DCC GARCH Research Articles

An Empirical Study of Volatility in Cryptocurrency Market

Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.

A Comparative Analysis on the Role and Market Linkages of Gold Backed Assets During COVID-19 Pandemic

Gold is a traditional favorite investment avenue for investors all over the globe, particularly during the crisis period. Irrespective of the nature of the crisis, investors are allocating their funds to different gold-backed assets. This paper uses various globally traded gold-backed assets to identify its role and market linkages during the Covid 19 pandemic. Daily prices of assets from March 2020 to January 2022 were employed. DCC GARCH model is used to ascertain time-varying correlations and quantile regression was employed to examine the relationship between assets in different quantiles. Based on the analysis, safe haven property of all the assets is revealed and it is associated with the severity of the stock market crash as specified by the quantiles. Moreover, double exposure of gold mining stock results in different flights to quality. Co-movement of gold bullion, gold futures, and gold volatility index is visible during this crisis. Gold Exchange Traded Funds and gold-backed cryptocurrency offer diversification by decoupling with gold bullion in the portfolio. The paper highlights the importance of the choice of gold-backed assets along with gold bullion in the investment portfolio based on its role and market linkages.

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

This paper attempts to understand the dynamic interrelationships and financial asset capabilities of Bitcoin by analysing several aspects of its volatility vis-a-vis other asset classes. This study aims to analyse the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk management and ideal for risk-averse investors in anticipation of negative shocks to the market (leverage effect). This paper also examines Bitcoin as an investment and hedge alternative to gold as well as NSE NIFTY using a multivariate DCC GARCH model. DCC GARCH models are also used to check whether correlation (co-movement) between the markets is time-varying, examine returns and volatility spillovers between markets and the effect of the outbreak of COVID-19 in India on the investigated markets. The results show that given the supply of Bitcoin is fixed, low returns realisation is equivalent to excess supply over demand wherein investors are selling off Bitcoin during bad times. The positive co-movement between Bitcoin and gold during the COVID-19 outbreak shows that investors perceived Bitcoin as a relatively safe investment. However, overall analysis shows that Bitcoin was not considered a safe hedge and an investment option by Indian investors during the study period.

Are Islamic investments still safe assets during the COVID‐19 pandemic?

While looking for safe‐haven assets, the literature obtained mixed and varying results, changing from one period to the next, or one geographical area to another. Recently, this field of research grew even more, motivated by the changing environment resulting from the global financial crisis and the current COVID‐19 pandemic. We compare five Islamic and five conventional leading financial indexes for the period 2004–2020, covering both global and regional data (Asia‐Pacific, Europe, GCC, and the United States). By employing DCC GARCH and extended GARCH (1,1) models, we find a lower volatility and higher persistence in Islamic indexes when compared to their conventional alternatives, holding also when traditional safe‐haven assets are included in comparative terms and across geographical areas. We therefore provide robust evidence on the consistent behavior of Islamic assets: Their defensive properties remain and are even stronger in the current unprecedented and ongoing crisis.

The volatility spillover between NFT investment index and global technology index: DCC-GARCH application

Purpose- NFT is a digital token that represents a unique, one-of-a-kind asset on the blockchain. In this respect, NFTs can be used to represent ownership of any unique asset. In this study, the volatility spillover relationship between the NFT Investment Index and the Global Technology Index (XTEC) is investigated. Methodology- More than one GARCH type model has been developed that reveals the relationship between assets in financial markets. The DCC GARCH model was preferred because it is a current model that reveals the variable correlation coefficient depending on time. The DCC-GARCH method was preferred for modeling the volatility spillover in the study. Daily data covering the period 19.04.2021-22.04.2022 are used. Findings- - According to the findings of the study; A mutual volatility spillover has been detected between the NFT Investment Index and XTEC. Accordingly, the 1% shock in XTEC increases the NFT Investment Index volatility by 0.24%, while the 1% shock in the NFT Investment Index increases the XTEC volatility by approximately 1.86%. The findings show that NFT Investment Index volatility is more effective on XTEC volatility. Conclusion- Those who invest in NFT or technology markets and those who are considering investing should also take into account the developments in the other market in question in terms of risk management. In addition, market regulators should take a proactive approach by considering the impact and importance of NFT markets. Keywords: Blockchain, crypto

Volatility Co-Movement between Bitcoin and Stablecoins: BEKK–GARCH and Copula–DCC–GARCH Approaches

This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.

