Bivariate GARCH Model Research Articles

Optimal hedging efficiency in global freight markets: Comparing FFAs and time charter strategies

The paper aims to mitigate financial risk in highly volatile shipping freight markets by employing a dynamic hedging model. The primary criterion for evaluating the effectiveness of various methods for estimating optimal hedge ratios through Forward Freight Agreements (FFAs) is the minimum variance hedging rule. Four different methods are utilized to estimate two types of hedge ratios. The first type, a static hedge ratio, is calculated using the OLS and ECM methods. The second type, a time-varying hedge ratio, is determined through a bivariate GARCH model and a Rolling Window OLS method. Additionally, the hedging effectiveness of the traditional method of hedging, time chartering, is compared to the more modern and sophisticated shipping derivatives methods using the coefficient of variation. Highlights Estimating optimal hedge ratios through Forward Freight Agreements (FFAs) with the minimum variance hedging rule Four different methods are utilized to estimate two types of hedge ratios for static and dynamic hedging Assess the hedging effectiveness of the different methods of hedge ration estimation Compare the hedging effectiveness of the traditional method of hedging, time chartering, to the more modern and sophisticated shipping derivatives methods using the coefficient of variation

Vulnerability of a developing stock market to openness: One-way return and volatility transmissions

Drawing from two independent literature sources on vulnerability and stock price volatility, we show that the high magnitude and persistence of stock return volatility evident in the Nigerian Exchange Group (NGX) are, at least in part, indications of vulnerability to openness, which paves the way for the transmission of external shocks. Using a uniquely modified set of bivariate asymmetric GARCH models, we investigate one-way return and volatility transmissions from the international Brent oil market, natural gas markets, carbon markets, and naira-dollar exchange market to the NGX. Firstly, we document direct and asymmetric return and volatility transmissions from the Brent oil market to the NGX, denoting the fragile sensitivity of the NGX to international oil price turbulence. Secondly, we find a direct volatility transmission from natural gas markets to the NGX, implying an increasing reliance on natural gas exports by the Nigerian government. Thirdly, we document an inverse volatility transmission from carbon markets to the NGX, indicating Nigeria's negative reaction, as a major oil and gas producer, to increasing global sustainable production activities. Fourthly, the insignificant effect of the naira-dollar official exchange rate on volatility in the NGX demonstrates the spurious impression that Nigeria's exertion of control over the exchange rate is shielding the economy. In sum, our results confirm that the openness of the NGX makes it vulnerable to external shock transmissions from related external markets subsisting outside Nigeria. Our results are robust to an alternative estimation procedure that employs a different measure of price volatility from the three external markets.

Diverse linkages between green bonds and equity indices of developed and developing economies

This paper investigates the dynamic conditional correlations and volatility spillovers between global stock markets and international green bonds. Envisaging the linkages between the emerging green bond markets and global equities given the unforeseen global pandemic. We use R software to run bivariate VAR-BEKK GARCH and BEKK GARCH models to investigate the time-varying conditional volatility between global stock indices and green bond indices. Daily prices of all variables from October 2014 to April 2023 are sourced from the Bloomberg database. The short-term influence of past events, as well as the long-term persistence of green bonds, on the current conditional volatility of global equity indices of developed and emerging economies is observed. Whereas from equity markets to green bond indices, the short-term as well as long-term impact is confined to only 2-3 indices. The results provide future direction for policymakers, researchers, and global investors in hedging and creating an optimal portfolio.

Systemic risk measurement: A Quantile Long Short-Term Memory network approach

In finance, systemic risk is the risk that the crisis of an institution could trigger instability or bring down an entire system or market. The Delta Conditional Value-at-Risk is a market-based measure proposed by the recent literature to quantify the systematicity of some financial institutions. Several methods have been proposed to estimate this measure, and the choice of the best method is still an open question. The bivariate constant conditional correlation GARCH model represents one of the most preferred approaches since it allows the computation of the Delta Conditional Value-at-Risk in a closed form. Nevertheless, it requires strong distributional assumptions that are often considered unrealistic. We develop a Quantile Long Short-Term Memory network approach that allows the estimation of the Delta Conditional Value-at-Risk of several financial institutions simultaneously. The model consists of a multi-output neural network able to provide, at the same time, the log-return quantiles of different institutions useful to measure the systemic risk. Furthermore, the proposed model does not need any particular assumption, and it is specifically designed to avoid quantile crossing issues affecting the traditional quantile regression-based approach. Numerical experiments on data of some global systemically important banks reported in the Financial Stability Board validated our approach. We obtain Delta Conditional Value-at-Risk estimates that accurately capture market dynamics and produce a ranking of systemic banks that meets the desired properties of stability and persistence.

