Can diversification be improved by using cryptocurrencies? Evidence from Indian equity market

During the COVID-19 pandemic and subsequent periods of US monetary policy normalization after quantitative easing during COVID-19, global financial markets have encountered elevated levels of volatility and risk. In response, investors have increasingly sought out unconventional financial assets, such as Bitcoin, to mitigate exposure and enhance portfolio diversification. This study utilizes a Dynamic Conditional Correlation (DCC) Multivariate GARCH model, specifically employing the GARCH (1,1) specification, to analyze the relationship between stock markets index of major countries and cryptocurrency, with a particular focus on Bitcoin. The results indicate statistically significant correlations between Bitcoin and stock market returns in several countries during the COVID-19 period. Volatility appears to be influenced by historical stock market performance during both the pandemic and the subsequent normalization of monetary policy. Furthermore, the DCC-GARCH models reveal low significant coefficients for ASEAN stock market indices before and during the COVID-19 pandemic, indicating that these markets may have displaced Bitcoin as a hedge asset. In contrast, stock market indices in America and Europe consistently show statistical significance across all periods, suggesting that Bitcoin’s role as a hedge in these regions is limited. In contrast, gold clearly demonstrated safe haven properties before the COVID-19 pandemic which a characteristic had not been observed for Bitcoin. However, gold has emerged as a safe haven for only ASEAN stock markets since the U.S. initial 0.25% interest rate hike.

The effects of commodity financialization on commodity market volatility

By analysing the Dynamic Conditional Correlations (DCC) of the daily stock returns of four OECD countries with that of the US for the period 2006–2010, we could find a process of increasing correlations (contagion) in the first phase of the US financial crisis and an additional increase of correlations (herding) during the second phase of the US financial crisis for the UK, Australia and Switzerland. However, the impact of the US financial crisis on Japan was limited to the increase in correlation volatilities in the first phase. We also propose a new approach (DCC Multivariate Generalized Autoregressive Conditional Heteroscedastic model with Exogenous variables (DCCX-MGARCH)) that allows simultaneous estimation of the DCC and their determinants, which can be used to identify channels of contagion. It is shown that an increase in VIX stock market index increases conditional correlations but an increase in the TED spread and relative stock market capitalization decrease conditional correlations of stock returns between four OECD countries and the US.

We examine the spillover effect from the Indian stock market to Mexico, Indonesia, Nigeria and Turkey (MINT) stock markets in order to check if suitable diversification opportunities are available to global portfolio managers investing in India. We apply Granger causality test, vector auto-regression (VAR) and dynamic conditional correlation (DCC)–MGARCH to investigate the level of integration between India and MINT economies. We observe bidirectional causality between India and Nigeria, unidirectional causality in Mexico and Indonesia, while no causality is found between India and Turkey. Our VAR results suggest that none of the MINT economies impact the return of the Indian stock market; rather returns of the Indian stock market are more affected by their own lagged values. Finally, by applying DCC–MGARCH, we observe that there is no volatility spillover from India to any of the MINT economies. We recommend that portfolio managers investing in the Indian economy may explore MINT economies as possible destinations to diversify their risk. Our study has implications for both academia and portfolio managers.

The present study examined the effects of contagion from the developed markets (The US, the UK, and Japan) to the BRIC stock markets during the period of Jan 1996 to July 2011 using daily data. It applied Dynamic Condition Correlations (DCC) model and Asymmetric Generalized Dynamic Conditional Correlation (AG-DCC) approach to capture the effects of contagion originated from developed countries. Stock market indices are observed to display a persistent and high correlation between them during and after high volatility periods. Evidence on contagion implies that diversification sought by investing in multiple markets from different regional blocks is likely to be lower when it is most desirable. As a result, an investment strategy focused solely on international diversification seems not to work in practice during turmoil periods. Since countries and financial markets react differently to sovereign shocks, stocks from different emerging economies could provide advantages over debt-only or equity-only portfolios.

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.

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

Determinants of Trading Decision: An Empirical Examination

PurposeThe purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region.Design/methodology/approachThe time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and dynamic conditional correlation multivariate GARCH model approaches, the study finds evidence of returns and volatility linkages along with the degree of connectedness among the markets.FindingsThe findings of this study are consistent with increasing market connectedness among a group of leading South American economies. Stocks exhibit relatively fewer asymmetries in conditional correlations in addition to conditional volatility; yet, the asymmetry is relatively less apparent in integrated markets. The results demonstrate that co-movements are higher toward the end of the sample period than in the early phase. The stock markets of Argentina, Brazil, Chile, and Peru are closely and strongly connected within the region followed by Colombia, whereas Venezuela is least connected with the group.Practical implicationsThe implication is that foreign investors may benefit from the reduction of the risk by adding the stocks to their investment portfolio.Originality/valueThe unique features of the paper include a large sample of national stock returns with updated time series data set that reveals the time series properties and empirical evidence on volatility testing. Unlike other studies, this paper uncovers the relation between the stock markets within the same region facing the same market condition.