Optimal Portfolio Allocation between Global Stock Indexes and Safe Haven Assets: Gold versus the Swiss Franc (1999–2021)

This paper contributes to the literature on safe haven assets, analyzing gold and the Swiss Franc’s defensive properties inside various global stocks portfolios. The analysis relies on monthly data extending over the last two decades. Drawing on Multivariate Garch DCC models, the hedging effectiveness of bivariate Swiss Franc-hedged portfolios is found to be notably higher than that of gold-hedged portfolios. Value-at-Risk simulations, assuming equal or “optimal” portfolio weights, confirm these results inside a multivariate asset framework, while a regression approach with quantile dummies provides further support in this regard. Since the better hedge and safe haven properties of the Swiss Franc are likely to persist in the future, the main policy implication of the paper concerns asset allocation strategies giving relatively more weight to the Swiss currency in global stock portfolios.

Contagion effect of cryptocurrency on the securities market: a study of Bitcoin volatility using diagonal BEKK and DCC GARCH models.

The fundamental aim of this study is to examine the contagion effect of Bitcoin on the National Securities Exchange, Shanghai Stock Exchange, London Stock Exchange, and Dow Jones Industrial Average by analyzing the volatility spillover and correlation between these markets to understand the short-term and long-term impact of this volatility ranging from the shocks during the period March 2017–May 2021. Irrespective of ups and downs happening in the cryptocurrency market, more investors are investing their money in the cryptocurrency market. This paper will contribute to the existing literature by studying volatility spillover in the market, its contagion effect, and to identify if there is a long-term and short-term impact between the Bitcoin and stock markets facilitating the transmission of volatility spillover. We employed the Diagonal BEKK and DCC MGARCH models to investigate the integration between Bitcoin and the stock markets. From the empirical analyses, we find the overall time-varying correlation between Bitcoin and the stock markets is low, indicating that Bitcoin can be taken as an asset to hedge against the risk of these stock markets. It was also evident that these stock markets responded more to the negative shocks during 2018 and 2021 than the positive shocks in the Bitcoin market. Our study may be helpful for investment decisions, academia, and policymakers.

DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations

This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances and correlations of daily returns based on measures of realized variances and correlations built from intraday data. Formulas for multi-step forecasts of conditional variances and correlations are provided. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecasts the scalar HEAVY models outperform the scalar BEKK-HEAVY model based on realized covariances and the scalar BEKK, DCC, and DECO multivariate GARCH models based exclusively on daily data.

Balanced Funds in India Amid COVID-19 Crisis: Spreader of Financial Contagion?

In its latest financial stability report, dated 11 January 2021, the Reserve Bank of India (RBI) emphasised the significance of balanced mutual funds in risk transmission. We investigate the transmission of volatility and contagion effect from Indian balanced funds to representative indices—Bank, PSU Bank, Private Bank, Financial Services, Broader Market, Services Sector and Fixed Income—using three established models: Diagonal BEKK (1995), Dynamic Conditional Correlation (DCC GARCH (2002)) and network model. In analyses of financial time series data, the COVID-19 pandemic has been widely regarded as a structural break. We may better understand the dynamism and scale of spillover before and during a crisis by dividing the study into two periods: pre-COVID-19 (January 2011–29 December 2019) and during COVID-19 (30 December 2019–20 April 2021). The results of all three models support our hypothesis of statistically significant spillover from balanced funds to chosen indices, with strong persistence and a marked increase in long-term volatility spillover, showing the presence of contagion effects. The findings of this paper can assist fund managers in diversifying their portfolios while also benefiting investors educationally. JEL Classification: C23, G12, G23

The COVID-19 Outbreak and Risk–Return Spillovers between Main and SME Stock Markets in the MENA Region