Price Discovery and Volatility Spillover: An Empirical Analysis of Indian Futures-Spot Cardamom Markets

Price discovery and its related volatility spillover effect are significant indicators in commodity derivatives market to hedge the risk against sharp price fluctuations. This study empirically analyses the price discovery and volatility spillover using vector error correction model, Granger causality and bivariate exponential GARCH model (EGARCH) in Indian cardamom spot-futures markets. Daily time series data of spot market and near month futures contracts spanning the period from January 2009 to December 2020 have been used for the study. These price series data are based on the authenticated sources of Multi Commodity Exchange. The results of vector error correction model supported by Granger causality test indicates that spot market plays the role of leader as it is more efficient in reflecting the new information to prices. The results of bivariate EGARCH (1,1) model show that volatility spillover from cardamom spot market to futures market is dominant.

A new bivariate approach for modeling the interaction between stock volatility and interest rate: An application to S&P500 returns and options

The GARCH models developed so far do not take into account the interaction between the volatility of asset returns and the dynamics of the interest rate. In this paper, we propose a bivariate GARCH model in which interest rate movements and asset price volatility are fully coupled. This approach yields explicit and simple to implement recursion formulas for the moment generating function, which can be exploited to compute option prices by applying the fast Fourier transform or other convolution techniques. We perform a thorough and comprehensive empirical analysis based on real S&P500 return and option data showing the usefulness and robustness of the suggested methodology. Both in-sample and out-of-sample results reveal the superiority of our approach over the GARCH model with constant interest rates.

예상치 못한 외국인 순매도로 인한 비대칭적 변동성

This paper investigates whether the unexpected net selling by foreign investors is regarded as a bad news for domestic investors and causes asymmetric volatility beyond the bad news of the market. Stocks are sorted into five groups by the ratios of foreign investors' holding stock, and analyze if the lower ratios would lead to any difference in the asymmetry. We estimate bivariate GARCH model, diagonal BEKK, and find that stocks with lower proportion of foreign investors tend to show higher asymmetric volatility. Since stocks with lesser foreign investors tend to higher proportions of local investors, we can conclude that stocks with higher proportions of local investors tend to show higher asymmetric volatility. The effect of firm size was further analyzed to determine whether this was the cause of the asymmetric volatility. The stocks of five groups were again sub-divided by firm size into three groups, large, mid-cap, and small stocks. Within each group, additional asymmetry due to the unexpected net selling by foreign investors was evident in small and mid-cap stocks. Thus we claim that domestic individual investors' risk aversion would be a possible cause for the asymmetric volatility along with a bandwagon effect. Bandwagon effect arise since individual traders are aware that foreign investors are well-informed traders and choose to follow their action.

Commodity trading and inflation: ground reality in India using bivariate GARCH models

PurposeThe nexus of commodity prices with inflation is one of the main concerns for a nation's economy like India. The literature does not have enough volatility-based study, especially using the multivariate GRACH family of models to find a link between these two. It is the main reason for the conduct of this study. This paper aims to estimate the volatility effects of commodity prices on inflation.Design/methodology/approachFor ten years (2011–2022), future prices of selected seven agriculture commodities and inflation indices (wholesale price index [WPI] and consumer price index [CPI]) are gathered every month. BEKK GARCH model (BGM) and DCC GARCH model (DGM) are employed to determine the volatility effect of commodity prices (CPs) on inflation.FindingsThe authors find that volatility's short-term (shock) impact on agricultural CPs to inflation does not exist. However, the long-term volatility spillover effect (VSE) is significant from commodities to inflation.Practical implicationsThe study's findings have a significant implication for the policymakers to take a long-term view on inflation management regarding commodity prices. The findings can facilitate policy on the choice of commodities and the flexibility of their trading on the commodities derivatives market.Originality/valueThe findings of the study are unique. The authors do not observe any study on the volatility effect of agri-commodities (agricultural commodities) prices on inflation in India. This paper applies advanced techniques to provide novel and reliable evidence. Hence, this research is believed to contribute significantly to the knowledge body through its novel evidence and advanced approach.

