haven, depending on market conditions. Extending this framework, Baur and McDermott (2010) 
confirmed the safe-haven behavior of gold during global stock market crises but also highlighted significant 
regional variations. The findings from these seminal studies laid the groundwork for subsequent research using 
both static and time-varying econometric models to test gold’s safe-haven role across markets and time horizons. 

Several international studies examined gold's behavior across different asset classes and economic scenarios. 
Capie, Mills, and Wood (2005) found that gold acts as a hedge against exchange rate fluctuations, especially 
against the U.S. dollar. Ciner, Gurdgiev, and Lucey (2013), analyzing short- and long-run relationships, revealed 
that gold's hedge and safe-haven properties were unstable and time-dependent. Sadorsky (2014) emphasized the 
importance of modeling dynamic correlations and demonstrated that gold’s behavior relative to stock markets can 
be significantly influenced by commodity price shocks and macroeconomic events. Al-Yahyaee, Rehman, Mensi, 
and Al-Jarrah (2019) used multifractal analysis to show that gold exhibited non-linear safe-haven characteristics 
during turbulent financial periods in both emerging and developed markets. While the consensus among global 
studies supports gold’s potential as a safe haven, it also warns against assuming a universal, static relationship 
across markets and time periods. 

India’s unique socio-economic affinity for gold provided fertile ground for evaluating its performance during 
stock market volatility. However, the number of rigorous studies focusing solely on India remains limited 
compared to developed markets. Jain and Ghosh (2013) examined the dynamic interrelationship between gold 
prices, stock market indices, and exchange rates in India using co-integration and Granger causality tests. They 
found that while gold served as a hedge over the long term, it did not exhibit robust safe-haven characteristics 
during short-term equity market turbulence. In contrast, Kumar (2014) analyzed the return and volatility 
transmission between gold and Indian equities using GARCH models and reported evidence that gold served as a 
volatility buffer during major market downturns, thus supporting the safe-haven hypothesis. Sharma and 
Mahendru (2010) focused on the co-movement of gold and equity markets during financial crises and found 
significant negative correlation during periods of uncertainty. However, they cautioned that gold’s performance 
was also influenced by inflation, interest rates, and currency values. 

A variety of econometric techniques were applied to examine the gold-equity relationship. While early studies 
relied on basic regression, co-integration, and causality analysis, more recent works adopt time-varying volatility 
models such as GARCH, EGARCH, and DCC-GARCH frameworks to better capture dynamic interactions. Bouri, 
Jain, Roubaud, and Kristoufek (2017) used a DCC-GARCH model to explore volatility spillover between gold and 
oil markets and showed that the co-movement increased significantly during periods of global economic stress. 
Applying such models to the Indian gold-equity relationship would enable more precise analysis of time-varying 
correlations during periods of market volatility. Patra and Patnaik (2020) employed a combination of ARCH-type 
models and rolling correlation techniques to demonstrate that gold’s role in Indian portfolios strengthened during 
periods of sharp equity corrections. However, their study called for deeper exploration of causality and cross-asset 
volatility effects using multivariate models. Furthermore, most Indian studies to date have focused primarily on 
physical gold or spot prices. With the rise in popularity of Gold ETFs, Sovereign Gold Bonds, and digital gold, 
there is a need to explore whether these instruments mirror the safe-haven behavior of physical gold, particularly 
during market stress. 

While existing literature offers useful insights into gold’s potential role as a hedge or safe haven, especially in 
developed markets, there remains a clear research gap in understanding the time-varying, crisis-specific, and 
behaviorally influenced relationship between gold and Indian equity markets. Most Indian studies either rely on 
static models or overlook key factors such as investor psychology, policy interventions, and international 
influences. 

This study sought to address these gaps by applying advanced time-series econometric models to explore the 
dynamic relationship between gold prices and Indian stock market volatility. It also considered the unique socio-
cultural and macroeconomic dimensions that influence gold investing in India, especially during the COVID-19 
pandemic and recent geopolitical events. 
 

