Journal articles on the topic 'Time-Varying Multivariate GARCH Models'
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Klepáč, Václav, and David Hampel. "Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1287–95. http://dx.doi.org/10.11118/actaun201563041287.
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The article points out the possibilities of using static D-Vine copula ARMA-GARCH model for estimation of 1 day ahead market Value at Risk. For the illustration we use data of the four companies listed on Prague Stock Exchange in range from 2010 to 2014. Vine copula approach allows us to construct high-dimensional copula from both elliptical and Archimedean bivariate copulas, i.e. multivariate probability distribution, created from process innovations. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we backtested D-Vine copula ARMA-GARCH model against the VaR rolling out of sample forecast from October 2012 to April 2014 of chosen benchmark models, e.g. multivariate VAR-GO-GARCH, VAR-DCC-GARCH and univariate ARMA-GARCH type models. Common backtesting via Kupiec and Christoffersen procedures offer generalization that technological superiority of model supports accuracy only in case of an univariate modeling – working with non-basic GARCH models and innovations with leptokurtic distributions. Multivariate VAR governed type models and static Copula Vines performed in stated backtesting comparison worse than selected univariate ARMA-GARCH, i.e. it have overestimated the level of actual market risk, probably due to hardly tractable time-varying dependence structure.
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Teulon, Frederic, Khaled Guesmi, and Salma Fattoum. "Is There A Difference Between Domestic And Foreign Risk Premium? The Case Of China Stock Market." Journal of Applied Business Research (JABR) 30, no. 5 (August 26, 2014): 1287. http://dx.doi.org/10.19030/jabr.v30i5.8785.
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This article studies the dynamic return and market price of risk for Chinese stocks (A-B shares). A Multivariate DCC-GARCH model is used to capture the feature of time-varying volatility in stock returns. We show evidence of different pricing mechanisms explained by the difference in the expected return and market price of risk between A and B shares. However, the significance of the difference between market prices of risk disappears if GARCH models are used.
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Mootamri, Imène. "Long Memory Process in Asset Returns with Multivariate GARCH Innovations." Economics Research International 2011 (September 7, 2011): 1–15. http://dx.doi.org/10.1155/2011/564952.
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The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long-term dependence in stock returns. More precisely, the long-term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process, and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confirm the presence of long memory property in the conditional mean of all stock returns.
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Fengler, Matthias R., and Helmut Herwartz. "Measuring Spot Variance Spillovers when (Co)variances are Time-varying - The Case of Multivariate GARCH Models." Oxford Bulletin of Economics and Statistics 80, no. 1 (May 16, 2017): 135–59. http://dx.doi.org/10.1111/obes.12191.
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Shiferaw, Yegnanew A. "Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models." Physica A: Statistical Mechanics and its Applications 526 (July 2019): 120807. http://dx.doi.org/10.1016/j.physa.2019.04.043.
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WU, EDMOND H. C., PHILIP L. H. YU, and W. K. LI. "VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS." International Journal of Neural Systems 16, no. 05 (October 2006): 371–82. http://dx.doi.org/10.1142/s0129065706000779.
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We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.
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Pradhan, Kailash. "The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India." South East European Journal of Economics and Business 6, no. 1 (April 1, 2011): 111–23. http://dx.doi.org/10.2478/v10033-011-0010-2.
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The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms of within sample and out of sample risk-return trade-off at various forecasting horizons. The analysis found that the VEC-GARCH time varying hedge ratio provides the greatest portfolio risk reduction and generates the highest portfolio returns.
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Burda, Martin, and Louis Bélisle. "Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo." Dependence Modeling 7, no. 1 (June 3, 2019): 133–49. http://dx.doi.org/10.1515/demo-2019-0006.
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AbstractThe Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series. The model allows for capturing more flexible dependence patterns than a multivariate GARCH model and also generalizes static copula dependence models. Nonetheless, the model is subject to a number of parameter constraints that ensure positivity of variances and covariance stationarity of the modeled stochastic processes. As such, the resulting distribution of parameters of interest is highly irregular, characterized by skewness, asymmetry, and truncation, hindering the applicability and accuracy of asymptotic inference. In this paper, we propose Bayesian analysis of the CMGARCH model based on Constrained Hamiltonian Monte Carlo (CHMC), which has been shown in other contexts to yield efficient inference on complicated constrained dependence structures. In the CMGARCH context, we contrast CHMC with traditional random-walk sampling used in the previous literature and highlight the benefits of CHMC for applied researchers. We estimate the posterior mean, median and Bayesian confidence intervals for the coefficients of tail dependence. The analysis is performed in an application to a recent portfolio of S&P500 financial asset returns.
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Metsileng, Lebotsa Daniel, Ntebogang Dinah Moroke, and Johannes Tshepiso Tsoku. "The Application of the Multivariate GARCH Models on the BRICS Exchange Rates." Academic Journal of Interdisciplinary Studies 9, no. 4 (July 10, 2020): 23. http://dx.doi.org/10.36941/ajis-2020-0058.
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The study investigated the BRICS exchange rate volatility using the Multivariate GARCH models. The study used the monthly time series data for the period January 2008 to January 2018. The BEKK-GARCH model revealed that all the variables were found to be statistically significant. The diagonal parameters estimates showed that only Russia and South Africa were statistically significant. This implied that the conditional variance of Russia and South Africa’s exchange rates are affected by their own past conditional volatility and other BRICS exchange rates past conditional volatility. The BEKK-GARCH model also revealed that there is a bidirectional volatility transmission between Russia and South Africa. The results from the DCC-GARCH model revealed that Brazil, China, Russia and South Africa had the highest volatility persistence and India has the least volatility persistence. All the BRICS exchange rates show that the fitted residuals are not normally distributed except for Russia. The recommendations for future studies were articulated.
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Linton, Oliver B., and Yang Yan. "Semi- and Nonparametric ARCH Processes." Journal of Probability and Statistics 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/906212.
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ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
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Kaura, Ruchika, Nawal Kishor, and Namita Rajput. "VOLATILITY SPILLOVER BETWEEN SPOT AND FUTURES MARKET OF HIGHLY TRADED COMMODITIES IN INDIA: The DCC-GARCH Approach." Australian Journal of Business and Management Research 05, no. 09 (July 10, 2018): 34–49. http://dx.doi.org/10.52283/nswrca.ajbmr.20180509a04.