Impact of exchange rate fluctuations on US stock market returns

PurposeThe authors analyze the relationship between exchange rate fluctuations of a number of major currencies and its impact on US stock market returns, as proxied by the S&P 500. Many studies have explored this topic since the early 1970s with varied results and with no evidence that clearly explains the relationship between exchange rates and stock market returns. This study takes a different look at this hypothesis and investigates the pairwise relationship between various exchange rates and the United States stock market returns (S&P 500 INDEX) from January 2000 to December 2019.Design/methodology/approachThe authors test the data for unit roots using Phillip-Perron method. They use Johansen cointegration model to determine whether returns on S&P 500 are integrated with S&P 500. They use the VAR/VECM analysis to test whether there are any interdependencies between exchange rates and stock market return. Finally, they use various GARCH models, including the EGARCH and TGARCH models, to determine whether there exist volatility spillovers from exchange rate fluctuations in various markets to the volatility in the US stock market.FindingsUsing GARCH modeling, the authors find volatility in Australian dollar, Canadian dollar and the euro impact market return, and the volatility of Australian dollars and euro spills over to the volatility of S&P 500. They also find that the spillover is asymmetric for Australian dollars.Research limitations/implicationsOne of the limitations could be that the authors use different bivariate GARCH models rather than the MV-GARCH models. For future project(s), they plan to do this analysis from the perspective of a European Union or a British investor and use returns in those markets to see the impact of exchange rates on those markets. It would be interesting to know how the relationship will change during periods of financial crises. This could be achieved by employing structural break methodology.Originality/valueMany studies have explored the relation between stock market returns and exchange rates since the early 1970s with varied results and with no evidence that clearly explains the relationship between exchange rates and stock market returns. This paper contributes by adding to the existing literature on impact of exchange rate on stock returns and by providing a detailed and different empirical analysis to support the results.

The volatility of beef and lamb prices in Türkiye: The role of COVID-19, livestock imports, and energy prices.

This study examines the volatility of beef and lamb prices in Türkiye, as food price inflation compromises the food security of low- and middle-income households. The inflation is the result of a rise in energy (gasoline) prices leading to an increase in production costs, together with a disruption of the supply chain by the COVID-19 pandemic. This study is the first to comprehensively explore the effects of multiple price series on meat prices in Türkiye. Using price records from April 2006 through February 2022, the study applies rigorous testing and selects the VAR(1)-asymmetric BEKK bivariate GARCH model for empirical analysis. The beef and lamb returns were affected by periods of livestock imports, energy prices, and the COVID-19 pandemic, but those factors influenced the short- and long-term uncertainties differently. Uncertainty was increased by the COVID-19 pandemic, but livestock imports offset some of the negative effects on meat prices. To improve price stability and assure access to beef and lamb, it is recommended that livestock farmers be supported through tax exemptions to control production costs, government assistance through the introduction of highly productive livestock breeds, and improving processing flexibility. Additionally, conducting livestock sales through the livestock exchange will create a price information source allowing stakeholders to follow price movements in a digital format and their decision-making.

Price Discovery Mechanism and Volatility Spillover between National Agriculture Market and National Commodity and Derivatives Exchange: The Study of the Indian Agricultural Commodity Market

Agricultural commodity markets are critical to the global economy. This study investigates the price discovery mechanism, lead-lag relationship, and volatility spillover between spot prices on the National Agriculture Market (E-NAM) and futures and spot prices on the National Commodity and Derivative Exchange (NCDEX) in the Indian agricultural commodity market. The Johansen Cointegration, Vector Error Correction (VEC), Granger causality tests, and bivariate GARCH models were applied to daily data from April 2016 to December 2020 for twelve agricultural commodities traded on the E-NAM and NCDEX. We discovered the long-run relationship using the Johansen Cointegration test and concluded that the NCDEX spot and futures market is dominant in the price discovery mechanism, and the NCDEX futures and spot markets lead the E-NAM spot prices having a unidirectional or bidirectional relationship. Furthermore, the bivariate GARCH model suggested a volatility spillover from E-NAM spot prices to NCDEX futures and spot markets for most commodities, except for bajra, barley, and jeera, which have no volatility spillover. The study’s findings have important implications for various stakeholders, including policymakers, farmers, investors, traders, and others who want to reduce price risks by using information from the E-NAM market’s spot prices.