3. Data and Methodology 
This study utilized secondary daily time-series data spanning from January 2005 to December 2023 to explore 

whether gold served as a safe-haven asset during periods of heightened Indian stock market volatility. The dataset 
included daily closing prices of gold (INR per 10 grams) sourced from the World Gold Council and MCX, the 
Nifty 50 and BSE Sensex indices from NSE and BSE websites, and the USD/INR exchange rate from the Reserve 
Bank of India to control for currency effects on gold valuation. 

All price data were transformed into log returns, defined as Rt=ln(Pt)−ln(Pt-1), to ensure stationarity and 
homoscedasticity. To empirically assess gold’s hedging and safe-haven properties, the methodology adopts a multi-
pronged econometric framework. Descriptive statistics and Pearson correlation coefficients were computed to 
examine average relationships between gold and stock returns, both across the full sample and during crisis 
periods, including the Global Financial Crisis (2008–09), the COVID-19 crash (2020), and the Russia-Ukraine 
conflict (2022). 

Volatility modeling was conducted using the GARCH (1,1) and EGARCH (1,1) models (Bollerslev, 1986; 
Nelson, 1991) to estimate time-varying volatility and capture asymmetric shocks. The GARCH (1,1) model, 



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specified as σ2
t=α0+α1ϵ2

t−1+β1σ2
t−1, captured persistence and clustering in volatility, while the EGARCH model, 

expressed as ln(σ2
t)=ω+βln(σ2

t−1)+γϵt−1/σt−1+α(∣ϵt−1/σt−1∣−2/π), accounted for asymmetric effects of positive 
and negative shocks. 

To examine the time-varying relationship between gold and equities, the Dynamic Conditional Correlation-
GARCH (DCC-GARCH) model proposed by Engle (2002) was employed. The model decomposed the conditional 
covariance matrix as Ht=DtRtDt, where Dt represents the time-varying standard deviations of individual assets and 
Rt captures dynamic correlations.  

Furthermore, to test whether gold acted as a safe haven during market crises, a linear regression model with 

interaction terms was estimated: Rgold,t=α+β1Rstock,t+β2Dcrisis+β3(Rstock,t×Dcrisis)+ϵt, where a significantly negative 

β3 implied that gold moves inversely with stocks during turbulent periods, indicating safe haven behavior (Baur & 
Lucey, 2010). Crisis periods were represented using dummy variables (Dcrisis = 1 during periods of sharp equity 
declines). Robustness checks include rolling-window correlations (30-day intervals) and sub-sample analyses, while 
alternative gold investment instruments such as Gold ETFs and Sovereign Gold Bonds are evaluated for 
consistency.  

This methodological framework allowed for a nuanced perception of gold’s dynamic role in Indian portfolios, 
whether as a hedge during normal periods or as a true safe haven during financial turmoil, filling a key gap in the 
literature on emerging markets’ asset behavior during systemic shocks (Baur & McDermott, 2010; Sadorsky, 2014). 
 
Table 1. Descriptive Statistics. 

 Index Mean return S.D. Skewness Kurtosis Jarque-Bera (p-value) 

Full sample Gold 0.03 1.14 0.17 3.82 0.00 

Nifty 50 0.04 1.71 -0.63 5.42 0.00 

Global financial crisis 
(2008–09) 

Gold 0.09 1.26 0.31 3.67 0.00 
Nifty 50 -0.13 3.45 -1.02 6.11 0.00 

COVID-19 crash (2020) 
 

Gold 0.14 1.31 0.51 4.10 0.00 
Nifty 50 -0.09 2.95 -0.87 5.73 0.00 

Russia-Ukraine conflict 
(2022) 

Gold 0.05 1.22 0.20 3.85 0.00 
Nifty 50 -0.04 2.21 -0.59 4.92 0.00 

 