This study investigates return and asymmetric volatility spillovers and dynamic correlations between the main and small and medium-sized enterprise (SME) stock markets in Saudi Arabia and Egypt for the periods before and during the COVID-19 pandemic. Return and volatility spillovers are modelled using a VAR-asymmetric BEKK–GARCH (1,1) model, while a VAR-asymmetric DCC–GARCH (1,1) model is employed to model the dynamic conditional correlations between these markets, which are then used to determine and explore portfolio design and hedging implications. The results show that while bidirectional return spillovers between the main and SME stock markets are limited to Saudi Arabia, shock and volatility spillovers have different characteristics and dynamics in both main–SME market pairs. In addition, the dynamic correlations between the main and SME markets are mostly positive and have notably increased during the COVID-19 pandemic, particularly in Saudi Arabia, suggesting that adding SME stocks to a main stock portfolio enhances its risk-adjusted return, especially during tranquil market phases. One practical implication of our results is that the development of SME stock markets can indirectly contribute to economic development via the main market channel and provide an avenue for portfolio diversification and risk management.

Real Estate Risk Exposure to Private and Public Sector Banks using DCC GARCH: Study from india

Real Estate Risk Exposure to Private and Public Sector Banks using DCC GARCH: Study from india

The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test.

Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between WTI crude oil futures market and Chinese stock index futures market or US stock index futures market. The relationship in mean between WTI crude oil futures prices and S&P 500 stock index futures, or CSI 300 stock index futures is weakening. Meanwhile, there is a decreasing dynamic conditional correlation between the WTI crude oil futures market and Chinese stock index futures market or US stock index futures market after the breakpoint in the price series. The Chinese stock index futures are less affected by short-term fluctuations in crude oil futures returns than US stock index futures.

Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis

Volatility and investor sentiment have been factors for the slow adoption rate of Bitcoin (BTC) that was first recognized in 2008 as a potential store of value, investment vehicle and a hedge alternative to gold during a recession. The purpose of this applied mathematics study will use a multivariate DCC GARCH model. Bitcoin holds its ground in volatility. This study examines Bitcoin as an investment and hedge alternative to gold as well as the major stock index. To perform the research to explore the viability of Bitcoin as an investment and hedge alternative to gold, the authors conducted a DCC GARCH model analysis. The findings of this research paper confirm Bitcoin’s cyclical performance between volatility and adoption. The findings give a strong ground for Bitcoin as the new digital currency, store of value, medium of exchange, and a unit of account and incentivize further research by theorists, scholars and examiners. The significance of this applied mathematics research and analysis will allow an unstoppable, incorruptible, and uncontrollable store of value, and investment vehicle, without governmental or institutional intervention. This study contributes by comparing and contrasting volatility stability based on the return levels of each Bitcoin on major indexes traded with BTC (based on fiat currencies) and gold.

Pengaruh Indeks Volatilitas, Nilai Tukar dan Pertumbuhan Ekonomi Terhadap Integrasi Pasar Modal ASEAN Dengan Pasar Modal Amerika Serikat

There are many variables which will be influencing capital market integration. Some of those variables are come from external factor, such as Volatility Index (VIX), exchange rate, and economic growth. This study aims to analyze the effect of VIX, exchange rate, and economic growth to the integration of ASEAN capital market and United States capital market. The data was collected from secondary data in the form of VIX quarterly data, exchange rate, economic growth, ASEAN capital market such as Indonesia (JKSE), Singapore (STI), Philippines (PSEi), Thailand (SETi), Malaysia (KLCI), and United States capital market (DJIA) during the first quarter from 2005 to third quarter 2020. Further, the data was analyze by using DCC GARCH method was used to see the dynamic correlation between ASEAN capital market and US capital market. Hence, GARCH method to examine the effect of changes in VIX, exchange rate, and economic growth for ASEAN capital market and US capital market. The result showed a positive dynamic correlation between ASEAN capital market and US capital market, the presence of VIX and exchange rate has significant effect for ASEAN capital market and US capital market, while economic growth only has significant effect to the integration of STI.