The influence of the COVID-19 pandemic on the short- and long-term interactions in the agricultural market: Evidence from a connectedness network approach.

This study employs a bivariate GARCH model to examine the influence of the COVID-19 pandemic on the interactions of the commodities in the agricultural market via a connectedness network approach. Empirical results show that this pandemic alters the commodities' roles-the activators, net transmitters, and net receivers-in the volatility and return connectedness but not for the activators in the correlation connectedness. Moreover, this pandemic enhances the interactive degree of the unidirectional negative return spillovers and the bidirectional distinct-sign volatility spillovers but doesn't for the interactive degree of correlation. Thus, the COVID-19 pandemic, a short-term drastic event, can influence short-term interactions like volatility and return spillovers but can't affect one long-term interaction like the correlation. Nevertheless, this pandemic raises the intensity of the correlation as well as volatility and return spillovers. The findings provide policymakers to make short- and long-term investment strategies in the agriculture market.

Impact of cryptos on the inflation volatility in India: an application of bivariate BEKK-GARCH models

Purpose The main aim of the study is to explore the volatility spillover effect of cryptocurrencies (Bitcoin, Ethereum and Litecoin) on inflation volatility in India.Design/methodology/approach A popular tool, the Bivariate GARCH model (BEKK-GARCH), to study the volatility spillover effect, is applied in the study. Monthly data of cryptocurrencies and inflation (WPI and CPI indices) are gathered from 2015 to 2021.Findings Significant short-term responsiveness of volatility of cryptocurrencies on the inflation volatility is found. In addition to this, the significant volatility spillover effect from the cryptocurrencies to the inflation volatility is found.Practical implications The findings of the current paper can be of use for inflation management, target inflation policies and policies to contain the volatility of cryptocurrencies. The significance of the current paper is relevant as governments worldwide are officially recognizing cryptocurrencies and starting the process of launching their official virtual currency.Originality/value No other study is observed on the topic. Hence, the contribution and novelty of the findings of the current paper are very high and add value to the nonexistent literature on the topic. Lack of the number of inflation observations (data of CPI and WPI are available only in monthly frequency) crimps the model estimation. As the cryptocurrencies become old, more data points will be available by design, and such problems can be resolved, and better model estimation may be possible.

Empirical Evidence of Associations and Similarities between the National Equity Markets Indexes and Crude Oil Prices in the International Market

The stock market is a major component of the financial sector of any economy and it is particularly affected by crude oil price. Moreover, the financialization of the oil market in the last three decades increased its association with the financial markets. The main purpose of this paper is to uncover similarities among the economy of selected countries based on the association between their national stock markets and crude oil price. This is achieved by time series clustering of the conditional correlations between the national stock market index returns and crude oil price returns estimated from bivariate GARCH models. The clusters do not lead to a clear classification concerning the countries’ stage of development, emerging and developed, or the geographical region which can be explained by crude oil market financialization.

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

Previous studies aimed at determining hedging strategies commonly used daily closing spot and futures prices for the analysis and strategy building. However, the daily closing price might not be the appropriate for price in some or all trading days. This is because the intraday data at various minute intervals, in our view, are likely to better reflect the information about the concrete behavior of the market returns and reactions of the market participants. Therefore, in this study, we propose using high-frequency data along with daily data in an attempt to determine hedging strategies, using five major international currencies against the American dollar. Specifically, in our study we used the 5-min, 30-min, 60-min, and daily closing prices of the USD/CAD (Canadian Dollar), USD/CNY (Chinese Yuan), USD/EUR (Euro), USD/GBP (British Pound), and USD/JPY (Japanese Yen) pairs over the 2018–2019 period. Using data at 5-min, 30-min, and 60-min intervals or high-frequency data, however, means the use of a relatively large number of observations for information extractions in general and econometric model estimations, making data processing and analysis a rather time-consuming and complicated task. To deal with such drawbacks, this study collected the high-frequency data in the form of a histogram and selected the representative daily price, which does not have to be the daily closing value. Then, these histogram-valued data are used for investigating the linear and nonlinear relationships and the volatility of the interested variables by various single- and two-regime bivariate GARCH models. Our results indicate that the Markov Switching Dynamic Copula-Generalized autoregressive conditional heteroskedasticity (GARCH) model performs the best with the lowest BIC and gives the highest overall value of hedging effectiveness (HE) compared with the other models considered in the present endeavor. Consequently, we can conclude that the foreign exchange market for both spot and futures trading has a nonlinear structure. Furthermore, based on the HE results, the best derivatives instrument is CAD using one-day frequency data, while GBP using 30-min frequency data is the best considering the highest hedge ratio. We note that the derivative with the highest hedging effectiveness might not be the one with the highest hedge ratio.