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This study intends to examine the volatility spillover effects and measure the time-varying correlations between futures and spot prices of thirteen highly traded commodities traded on Multi Commodity Exchange (MCX) of India. The research uses Exponential GARCH proposed by Nelson (1991) to explore the direction and magnitude of spillover effects between futures and spot commodity market and employs Dynamic Conditional Correlation (DCC) GARCH proposed by Engle (2002) to demonstrate the time varying conditional correlation between heteroscedastic coefficients of the futures and spot markets. Empirical results show that significant and asymmetric bi-directional volatility spillover effects exist in case of most of the selected commodities, even though, the magnitude of volatility spillover is found larger in the direction from futures market to spot market. The dynamic correlation between the conditional variance of the spot and future markets is found to be significant in case of all the commodities except Silver and Copper. It proves that significant volatility spillover effect is present between spot and futures markets of selected commodities. Understanding of volatility transmission and interrelationship between spot and futures commodity market will help investors make right investment decisions, portfolio optimization and financial risk management. Policy makers and regulators can use this knowledge in planning and implementing appropriate regulatory framework. Much of the earlier research focuses on inter market volatility spillover taking into consideration two or more different financial markets. This study focuses on intra market volatility spillover by studying the interactions of spot-futures prices of commodities. Also, considering the time-varying nature of conditional correlations, this study employs EGARCH and multivariate GARCH (DCC) to capture the volatility spillover effects instead of univariate GARCH or standard linear VAR models.
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Mendes, Beatriz Vaz de Melo. "Calculando VaR Condicionais Usando Cópulas que Variam no Tempo." Brazilian Review of Finance 3, no. 2 (January 1, 2005): 251. http://dx.doi.org/10.12660/rbfin.v3n2.2005.1152.
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It is now widespread the use of Value-at-Risk (VaR) as a canonical measure at risk. Most accurate VaR measures make use of some volatility model such as GARCH-type models. However, the pattern of volatility dynamic of a portfolio follows from the (univariate) behavior of the risk assets, as well as from the type and strength of the associations among them. Moreover, the dependence structure among the components may change conditionally t past observations. Some papers have attempted to model this characteristic by assuming a multivariate GARCH model, or by considering the conditional correlation coefficient, or by incorporating some possibility for switches in regimes. In this paper we address this problem using time-varying copulas. Our modeling strategy allows for the margins to follow some FIGARCH type model while the copula dependence structure changes over time.
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Asai, Manabu, Chia-Lin Chang, Michael McAleer, and Laurent Pauwels. "Asymptotic and Finite Sample Properties for Multivariate Rotated GARCH Models." Econometrics 9, no. 2 (May 4, 2021): 21. http://dx.doi.org/10.3390/econometrics9020021.
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This paper derives the statistical properties of a two-step approach to estimating multivariate rotated GARCH-BEKK (RBEKK) models. From the definition of RBEKK, the unconditional covariance matrix is estimated in the first step to rotate the observed variables in order to have the identity matrix for its sample covariance matrix. In the second step, the remaining parameters are estimated by maximizing the quasi-log-likelihood function. For this two-step quasi-maximum likelihood (2sQML) estimator, this paper shows consistency and asymptotic normality under weak conditions. While second-order moments are needed for the consistency of the estimated unconditional covariance matrix, the existence of the finite sixth-order moments is required for the convergence of the second-order derivatives of the quasi-log-likelihood function. This paper also shows the relationship between the asymptotic distributions of the 2sQML estimator for the RBEKK model and variance targeting quasi-maximum likelihood estimator for the VT-BEKK model. Monte Carlo experiments show that the bias of the 2sQML estimator is negligible and that the appropriateness of the diagonal specification depends on the closeness to either the diagonal BEKK or the diagonal RBEKK models. An empirical analysis of the returns of stocks listed on the Dow Jones Industrial Average indicates that the choice of the diagonal BEKK or diagonal RBEKK models changes over time, but most of the differences between the two forecasts are negligible.
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Kah, Fatoumata Baboucar Omar, and Abdou Kâ Diongue. "Modeling exchange rate volatility in the gambia using dynamic conditonal correlaton model." African Journal of Applied Statistics 7, no. 1 (January 1, 2020): 805–27. http://dx.doi.org/10.16929/ajas/2020.805.243.
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The relationship between different international stock markets is of importance for both financial practitioners and academicians in order to manage risks. Especially after the financial crisis, the pronounced financial contagion draws the public attention to look into such associations. However, measuring and modelling dependence structure becomes complicated when asset returns present non-linear, non-Gaussian and dynamic features. This paper examines the time-varying conditional correlations to the weekly exchange rate returns for the USD, EURO and GBP against the Gambian Dalasi (GMD) during the period 2000 to 2017. We use a dynamic conditional correlation (DCC) multivariate GARCH model. This model can be simplified by estimating univariate GARCH models for each return series, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. DCC-GARCH model was implemented for two different assumptions of the error distribution; assuming Gaussian and Student t-distribution. Empirical results show substantial evidence of significant increase in conditional correlation. It is also clean that, the Student t-distributed errors better forecast the conditional correlation.
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Tsuji, Chikashi. "A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan." International Journal of Business Administration 10, no. 3 (March 27, 2019): 39. http://dx.doi.org/10.5430/ijba.v10n3p39.
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This paper quantitatively inspects the effects of structural breaks in stock returns on their volatility persistence by using the stock return data of the US and Japan. More concretely, applying the diagonal BEKK-MGARCH model with and without structural break dummies to the returns of S&P 500 and TOPIX, we reveal the following interesting findings. (1) First, we clarify that for both the US and Japanese stock returns, the values of the GARCH parameters, namely, the values of the volatility persistence parameters in the diagonal BEKK-MGARCH models decrease when we include the structural break dummies in the models. (2) Second, we further find that interestingly, during the Lehman crisis in 2008, the estimated time-varying volatilities from the diagonal BEKK-MGARCH model with structural break dummies are slightly higher than those from the no structural break dummy model. (3) Third, we furthermore reveal that also very interestingly, the estimated time-varying correlations from the diagonal BEKK-MGARCH model with no structural break dummy are slightly higher than those from the structural break dummy model.