4. Empirical Results and Analysis 
4.1. Descriptive Statistics 

Table 1 demonstrated that mean returns of gold showed positive mean returns across all periods, especially 
during crises, suggesting it performed well as a hedge or safe haven. Nifty 50, on the other hand, experienced 
negative returns during all crisis periods, most severely during the global financial crisis and the COVID-19 crash. 
The standard deviation (volatility) of Nifty 50 displayed higher volatility than gold across all periods, particularly 
during the global financial crisis and COVID-19 crash, confirming stock market instability during crises. Gold 
volatility increased during crises, but to a lesser extent, indicating relative stability. Skewness & kurtosis of Nifty 
50 showed negative skewness (left-tailed distribution), suggesting more frequent extreme losses. Gold showed 
positive skewness, meaning gains were more frequent during uncertainty. Both assets showed leptokurtic 
distributions, indicating fat tails and a higher probability of extreme events than a normal distribution. The p-
values of the Jarque-Bera test were 0.000 for all series, rejecting normality, supporting the use of GARCH-type 
models to account for non-normality and volatility clustering. 
 

4.2. Correlation Analysis 
Table 2 demonstrated that gold and Nifty 50 showed a weak negative correlation for the full sample, indicating 

that in general, gold had a mild inverse movement with the stock market. It was a diversification tool, but not 
always a strong safe haven. Correlation strengthened in the negative direction during the global financial crisis, 
which indicated clear safe-haven behavior. Also, correlation strengthened in the negative direction during the 
COVID-19 crash, which indicated gold appreciated as stocks fell or gold again served as a hedge. Moreover, 
correlation was reinforced in the negative direction during the Russia-Ukraine conflict, which illustrated a slightly 
stronger hedge effect during geopolitical tension. This pattern aligned with Baur and Lucey (2010), who define a 
safe haven as an asset that is negatively correlated or uncorrelated with stocks during times of market stress. 
 
Table 2. Correlation analysis. 

 Index Coefficient 

Full sample Gold 
-0.17 

Nifty 50 

Global financial crisis (2008–09) 
 

Gold 
-0.31 

Nifty 50 

COVID-19 crash (2020) 
 

Gold 
-0.26 

Nifty 50 

Russia-Ukraine conflict (2022) 
 

Gold 
-0.20 

Nifty 50 

 
These results support the hypothesis that gold can serve as a risk-mitigating asset, especially when the Indian 

stock market experiences stress. 
 
 
 
 



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Table 3. GARCH (1,1) Model Test Results. 

 ω α β α + β 

Full sample 0.000002** 0.09** 0.89** 0.98 
0.000001** 0.12** 0.87** 0.99 

Global financial crisis 0.000003* 0.10** 0.87** 0.97 

0.000002** 0.17** 0.80** 0.97 

COVID-19 crash 0.000002* 0.09** 0.90** 0.99 

0.000003** 0.18** 0.78** 0.96 

Russia-Ukraine conflict 0.000001* 0.08** 0.91** 0.99 
0.000002** 0.15** 0.81** 0.96 

Note: **significant at the 1% level. *significant at the 5% level. 

 

4.3. GARCH (1,1) Model Test Results  
Table 3 demonstrated that both assets are about 1, suggesting high volatility persistence and strong clustering 

with past shocks impacting current volatility during the full sample periods. The Indian stock market is more 
sensitive to past volatility shocks than gold. Gold’s lower ARCH coefficient indicates lower short-term response to 
shocks, making it a relatively stable asset. During the global financial crisis, gold’s volatility persistence declined 
slightly compared to the full sample but remains high. Nifty 50 showed a higher ARCH effect, indicating strong 
immediate response to market shocks during the crisis. Gold maintained stability with lower ARCH, reaffirming its 
role as a safe haven. During the COVID-19 crash, gold showed higher volatility persistence than the Nifty 50 in 
this period. Gold’s lower ARCH effect again confirmed a less short-term shock reaction, reinforcing its safe-haven 
status. Nifty 50’s behavior was more reactive, but its volatility dissipated quicker than during the global financial 
crisis. During the Russia-Ukraine conflict, Nifty 50 displayed higher ARCH (shock sensitivity) and slightly lower 

persistence. Gold remained highly persistent in volatility, but its low α means that gold absorbed shocks gradually, 
making it less erratic during geopolitical crises. The market reacted strongly to geopolitical shocks. 
 
4.4. EGARCH (1,1) Model Test Results   

EGARCH helps detect leverage effects, i.e., whether negative impacts have a stronger effect on volatility. 
 