Exploring the Systemic Risk of Domestic Banks with ΔCoVaR and Elastic-Net

We analyze the systemic risk of Italian banks with the ΔCoVaR from a bivariate normal GARCH model. The results show that it is a good measure of systemic risk and is applicable to the ranking of Italian other systemically important institutions. Using an elastic-net approach, we identify the balance sheet and market variables that explain the ΔCoVaR of Italian banks. The analysis confirms that these variables are key determinants of systemic importance and highlights how higher capitalization is beneficial to tackling systemic risk. And, we detect a connection between ΔCoVaR and some variables for trading and investment banking.

Volatility integration of gold and crude oil prices with the interest rates in India

PurposeCrude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.Design/methodology/approachThis study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.FindingsThe main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.Practical implicationsGold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.Originality/valueGold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.

Volatility spillover among sector equity returns under structural breaks

Recent evidence suggests that ignoring structural breaks in volatility in financial asset returns can result in overestimation of volatility spillover among markets. This paper examines volatility spillover among major US equity sectors (i.e. Financial, Technology, Energy, Health, Consumer and Industrial) with bivariate GARCH models utilizing daily data from April 2006 to March 2021 after adjusting for volatility breaks. I find significantly less volatility spillover between sector returns after adjusting for detected volatility breaks into a bivariate GARCH model. I also show that after adding volatility breaks into a model the estimated hedge ratios change significantly and show considerably less variability over time, which can result in substantial savings in portfolio rebalancing costs.

Non-Traditional Systemic Risk Contagion within the Chinese Banking Industry

Systemic risk contagion is a key issue in the banking sector in maintaining financial system stability. This study is among the first few to use three different distance-to-risk measures to empirically assess the domestic interbank linkages and systemic contagion risk of the Chinese banking industry, by using bivariate dynamic conditional correlation GARCH model on data collected from eight prominent Chinese banks for the period 2006–2018. The results show a relatively high correlation among almost all the banks, suggesting an interconnectedness among the banks. We found evidence that the banking system is exposed to significant domestic contagion risks arising from systemic defaults. Given that Chinese markets deliver weak signals of forthcoming stress in banking sectors, new policy intervention is crucial to resolve the hidden stress in the system. The results have important policy implications and will provide scholars and policymakers further insight into the risk contagion originating from interbank networks.

Volatility spillover between exchange rate and stock returns under volatility shifts

Recent evidence suggests that financial markets experience shifts in volatility (i.e. structural breaks) and these volatility shifts should be accounted for in the models of volatility estimation. This study re-examines volatility dynamics of the US Dollar exchange rate and the US stock market utilizing bivariate GARCH models using daily data from January 2003 to May 2018. The modified iterative cumulative sum of square (ICSS) algorithm is employed to identify shifts in the variance of the two return series. The results show that if volatility shifts are ignored, there is significant volatility transmission from the US stock market to the US Dollar exchange rate but not vice versa, which is consistent with previously documented research. However, after accounting for endogenously determined variance shifts in the bivariate GARCH model, I find no significant volatility transmission across markets. I also show that dynamic risk-minimizing hedge ratios and portfolio weights change substantially when volatility shifts are incorporated into the bivariate GARCH model. My findings point to the presence of cross-market hedging by financial market participants in these markets and indicate an inherent estimation bias in the bivariate GARCH models as they tend to overestimate the volatility spillovers across markets when volatility shifts are present but ignored. I show that my results are robust to using data from other major countries over a longer time series data.