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Liston-Perez, Daniel, Patricio Torres-Palacio, and Sidika Gulfem Bayram. "Does investor sentiment predict Mexican equity returns?" International Journal of Managerial Finance 14, no. 4 (August 6, 2018): 484–502. http://dx.doi.org/10.1108/ijmf-05-2017-0088.
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Purpose The purpose of this paper is to test whether investor sentiment is a significant predictor of future Mexican stock market returns. It also estimates the dynamic correlation between investor sentiment and equity returns. Finally, it examines if investor sentiment innovations impact unexpected returns for a variety of portfolios. Design/methodology/approach This study utilizes predictive regressions to determine if sentiment can predict Mexican equity returns. Multivariate GARCH models are estimated to examine the time-varying correlations between investor sentiment and equity returns. Findings The results show that Mexican investor sentiment is a significant predictor of Mexican equity returns for up to 24 months ahead. The findings show that high levels of sentiment today are associated with lower equity returns over the near term. Furthermore, multivariate GARCH estimations indicate that the correlation between investor sentiment and equity returns is not static and varies considerably over time. Finally, the findings indicate that sentiment innovations are significantly correlated with unexpected returns, reinforcing the notion that unexplained sentiment fluctuations lead to unexplained changes in stock market returns. Overall, these results suggest that investor sentiment is a significant source of risk for the Mexican stock market. Originality/value This study seeks to further our understanding of how behavioral factors influence and predict Mexican equity returns.
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Sakti, Muhammad Rizky Prima. "Testing the conditional correlations and volatility spillovers between US and ASEAN Islamic stock markets: A Multivariate GARCH Analysis." Global Review of Islamic Economics and Business 2, no. 1 (May 5, 2015): 029. http://dx.doi.org/10.14421/grieb.2014.021-03.
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This study examines the conditional correlations and volatility spillovers between the US and ASEAN Islamic stock markets. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic indexes as it adopted stringent restriction to include companies in sharia list. By using a three multivariate GARCH models (BEKK, diagonal VECH, and CCC model), we find evidence of returns and volatility spillovers from the US to the ASEAN Islamic stock markets. However, as the estimated time-varying conditional correlations and volatilities indicate there is still a room for diversification benefits, particularly in the single markets. The Islamic MSCI of Thailand, Indonesia, and Singapore are less correlate to the US MSCI Islamic index. The implication is that foreign investors may benefit from the reduction of risk by adding the Islamic stocks in those countries.
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Do, Giam Quang, Nguyen Van Phuong, and Vu Thi Hai. "Estimating Conditional Correlations among Subgroups of Service Sector in Vietnam Stock Exchanges." IAR Journal of Business Management 3, no. 02 (April 10, 2022): 33–41. http://dx.doi.org/10.47310/iarjbm.2022.v03i02.006.
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The paper explores the time-varying correlations among five subgroups of the service sector i.e., CONSTRUCTION, ENERGY, TECHOLOGY, TRANSPORT and TOURISM, and market index ‘VNINDEX’ in the Vietnam stock exchanges. Two multivariate GARCH models namely the CCC and DCC were employed to examine how closely returns of each pair correlate with each other. The estimates of conditional correlations reveal low to relatively high [0.1958; 0.6720] between the selected indexes, of which the subgroup ‘TECHNOLOGY’ exhibits low correlations with the others, while the remaining subgroups show medium interdependence from each other and relatively high with the market index. Moreover, the estimates of the DCC model show the time-varying patterns in the pair correlation paths among the selected indexes. The findings provide a crucial background for investors, capital investment funds, listed firm owners and market managers in the Vietnam stock exchanges in fore-seeing long-run and short-run investment, portfolio selection and risk management in the selected indexes.
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Murty, Sarika, Vijay Victor, and Maria Fekete-Farkas. "Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis." Journal of Risk and Financial Management 15, no. 7 (July 21, 2022): 317. http://dx.doi.org/10.3390/jrfm15070317.
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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.
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KUMAR, K. KIRAN, and SHREYA BOSE. "HEDGING EFFECTIVENESS OF CROSS-LISTED NIFTY INDEX FUTURES." Global Economy Journal 19, no. 02 (June 2019): 1950011. http://dx.doi.org/10.1142/s2194565919500118.
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This paper investigates the hedging effectiveness of cross-listed Nifty Index futures and compares the performance of constant and dynamic optimal hedging strategies. We use daily data of Nifty index traded on the National Stock Exchange (NSE), India and cross-listed Nifty futures traded on the Singapore Stock Exchange (SGX) for a period of six years from July 15, 2010 to July 15, 2016. Various competing forms of Multivariate Generalised Autoregressive Conditional Heteroscedasticity (MGARCH) models, such as Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC), have been employed to capture the time-varying volatility. The results clearly depict that dynamic hedge ratios outperform traditional constant hedge ratios with the DCC–GARCH model being the most efficient with maximum variance reduction from the unhedged portfolio.
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Singh, Amit Kumar, Rajat Agarwal, and Rohit Kumar Shrivastav. "Returns and Volatility Spillover Between BSE SENSEX and BSE SME Stock Exchange of India." SEDME (Small Enterprises Development, Management & Extension Journal): A worldwide window on MSME Studies 48, no. 3 (September 2021): 257–71. http://dx.doi.org/10.1177/09708464211070054.
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Investigating the impact of volatility spillover among various markets has been the subject matter of numerous research. This study investigates the dynamic relationship between the Bombay Stock Exchange index (SENSEX) and the small and medium enterprises (SME) stock index (BSE SME) in India. The study uses univariate autoregressive conditional heteroskedasticity (ARCH)/generalised autoregressive conditional heteroskedasticity (GARCH) models to model the time-varying volatility of the BSE SME market and multivariate BEKK-GARCH analysis to model the volatility of the SENSEX and BSE SME Index considering the existence of some linkages between them. The study is based on the daily stock indices data ranging from 16 August 2012 to 31 March 2021. Furthermore, the study reveals statistically significant internal volatility spillovers in the SME stock market and the cross-volatility transmission between the two indices. It affirms statistically significant volatility and return spillover between the main market index, SENSEX and SME index, BSE SME. The findings of this research have important implications for the diversification process. It provides crucial signals to investors, portfolio managers and policymakers, especially when there has been much impetus and promotion from the Indian government and emerging foreign investments in India’s SMEs in recent years.