Table 4. EGARCH (1,1) model test results. 

Parameter ω α β γ 

Full sample -0.12** 0.07** 0.93** 0.01 (p=0.37) 
-0.10** 0.09** 0.92** -0.16** 

Global financial crisis -0.09* 0.06** 0.94** 0.01 (p=0.29) 
-0.11** 0.09** 0.92** -0.18** 

COVID-19 crash -0.14** 0.07** 0.95** 0.00 (p=0.6) 
-0.12** 0.09** 0.90** -0.17** 

Russia-Ukraine conflict -0.11** 0.06** 0.96** 0.01** 
-0.10** 0.09** 0.89** -0.15** 

Note: **significant at the 1% level. *significant at the 5% level. 

 

Table 4 demonstrated that gold's γ was statistically insignificant, meaning no significant leverage effect during 
the full sample periods. Negative and positive shocks affected volatility similarly. Nifty 50 showed a strong 
negative and confirmed leverage effect, which indicated that bad news led to greater volatility in equity markets. 

Gold's high β showed persistent volatility, but relatively symmetric behavior over time. During the global financial 

crisis, gold remained relatively symmetric in response to news (γ not significant), Nifty 50 experienced intense 
asymmetry, showing investors panicked more with bad news, and gold continued to display safe haven properties 
even in a systemic shock scenario. During the COVID-19 crash, gold remained symmetric, indicating shock 
absorption capacity even during a health and economic crisis. Nifty 50 again showed highly negative and 

significant γ, confirming risk amplification from negative returns, and gold maintained its role as a volatility 

stabilizer. During the Russia-Ukraine conflict, gold showed no leverage effect (γ insignificant), acting as a risk-
mitigating asset. Nifty 50 showed significant leverage, with investors reacting strongly to uncertainty, reinforcing 
the conclusion that gold behaves as a hedge against political instability. 
 

4.5. DCC-GARCH Model Test Results  
The DCC-GARCH model, proposed by Engle (2002), is widely used to model time-varying correlations 

between financial assets. It is particularly useful in understanding whether gold diversifies risk during market 
stress and how the correlation between gold and stock market returns changes during turbulent periods. 

 
 
 
 
 
 
 
 
 
 
 
 
 



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Table 5. DCC-GARCH model test results. 

Parameter  Estimate Std. Error z-Statistic Prob. 

Full sample α (DCC1) 0.02 0.01 5.49 0.00 

β (DCC2) 0.95 0.01 142.69 0.00 

Mean correlation -0.07 — — — 
Min correlation -0.21 — — — 
Max correlation 0.14 — — — 

Global financial 
crisis 

α (DCC1) 0.04 0.01 7.17 0.00 

β (DCC2) 0.92 0.01 127.83 0.00 

Mean correlation -0.12 — — — 
Min correlation -0.21 — — — 
Max correlation 0.08 — — — 

COVID-19 crash α (DCC1) 0.04 0.01 7.48 0.00 

β (DCC2) 0.90 0.01 106.17 0.00 

Mean correlation -0.09 — — — 
Min correlation -0.22 — — — 
Max correlation 0.11 — — — 

Russia-Ukraine 
conflict 

α (DCC1) 0.03 0.01 5.33 0.00 

β (DCC2) 0.93 0.01 122.62 0.00 

Mean correlation -0.08 — — — 
Min correlation -0.19 — — — 

Max correlation 0.12 — — — 

 

Table 5 revealed that the high β value showed persistent correlation behavior during the full sample periods, 

low α indicated slow reaction to shocks. Generally, the correlation between gold and Nifty 50 was slightly 
negative, suggesting gold may serve as a hedge. The fluctuation between -0.21 and +0.13 implied that this hedge 
effect was time-varying. During the global financial crisis, stronger negative average correlation was observed, and 

gold acted as a safe haven. With high β and slightly higher α compared to the full sample, gold exhibited dynamic 
but persistent behavior. The DCC fell more during crisis shocks, indicating gold's decoupling from equities when 
panic rises. During the COVID-19 crash, gold and Nifty again showed a moderately negative average correlation, 

with the correlation dipping as low as -0.221, demonstrating gold's effectiveness as a crisis hedge. The increased α 
reflected higher reactivity to shocks during a public health and financial panic. During the Russia-Ukraine conflict, 
negative average correlation persisted, reaffirming gold’s decoupling tendency during geopolitical risk. The market 
remained sensitive, though correlation persistence stayed high, and gold maintained its role as a risk diversifier. 
 