Volatility spillovers in the dry bulk shipping markets

Baltic Dry Index over the years has witnessed volatility and been an enigma for ship owners and cargo owners. This study tries to investigate the volatility and spillovers in Dry Baltic sub-indices, namely Handymax, Supramax, Panamax and Capesize. The study uses daily data from August 2012 till July 2019 for seven years. The paper contributes to the literature by applying three different multivariate GARCH models, namely the DCC GARCH model, corrected DCC model, and asymmetric corrected DCC GARCH model. Hosking test is applied to find the best model fit and corrected DCC GARCH model is found to be the best fit. The results bring out many interesting spillovers between the Baltic sub-indices and various factors affecting the same are discussed. China trade factor emerges as the major contributor to various spillovers and volatility in the dry bulk market.

BORSA, DÖVİZ KURU VE PETROL FİYATLARI ARASINDAKİ OYNAKLIK YAYILIMI

Küreselleşme, piyasalar arasındaki ilişkileri artması, finansal piyasalardaki risklerin yükselmesi, iletişim araçlarının yaygınlaşması ve iletişim maliyetinin düşmesi gibi konular piyasalar arasındaki oynaklıkların artmasına neden olmuştur. Oynaklık ve oynaklık yayılımları finansta önemli hale gelmiştir. Piyasalar arasındaki oynaklık yayılımlarının bilinmesi yatırım kararlarının verilmesi, çeşitlendirme ile riski azaltımı, portföy yönetimi, fiyat keşfi, uluslararası sermaye hareketleri gibi konular bakımından önem taşımaktadır. Bu çalışmada Borsa İstanbul (BİST), döviz kuru ($/₺) ve petrol (WTI) arasındaki oynaklık yayılımları araştırılmıştır. Çalışma 04.01.2010 – 15.03.2019 dönemine ait günlük verilerle yapılmıştır. Değişkenler GARCH modeliyle tahmin edildikten sonra Hong’un (2001) varyansta nedensellik testi ile oynaklık yayılımları ve DCC GARCH yöntemiyle de değişkenler arası ilişkiler araştırılmıştır. Çalışma sonuçları Borsa İstanbul ve döviz kuru arasında karşılıklı, petrolden ise Borsa İstanbul ve döviz kuruna doğru tek yönlü oynaklık yayılımları olduğunu göstermiştir. DCC GARCH yönteminde ise petrol ile Borsa İstanbul arasındaki ilişki belirlenmiştir. Bu sonuçlar Borsa İstanbul ve döviz kurunda oluşan oynaklıklarda petrolün önemli bir belirleyici olduğunu göstermektedir.

Analysing volatility spillovers between grain and freight markets

The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model (DCC GARCH) is used to find the spillovers between the two markets and thereby explore the effect of GOFI on GOI markets from the year 2013. The research article consists of four sections after introducing the subject namely a literature review, research methodology and models, analysis and conclusions of the study.

Weathering financial crisis in China: the role of global market integration

ABSTRACT We test the hypothesis that the integration of China into the global financial system, as a consequence of domestic market reforms, reduced the effectiveness of intervention efforts during the stock market crises. Using an event study methodology in tandem with the DCC GARCH and Markov Regime Switching models, we investigate whether the interventions were able to address the decline in market returns and volatility during the 2008 and 2015 stock market collapses. Results show that the 2015 measures were ineffective compared to the 2008 ones and that the increasing global integration of the Chinese market played a significant role in their failure. This study is the first of its kind and the results have important implications for policymakers, given the vital position of China in the international economy.

Volatility Spillover Between Stock Market Sectors

In this study, volatility spillover and volatility relationship of Istanbul Stock Exchange (BIST) basic markets were investigated by using daily data between 4 January 2010 and 28 August 2019. Volatility spillover between industry, trade, service and financial sectors were examined by causality test in the variance Hafner and Herwartz (2006), and inter-sector relations were examined by DCC GARCH method. In this study, dynamic conditional correlation between BIST core markets and volatility spillover from industry, trade and service sector to financial sector were observed. In addition, it has been determined that the relationship between the major sectors of BIST varies with time and that economic and political events partially determine these changes. It has been understood that the relationship between financial-industry, service-trade, financial-service and service-industry sectors is higher than the relationship between trade-financial and industry-trade sectors. According to these results, it is not possible to reduce risk through diversification based on BIST core markets. The results provide guidance for investors, risk managers and portfolio managers.