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Gaiduchevici, Gabriel. "A Method for Systemic Risk Estimation Based on CDS Indices." Review of Economic and Business Studies 8, no. 1 (June 1, 2015): 103–24. http://dx.doi.org/10.1515/rebs-2016-0018.
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AbstractThe copula-GARCH approach provides a flexible and versatile method for modeling multivariate time series. In this study we focus on describing the credit risk dependence pattern between real and financial sectors as it is described by two representative iTraxx indices. Multi-stage estimation is used for parametric ARMA-GARCH-copula models. We derive critical values for the parameter estimates using asymptotic, bootstrap and copula sampling methods. The results obtained indicate a positive symmetric dependence structure with statistically significant tail dependence coefficients. Goodness-of-Fit tests indicate which model provides the best fit to data.
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Moran, John L., and Patricia J. Solomon. "Volatility in High-Frequency Intensive Care Mortality Time Series: Application of Univariate and Multivariate GARCH Models." Open Journal of Applied Sciences 07, no. 08 (2017): 385–411. http://dx.doi.org/10.4236/ojapps.2017.78030.
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Ghorbel, Achraf, and Ahmed Jeribi. "Contagion of COVID-19 pandemic between oil and financial assets: the evidence of multivariate Markov switching GARCH models." Journal of Investment Compliance 22, no. 2 (May 20, 2021): 151–69. http://dx.doi.org/10.1108/joic-01-2021-0001.
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Purpose In this paper, we investigate empirically the time-frequency co-movement between the recent COVID-19 pandemic, G7stock markets, gold, crude oil price (WTI) and cryptocurrency markets (bitcoin) using both the multivariate MSGARCH models. Design/methodology/approach This paper examines the relationship between the volatilities of oil, Chinese stock index and financial assets (cryptocurrency, gold, and G7 stock indexes), for the period January 17th 2020 to December 10th 2020. It tests the presence of regime changes in the GARCH volatility dynamics of bitcoin, gold, Chinese, and G7 stock indexes as well as oil prices by using Markov–Switching GARCH model. Also, the paper estimates the dynamic correlation and volatility spillover between oil, Chinese and financial assets by using the MSBEKK-GARCH and MSDCC-GARCH models. Findings Overall, we find that all variables display a strong volatility concentrated in the first four months of Covid-19 outbreak. The paper conducts different backtesting procedures of the 1% and 5% Value-at-Risk forecasts of risk. The results find that gold has the lowest VaR. However, the Canadian and American indices have the highest VaR, for respectively 1% and 5% confidence level. The estimation results of MSBEKK-GARCH prove the volatility spillover between Chinese index, oil and financial assets. Although, the past news about shocks in the Chinese index significantly affects the current conditional volatility of financial assets. Moreover, for the high regime, the correlation increased between Chinese and G7 stock indexes which proving the contagion effect of the COVID-19 pandemic. On the contrary, the correlation decreased between Chinese-gold and Chinese-bitcoin, which confirming that gold and bitcoin can be considered as an alternative hedge for some investors during a crisis. During the COVID-19 pandemic, the correlations for the couples oil-gold and oil-bitcoin peaked. Contrary to gold, bitcoin cannot be considered as a safe haven during the global pandemic when investing in crude oil. Originality/value In contrast, comparative analysis in terms of responses to US COVID-19 pandemic, the US Covid-19 confirmed cases have relative higher impact on the co-movement in WTI and bitcoin. This paper confirms that gold is a safe haven during the COVID19 pandemic period.
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Yeshiwas, Dawit, and Yebelay Berelie. "Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data." Journal of Probability and Statistics 2020 (April 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/1424020.
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Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.
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Sung, Sang-Ha, Jong-Min Kim, Byung-Kwon Park, and Sangjin Kim. "A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model." Axioms 11, no. 9 (September 1, 2022): 448. http://dx.doi.org/10.3390/axioms11090448.
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Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the selection of the most influential major features for each cryptocurrency using the volatility features of cryptocurrency, derived from the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models, along with the closing price of the cryptocurrency. In addition, we sought to predict the log-return price of cryptocurrencies by implementing various types of time-series model. Based on the selected major features, the log-return price of cryptocurrency was predicted through the autoregressive integrated moving average (ARIMA) time-series prediction model and the artificial neural network-based time-series prediction model. As a result of log-return price prediction, the neural-network-based time-series prediction models showed superior predictive power compared to the traditional time-series prediction model.
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Lieberman, Offer. "ASYMPTOTIC THEORY OF STATISTICAL INFERENCE FOR TIME SERIES." Econometric Theory 18, no. 4 (May 17, 2002): 993–99. http://dx.doi.org/10.1017/s0266466602004103.
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Modern time series econometrics involves a diversity of models. In addition to the more traditional vector autoregressive (VAR) and autoregressive moving average (ARMA) systems, cointegration and unit root models are in widespread use for macroeconomic data, nonlinear and non-Gaussian models are popular for financial data, and long memory models are becoming more common in both macroeconomic and financial applications. Much econometric thought relates to issues of estimation and hypothesis testing, and so, in the absence of a usable finite sample theory (as is the case for the models just mentioned), an enormous amount of effort has been given to developing adequate asymptotics for statistical inference. There is often a lag between the introduction of a new model and the development of an asymptotic theory. In consequence, applied econometricians sometimes have to estimate time series models for which no asymptotic theory is available. For instance, multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models have been in use in empirical research for a while, and practitioners have been using asymptotic normality of estimators in this model even though a theoretical justification is not available.
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Abbas, Ghulam, and Shouyang Wang. "Does macroeconomic uncertainty really matter in predicting stock market behavior? A comparative study on China and USA." China Finance Review International 10, no. 4 (May 18, 2020): 393–427. http://dx.doi.org/10.1108/cfri-06-2019-0077.