5. Conclusion 
This study investigated the dynamic relationship between gold prices and Indian stock market volatility, 

focusing on whether gold acts as a safe haven, a hedge, or a risky asset, especially during periods of market distress 
using GARCH(1,1), EGARCH(1,1), and DCC-GARCH, and data spanning normal periods and crises including the 
Global Financial Crisis (2008–09), COVID-19 crash (2020), and Russia-Ukraine conflict (2022). Several insightful 
conclusions emerge. 

The GARCH (1,1) results for both gold and Nifty 50 confirmed strong volatility clustering, indicated by the 
high persistence values, reflecting that shocks to returns had long-lasting effects. The EGARCH results revealed 

significant leverage effects in Nifty 50 returns (γ negative and significant), suggesting that negative news impacted 

volatility more than positive news. For gold, however, the leverage term (γ) was statistically insignificant, 
reinforcing gold's role as a stabilizing asset in turbulent markets. The DCC-GARCH model provided the most 
nuanced understanding. Over the full sample period, gold and Nifty 50 showed a slightly negative but time-varying 
correlation, indicating that gold can act as a weak hedge against Indian equities. However, during crisis periods, 
the correlation became more strongly negative, especially during the Global Financial Crisis, confirming gold’s 
role as a safe haven asset when financial markets experienced extreme distress. 
 

5.1. Policy Implications 
The negative and dynamic correlation between gold and Nifty 50 during crises implied that investors and 

portfolio managers should consider gold as a strategic component of investment portfolios, particularly during 
volatile market conditions. Allocating a portion to gold can reduce portfolio risk and enhance stability in uncertain 
times. Retail investors in India turn to gold as a traditional investment. These findings supported that such 
behavior was empirically justified, especially during financial turmoil. Financial educators and advisory firms 
should emphasize gold’s hedging and safe haven properties in risk-focused investment education. 

Gold reserves continue to play a vital role for the RBI. These results reinforce the need for maintaining and 
strategically managing gold reserves, especially in anticipation of external shocks, currency volatility, or 
geopolitical crises. During periods of systemic risk, policy responses can include stabilizing commodity markets, 
ensuring gold market liquidity, and monitoring speculative behavior. Gold’s inverse relationship with equities 
during crises can be used as a real-time indicator of investor sentiment and stress in financial markets. The results 
justify further innovation in gold-based ETFs, mutual funds, and derivatives that can be more widely accessible to 
retail and institutional investors. SEBI could encourage the development of hedging tools linked to gold for 
managing equity portfolio risk. 
 

References 
Al-Yahyaee, K. H., Rehman, M. U., Mensi, W., & Al-Jarrah, I. M. W. (2019). Can uncertainty indices predict Bitcoin prices? A revisited 

analysis using partial and multivariate wavelet approaches. The North American Journal of Economics and Finance, 49, 47-56. 
https://doi.org/10.1016/j.najef.2019.03.019 

https://doi.org/10.1016/j.najef.2019.03.019


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https://doi.org/10.1111/j.1540-6288.2010.00244.x
https://doi.org/10.1016/j.jbankfin.2009.12.008
https://doi.org/10.1016/0304-4076(86)90063-1
https://doi.org/10.1016/j.intfin.2004.07.002
https://doi.org/10.1016/j.irfa.2015.03.011
https://doi.org/10.1016/j.irfa.2012.12.001
https://doi.org/10.1198/073500102288618487
https://doi.org/10.1016/j.resourpol.2012.10.001
https://doi.org/10.1016/j.iimb.2013.12.002
https://doi.org/10.2307/2938260
https://doi.org/10.1016/j.eneco.2014.02.014
https://doi.org/10.1126/science.185.4157.1124