Purpose – This paper aims to employ GARCH-class models (GARCH, IGARCH and CGARCH) to estimate the volatility persistence on crude oil, US, Gulf Corporation Council (GCC), Brazil, Russia, India and China (BRIC) stock markets. Also, the paper investigates the volatility spillover and the dynamic conditional correlation between crude oil, US stock index and stock indices of GCC and BRIC countries. The results prove a high degree of volatility persistence in the crude oil and stock markets. Based on the BEKK-GARCH and DCC-GARCH results, the paper finds strong evidence of the contagion effect of the oil shock and US financial crisis of 2008 on GCC and BRIC stock markets. Design/methodology/approach – In the beginning, the paper uses univariate GARCH models to estimate the volatility persistence of the oil market, US stock market, and GCC and BRIC stock markets. Then, the paper uses a trivariate BEKK-GARCH model of Malik and Hammoudeh to examine the volatility spillover between oil market, US stock market and stock markets for GCC and BRIC countries. Finally, the paper analyses the dynamic conditional correlation between US market and each stock market of GCC and BRIC countries using the DCC-GARCH model. Also, the paper estimates the dynamic conditional correlation between oil market and all stock markets. Findings – The results indicate the contagion effect of the oil shock and US financial crisis of 2008 on the GCC stock markets which are among the most important oil-exporting countries and also on BRIC stock markets which are among the emergent countries which are characterized by high economic growth level. Originality/value – The contribution of this paper is to investigate the existence of contagion effect between oil market, US stock market and two panels of emerging stock markets which have different economic characteristics, the GCC and BRIC countries, during the oil shock and US financial crisis period of 2008-2009.

Investment Strategies in Indian Stock Market: A Survey

PurposeThis study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.Design/methodology/approachThis study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.FindingsThe outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.Research limitations/implicationsThe analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.Originality/valueMost of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

The purpose of this paper is to investigate dynamic changes of the volatilities among stock indices, exchange rates, and interest rates in Taiwan, America, and South Korea during and after the period of American 9/11 terrorist attacks. Using sample data from October 1, 1999 to January 24, 2006, this study applies the Dynamic Conditional Correlation Multivariate GARCH model proposed by Engle (2002) to estimate the volatilities of markets themselves and the dynamic conditional correlations between different markets. The empirical results show: (1) during the period of 9/11 incident, both the short-run volatilities of every market and the short-run dynamic correlations between different markets enlarged significantly, but all correlations with a time lag of one day; (2) after 9/11 incident, the long-run volatilities of stock and exchange markets in all countries enlarged significantly, but the volatilities of interest rate markets diminished significantly or changed insignificantly; the long-run dynamic correlations between the same markets of different countries changed significantly, but the correlations between different markets of the same countries changed insignificantly.

The research article presents an overall comparative study of multivariate GARCH M-GARCH in portfolio risk management, where three prevailing specifications VEC, CCC and DCC models are considered. We take daily closing prices of four major assets of the year 2018 through to 2023; S&P 500, NASDAQ-100, gold futures, and US Treasury Bonds, to estimate conditional variances, covariances, and dynamic correlations using maximum likelihood estimation. The descriptive statistics indicate that volatility is highly concentrated in clustering and time varying across assets with NASDAQ having the highest volatility (2.08) and significant negative skewness that shows non-normal returns. Comparison on models based on information criteria indicates that the Dynamic Conditional Correlation (DCC) specification has better performance with less computation need, fewer 8 parameters as compared to 21 (VEC) and greater log-likelihood with high improvement (164.67 units). Adequate model specification is shown through diagnostic testing using Ljung-Box test and ARCH-LM tests. Empirical results indicate that the volatility persistence (alpha + - 0 = 0.98) and the dynamics of high correlation (beta = 0.9321) are high which indicates long-memory properties and mean-reverting behavior which does not support constant correlation assumptions.

The primary focus of this study is to examine how shocks and volatility are transmitted across sector indices in the Indian stock market. This study employs a dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, utilizing daily data from five prominent sectoral indices of the National Stock Exchange (NSE) in India, from 1 January 2016 to 31 May 2024. The study shows that sectoral volatility responds very differently across pre-COVID, COVID, post-COVID and geopolitical-crisis periods, with pro-cyclic sectors displaying sharper shock sensitivity during the pandemic and defensive sectors showing stronger stability under geopolitical stress. Consumer durables and financial services consistently provide the strongest hedging benefits during COVID and post-COVID periods, while IT-linked pairs exhibit weak hedging relationships throughout. The geopolitical shock generates volatility but does not compress sector movements as strongly as the pandemic, resulting in shallower and less persistent spillovers. Dynamic hedge ratios and portfolio weights reveal that IT becomes more relevant only in the post-COVID phase, but it does not function as a strong hedge during crisis-driven volatility. Crisis conditions tend to compress diversification opportunities by increasing correlations and connectedness, requiring more careful portfolio rebalancing. These findings offer new insights into how sector-specific behaviours and hedging performance evolve under varying macro-financial conditions, contributing to adaptive risk-management and portfolio-optimization strategies.