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PurposeThe study aims to analyze the interaction between macroeconomic uncertainty and stock market return and volatility for China and USA and tries to draw some invaluable inferences for the investors, portfolio managers and policy analysts.Design/methodology/approachEmpirically the study uses GARCH family models to capture the time-varying volatility of stock market and macroeconomic risk factors by using monthly data ranging from 1995:M7 to 2018:M6. Then, these volatility series are further used in the multivariate VAR model to analyze the feedback interaction between stock market and macroeconomic risk factors for China and USA. The study also incorporates the impact of Asian financial crisis of 1997–1998 and the global financial crisis of 2007–2008 by using dummy variables in the GARCH model analysis.FindingsThe empirical results of GARCH models indicate volatility persistence in the stock markets and the macroeconomic variables of both countries. The study finds relatively weak and inconsistent unidirectional causality for China mainly running from the stock market to the macroeconomic variables; however, the volatility spillover transmission reciprocates when the impact of Asian financial crisis and Global financial crisis is incorporated. For USA, the contemporaneous relationship between stock market and macroeconomic risk factors is quite strong and bidirectional both at first and second moment level.Originality/valueThis study investigates the interaction between stock market and macroeconomic uncertainty for China and USA. The researchers believe that none of the prior studies has made such rigorous comparison of two of the big and diverse economies (China and USA) which are quite contrasting in terms of political, economic and social background. Therefore, this study also tries to test the presumed conception that macroeconomic uncertainty in China may have different impact on the stock market return and volatility than in USA.
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Matilainen, Markus, Jari Miettinen, Klaus Nordhausen, Hannu Oja, and Sara Taskinen. "On Independent Component Analysis with Stochastic Volatility Models." Austrian Journal of Statistics 46, no. 3-4 (April 12, 2017): 57–66. http://dx.doi.org/10.17713/ajs.v46i3-4.671.
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Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to capture nonlinear autocorrelation of the time series and extract the independent components. Simulation study shows that the proposed method outperforms the existing methods when latent components follow GARCH and SV models. This paper is an invited extended version of the paper presented at the CDAM 2016 conference.
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Perez Liston, Daniel. "Internet gambling stock returns: empirical evidence from the UK." International Journal of Managerial Finance 13, no. 1 (February 6, 2017): 36–49. http://dx.doi.org/10.1108/ijmf-10-2015-0176.
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Purpose The purpose of this paper is to quantify beta for an online gambling portfolio in the UK and investigates whether it is time-varying. It also examines the dynamic correlations of the online gambling portfolio with both the market and socially responsible portfolios. In addition, this paper documents the effect of important UK gambling legislation on the betas and correlations of the online gambling portfolio. Design/methodology/approach This study uses static and time-varying models (e.g. rolling regressions, multivariate GARCH models) to estimate betas and correlations for a portfolio of UK online gambling stocks. Findings This study finds that beta for the online gambling portfolio is less than 1, indicative of defensiveness toward the market, a result that is consistent with prior literature for sin stocks. In addition, the conditional correlation between the market and online gambling portfolio is small when compared to the correlation of the market and socially responsible portfolios. Findings suggest that the adoption of the Gambling Act 2005 increases the conditional correlation between the market and online gambling portfolio and it also increases the conditional betas for the online gambling portfolio. Research limitations/implications This paper serves as a starting point for future research on online gambling stocks. Going forward, studies can focus on the financial performance or accounting performance of online gambling stocks. Originality/value This empirical investigation provides insight into the risk characteristics of publicly listed online gambling companies in the UK.
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USMAN, Mustofa, N. INDRYANI, WARSONO A., and AMANTO WAMILIANA. "DYNAMIC MODELING OF TIME SERIES DATA USING BEKK-GARCH MODEL." Periódico Tchê Química 17, no. 36 (December 20, 2020): 1186–98. http://dx.doi.org/10.52571/ptq.v17.n36.2020.1202_periodico36_pgs_1186_1198.pdf.
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The Vector Autoregressive Moving Average (VARMA) model is one of the models that is often used in modeling multivariate time series data. In time-series data of economics, especially data return, they usually have high fluctuations in some periods, so the return volatility is unstable. In modeling data return of share prices ADRO and ITMG, the behavior of high volatility will be considered. This study aims to find the best model that fits the data return of share price of the energy companies of PT Adaro Energy Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG), to analyze the behavior of impulse response of the variables data return ADRO and ITMG, to analyze the granger causality test, and to forecast the next 12 periods. Based on the selection of the best model using the criteria of AICC, HQC, AIC, and SBC, it was found that the VARMA (2.2) -GARCH (1.1) model is the best one for the data in this study. The model VARMA(2,2)-GARCH (1,1) is then written as a univariate model. For the univariate ADRO model, the test statistics F = 4,73 and P-value = 0,0084, which indicates the model is very significant; and for the univariate ITMG model, the test statistics is F = 5,82 and P-value 0,0001, which indicates the model is significant. Based on the best model selected, the impulse response, Granger causality test, and forecasting for the next 12 periods are discussed.
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McAleer, Michael, Felix Chan, Suhejla Hoti, and Offer Lieberman. "GENERALIZED AUTOREGRESSIVE CONDITIONAL CORRELATION." Econometric Theory 24, no. 6 (July 9, 2008): 1554–83. http://dx.doi.org/10.1017/s0266466608080614.
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This paper develops a generalized autoregressive conditional correlation (GARCC) model when the standardized residuals follow a random coefficient vector autoregressive process. As a multivariate generalization of the Tsay (1987, Journal of the American Statistical Association 82, 590–604) random coefficient autoregressive (RCA) model, the GARCC model provides a motivation for the conditional correlations to be time varying. GARCC is also more general than the Engle (2002, Journal of Business & Economic Statistics 20, 339–350) dynamic conditional correlation (DCC) and the Tse and Tsui (2002, Journal of Business & Economic Statistics 20, 351–362) varying conditional correlation (VCC) models and does not impose unduly restrictive conditions on the parameters of the DCC model. The structural properties of the GARCC model, specifically, the analytical forms of the regularity conditions, are derived, and the asymptotic theory is established. The Baba, Engle, Kraft, and Kroner (BEKK) model of Engle and Kroner (1995, Econometric Theory 11, 122–150) is demonstrated to be a special case of a multivariate RCA process. A likelihood ratio test is proposed for several special cases of GARCC. The empirical usefulness of GARCC and the practicality of the likelihood ratio test are demonstrated for the daily returns of the Standard and Poor's 500, Nikkei, and Hang Seng indexes.
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Benavides, Guillermo. "PREDICTIVE ACCURACY OF FUTURES OPTIONS IMPLIED VOLATILITY: THE CASE OF THE EXCHANGE RATE FUTURES MEXICAN PESO-US DOLLAR." PANORAMA ECONÓMICO 5, no. 9 (April 26, 2017): 41. http://dx.doi.org/10.29201/pe-ipn.v5i9.83.
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There has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.
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Just, Małgorzata, and Aleksandra Łuczak. "Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods." Sustainability 12, no. 6 (March 24, 2020): 2571. http://dx.doi.org/10.3390/su12062571.
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The dynamic development of commodity derivatives markets has been observed since the mid-2000s. It is related to the development of e-commerce, the inflow of financial investors’ capital, and the emergence of exchange-traded funds and passively managed index funds focused on commodities. These advances are accompanied by changes in dependence structure in the markets. The main purpose of this study is to assess the conditional dependence structure in various commodity futures markets (energy, metals, grains and oilseeds, soft commodities, agricultural commodities) in the period from the beginning of 2000 to the end of 2018. The specific purpose is to identify the states of the market corresponding to typical patterns of the conditional dependency structure, and to determine the time of transition from one state to another. The copula-based Multivariate Generalized Autoregressive Conditional Heteroskedasticity models were used to describe the dynamics of dependencies between the rates of return on prices of commodity futures, while the dynamic Kendall’s tau correlation coefficients were applied to measure the strength of dependencies. The daily changes in the conditional dependence structure in the markets (changes in states of the markets) were identified with the fuzzy c-means clustering method. In 2000–2018, the conditional dependence structure in commodity futures markets was not stable, as evidenced by the different states of markets identified (two states in the grains and oilseeds market, the agricultural market, the soft commodities market and the metals market, and three states in the energy market).
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Costa, Hudson Chaves, Sabino Da Silva Porto Junior, and Gabrielito Menezes. "Um Estudo Empírico da Dinâmica da Correlação do Retorno das Ações do Brasil." Brazilian Review of Finance 16, no. 4 (January 18, 2019): 635. http://dx.doi.org/10.12660/rbfin.v16n4.2018.72142.
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This article examines empirically the behavior of the correlation between the return of shares listed on the BMF& BOVESPA over the period from 2000 to 2015. To this end, we use multivariate GARCH models introduced by Bollerslev (1990) to remove the temporal series of arrays of conditional correlation of returns of stocks. With the temporal series of the largest eigenvalues of matrices of correlation estimated conditional, we apply statistical tests (unit root, structural breaks and trend) to verify the existence of stochastic trend or deterministic to the intensity of the correlation between the returns of the shares represented by eigenvalues. Our results confirm that both in times of crises at national and international turbulence, there is greater correlation between the actions. However, we did not find any long-term trend in time series of the largest eigenvalues of matrices of correlation conditional.
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Aiube, Fernando Antonio Lucena, and Winicius Botelho Faquieri. "Hedging the Brazilian stock index in the era of low interest rates: What has changed?" Brazilian Review of Finance 18, no. 3 (September 5, 2020): 5. http://dx.doi.org/10.12660/rbfin.v18n3.2020.81625.
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<p>In this paper we analyze the ability of different asset classes to hedge the Brazilian stock index in periods of high and low interest rates in the Brazilian economy, using two multivariate GARCH models. Our analysis includes two categories of assets: those traded in domestic currency and those traded in U.S. dollars. From the perspective of a local investor, we find that the exchange rate (R$/US$) and gold are the assets least correlated with equities. From the standpoint of a foreign investor, commodity index and fixed-income assets are the most useful. These results prevail in the low- and high-interest-rate periods. Moreover, in the period of low interest rates, the standard deviation of the estimated conditional correlation time series decreases, suggesting that in this period investors are more confident about macroeconomic policies.</p>
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Gu, Xiaomeng, Andrew Metcalfe, Nigel Cook, Chris Aldrich, and L. George. "Exploratory analysis of multivariate drill core time series measurements." ANZIAM Journal 63 (January 10, 2023): C208—C230. http://dx.doi.org/10.21914/anziamj.v63.17192.
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Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase in the cost, complexity and environmental footprint associated with mining and mineral processing. Enhanced knowledge of orebody characteristics is thus vital for mining companies to optimize profitability. We present a pilot study to investigate prediction of geometallurgical variables from drill sensor data. A comparison is made of the performance of multilayer perceptron (MLP) and multiple linear regression models (MLR) for predicting a geometallurgical variable. This comparison is based on simulated data that are physically realistic, having been derived from models fitted to the one available drill core. The comparison is made in terms of the mean and standard deviation (over repeated samples from the population) of the mean absolute error, root mean square error, and coefficient of determination. The best performing model depends on the form of the response variable and the sample size. The standard deviation of performance measures tends to be higher for the MLP, and MLR appears to offer a more consistent performance for the test cases considered. References R. M. Balabin and S. V. Smirnov. Interpolation and extrapolation problems of multivariate regression in analytical chemistry: Benchmarking the robustness on near-infrared (NIR) spectroscopy data”. Analyst 137.7 (2012), pp. 1604–1610. doi: 10.1039/c2an15972d C. M. Bishop. Pattern recognition and machine learning. Springer, 2006. url: https://link.springer.com/book/9780387310732 J. B. Boisvert, M. E. Rossi, K. Ehrig, and C. V. Deutsch. Geometallurgical modeling at Olympic dam mine, South Australia”. Math. Geosci. 45 (2013), pp. 901–925. doi: 10.1007/s11004-013-9462-5 T. Bollerslev. Generalized autoregressive conditional heteroskedasticity”. J. Economet. 31.3 (1986), pp. 307–327. doi: 10.1016/0304-4076(86)90063-1 C. Both and R. Dimitrakopoulos. Applied machine learning for geometallurgical throughput prediction—A case study using production data at the Tropicana Gold Mining Complex”. Minerals 11.11 (2021), p. 1257. doi: 10.3390/min11111257 J. Chen and G. Li. Tsallis wavelet entropy and its application in power signal analysis”. Entropy 16.6 (2014), pp. 3009–3025. doi: 10.3390/e16063009 S. Coward, J. Vann, S. Dunham, and M. Stewart. The primary-response framework for geometallurgical variables”. Seventh international mining geology conference. 2009, pp. 109–113. https://www.ausimm.com/publications/conference->url: https://www.ausimm.com/publications/conference- proceedings/seventh-international-mining-geology- conference-2009/the-primary-response-framework-for- geometallurgical-variables/ A. C. Davis and N. B. Christensen. Derivative analysis for layer selection of geophysical borehole logs”. Comput. Geosci. 60 (2013), pp. 34–40. doi: 10.1016/j.cageo.2013.06.015 C. Dritsaki. An empirical evaluation in GARCH volatility modeling: Evidence from the Stockholm stock exchange”. J. Math. Fin. 7.2 (2017), pp. 366–390. doi: 10.4236/jmf.2017.72020 R. F. Engle and T. Bollerslev. Modelling the persistence of conditional variances”. Econ. Rev. 5.1 (1986), pp. 1–50. doi: 10.1080/07474938608800095 A. S. Hadi and R. F. Ling. Some cautionary notes on the use of principal components regression”. Am. Statistician 52.4 (1998), pp. 15–19. doi: 10.2307/2685559 J. Hunt, T. Kojovic, and R. Berry. Estimating comminution indices from ore mineralogy, chemistry and drill core logging”. The Second AusIMM International Geometallurgy Conference (GeoMet) 2013. 2013, pp. 173–176. http://ecite.utas.edu.au/89773>url: http://ecite.utas.edu.au/89773 on p. C210). R. Hyndman, Y. Kang, P. Montero-Manso, T. Talagala, E. Wang, Y. Yang, M. O’Hara-Wild, S. Ben Taieb, H. Cao, D. K. Lake, N. Laptev, and J. R. Moorman. tsfeatures: Time series feature extraction. R package version 1.0.2. 2020. https://CRAN.R-project.org/package=tsfeatures>url: https://CRAN.R-project.org/package=tsfeatures on p. C222). C. L. Johnson, D. A. Browning, and N. E. Pendock. Hyperspectral imaging applications to geometallurgy: Utilizing blast hole mineralogy to predict Au-Cu recovery and throughput at the Phoenix mine, Nevada”. Econ. Geol. 114.8 (2019), pp. 1481–1494. doi: 10.5382/econgeo.4684 E. B. Martin and A. J. Morris. An overview of multivariate statistical process control in continuous and batch process performance monitoring”. Trans. Inst. Meas. Control 18.1 (1996), pp. 51–60. doi: 10.1177/014233129601800107 E. Sepulveda, P. A. Dowd, C. Xu, and E. Addo. Multivariate modelling of geometallurgical variables by projection pursuit”. Math. Geosci. 49.1 (2017), pp. 121–143. doi: 10.1007/s11004-016-9660-z S. J. Webb, G. R. J. Cooper, and L. D. Ashwal. Wavelet and statistical investigation of density and susceptibility data from the Bellevue drill core and Moordkopje borehole, Bushveld Complex, South Africa”. SEG Technical Program Expanded Abstracts 2008. Society of Exploration Geophysicists, 2008, pp. 1167–1171. doi: 10.1190/1.3059129 R. Zuo. Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China)”. J. Geochem. Explor. 111.1-2 (2011), pp. 13–22. doi: 10.1016/J.GEXPLO.2011.06.012
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Kharbanda, Varuna, and Archana Singh. "Futures market efficiency and effectiveness of hedge in Indian currency market." International Journal of Emerging Markets 13, no. 6 (November 29, 2018): 2001–27. http://dx.doi.org/10.1108/ijoem-08-2017-0320.
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Purpose Corporate treasurers manage the currency risk of their organization by hedging through futures contracts. The purpose of this paper is to evaluate the effectiveness of hedging by US currency futures contracts by taking into account the efficiency of the currency market. Design/methodology/approach The static models for calculating hedge ratio are as popular as dynamic models. But the main disadvantage with the static models is that they do not consider important properties of time series like autocorrelation and heteroskedasticity of the residuals and also ignore the cointegration of the market variables which indicate short-run market disequilibrium. The present study, therefore, measures the hedging effectiveness in the US currency futures market using two dynamic models – constant conditional correlation multivariate generalized ARCH (CCC-MGARCH) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH). Findings The study finds that both the dynamic models used in the study provide similar results. The relative comparison of CCC-MGARCH and DCC-MGARCH models shows that CCC-MGARCH provides better hedging effectiveness result, and thus, should be preferred over the other model. Practical implications The findings of the study are important for the company treasurers since the new updated Indian accounting standards (Ind-AS), applicable from the financial year 2016–2017, make it mandatory for the companies to evaluate the effectiveness of hedges. These standards do not specify a quantitative method of evaluation but provide the flexibility to the companies in choosing an appropriate method which justifies their risk management objective. These results are also useful for the policy makers as they can specify and list the appropriate methods for evaluating the hedge effectiveness in the currency market. Originality/value Majorly, the studies on Indian financial market limit themselves to either examining the efficiency of that market or to evaluate the effectiveness of the hedges undertaken. Moreover, most of such works focus on the stock market or the commodity market in India. This is one of the first studies which bring together the concepts of efficiency of the market and effectiveness of the hedges in the Indian currency futures market.
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Hung, Ngo Thai. "Bitcoin and CEE stock markets: fresh evidence from using the DECO-GARCH model and quantile on quantile regression." European Journal of Management and Business Economics 30, no. 2 (May 18, 2021): 261–80. http://dx.doi.org/10.1108/ejmbe-06-2020-0169.
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PurposeThis study examines the inter-linkages between Bitcoin prices and CEE stock markets (Hungary, the Czech Republic, Poland, Romania and Croatia).Design/methodology/approachThe dynamic contemporaneous nexus has been analyzed using both the multivariate DECO-GARCH model proposed by Engle and Kelly (2012) and quantile on quantile (QQ) methodology proposed by Sim and Zhou (2015). Our study is implemented using the daily data spanning from 6 September 2012 to 12 August 2019.FindingsFirst, the findings show that the average return equicorrelation across Bitcoin prices and CEE stock indices are positive, even though it is found to be time-varying over the research period shown. Second, the Bitcoin-CEE stock market association has positive signs for most pairs of quantiles of both variables and represents a rather similar pattern for the cases of Poland, the Czech Republic and Croatia. However, a weaker and primarily negative connectedness is found for Hungary and Romania, respectively. Furthermore, the interconnectedness between the co-movements in the Bitcoin market and stock returns changes significantly across quantiles of both variables within each nation, indicating that the Bitcoin-stock market relationship is dependent on both the cycle of the stock market and the nature of Bitcoin price shocks.Practical implicationsThe evidence documented in this study has significant implications for divergent economic agents, including global investors, risk managers and policymakers, who would benefit from a comprehensive knowledge of the Bitcoin-stock market relationship to build efficient risk-hedging models and to conduct appropriate policy reactions to information spillover effects in different time horizons.Originality/valueThis paper is the first study employing both the multivariate DECO-GARCH model and QQ methodology to shed light on the nexus between Bitcoin prices and the stock markets in CEE countries. The DECO model uses more information to compute dynamic correlations between each pair of returns than standard dynamic conditional correlation (DCC) models, declining the estimation noise of the correlations. Besides, QQ approach allows us to capture some nuanced features of the Bitcoin-stock market relationship and explore the interdependence in its entirely. Therefore, the main contribution of this article to the related literature in this field is significant.研究目的本研究旨在探討比特幣的價格與中東歐股市(匈牙利、捷克共和國、波蘭、羅馬尼亞和克羅地亞) 之相互聯繫.研究設計/方法/理念研究使用恩格爾與凱利(2012)(Engle and Kelly (2012)) 提出的多變量DECO-GARCH模型及Sim 與Zhou(2015)(Sim and Zhou ( 2015)) 研製的分位數-分位數方法來分析動態同期的聯繫。我們的研究使用由2012年9月6日至2019年8月12日期間取得的每日數據來進行.研究結果首先、研究結果顯示、跨比特幣價格與中東歐股價指數的平均回報當量關聯是正相關的，即使在研究期間被發現是隨時間而變化的。第二、比特幣與中東歐股市之聯繫在大多數兩變數分位數對而言出現正相關跡象，而且，這聯繫在波蘭、捷克共和國及克羅地亞而言表現一個頗相似的模式。唯就匈牙利而言、這聯繫則較弱、而羅馬尼亞則主要是負聯繫。研究結果亦顯示: 比特幣市場內的聯動與股票回報間之內在關聯會在每個國家內跨兩個變數的分位數而顯著地改變，這顯示比特幣-股市關係是取決於股市的週期和比特幣價格衝擊的本質.實際的意義本研究所記載的證據、對不同的經濟行為者而言極具意義 (這包括國際投資者、風險管理經理和政策制定者)，因他們會受惠於對比特幣-股市關係的全面認識，他們可建立有效的風險對沖模型、及在不同時間範圍對資訊溢出效應進行適當的政策反應.研究的原創性/價值本文為首個研究使用多變量DECO-GARCH模型和分位數-分位數(QQ)方法、來解釋比特幣價格與中東歐國家之股市的關係。這DECO模型使用比標準動態條件關係模型更多資訊，來計算每對回報間之動態關係，這能減少估測雜訊，而且，QQ方法讓我們可以取得比特幣-股市關係的一些細微特徵及全面地探索其相互依賴性。因此，本文的主要貢獻是在這學術領域內有關的文獻上.
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Nouman, Muhammad, Maria Hashim, Vanina Adoriana Trifan, Adina Eleonora Spinu, Muhammad Fahad Siddiqi, and Farman Ullah Khan. "Interest rate volatility and financing of Islamic banks." PLOS ONE 17, no. 7 (July 26, 2022): e0268906. http://dx.doi.org/10.1371/journal.pone.0268906.
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Despite a direct ban on charging interest, interest-based benchmarks are used as a pricing reference by a majority of Islamic banks, due in part to the absence of stable and widely- published alternatives. Benchmarking interest rate exposes Islamic banks to the problems of conventional banks, particularly the interest rate risk. Against this backdrop, the present study empirically examines the dynamic linkage between the interest rate volatility and the financing of Islamic banks. The empirical analysis is carried using evidence from the Islamic banking industry of Pakistan during the time period 2006–2020. The multivariate Johansen and Jusiles Co-integration test and Vector Error Correction Model (VECM) are used as the baseline econometric models. Moreover, the DCC-GARCH model is employed for robustness and ensuring the consistency of results. The results indicate that a significant long-term and short-term relationship exists between the interest rate volatility and the financing of Islamic banking industry providing significant evidence for co-movements and convergence. These findings suggest that paradoxical as it may seem, the financing of Islamic banks operating within a dual banking system is subject to interest rate risk, mainly due to benchmarking interest rate, which in-turn makes Islamic banks vulnerable to the rate of return risk and withdrawal risk. Moreover, corporate financing, in particular, is more vulnerable to interest rate risk.
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Aktan, Bora, Renata Korsakienė, and Rasa Smaliukienė. "TIME‐VARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS." Journal of Business Economics and Management 11, no. 3 (September 30, 2010): 511–32. http://dx.doi.org/10.3846/jbem.2010.25.
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As time‐varying volatility has found applications in roughly all time series modelling in economics, it largely draws attention in the areas of financial markets. This study also examines the characteristics of conditional volatility in the Baltic Stock Markets (Estonia, Latvia and Lithuania) by using a broad range of GARCH volatility models. Correctly forecasting the volatility leads to better understanding and managing financial market risk. Daily returns from four Baltic stock indexes are used; Estonia (TALSE index), Latvia (RIGSE index), Lithuania (VILSE index) and synthetic BALTIC benchmark index. We test a large family of GARCH models, including; the basic GARCH model, GARCH‐in‐mean model, asymmetric exponential GARCH and GJR GARCH, power GARCH and component GARCH model. We find strong evidence that daily returns from Baltic Stock Markets can be successfully modelled by GARCH‐type models. For all Baltic markets, we conclude that increased risk will not necessarily lead to a rise in the returns. All of the analysed indexes exhibit complex time series characteristics involving asymmetry, long tails and complex autoregression in the returns. Results from this study are firmly recommended to financial officers and international investors. Santrauka Straipsnyje analizuojamas salyginis Baltijos vertybiniu popieriu rinku (Estijos, Latvijos ir Lietuvos) nepastovumas, taikant eile GARCH kintamumo modeliu. Pažymetina, kad tinkamai prognozuojant nepastovuma, galima geriau suvokti ir valdyti finansiniu rinku rizika. Straipsnyje remiamasi keturiu Baltijos šaliu kasdienemis akciju indeksu gražomis; Estijos (TALSE indeksu), Latvijos (RIGSE indeksu), Lietuvos (VILSE indeksu) ir sintetiniu palyginamuoju BALTIC indeksu. Pritaikius eile GARCH kintamumo modeliu, galima teigti, kad didejanti rizika Baltijos šaliu rinkose nebūtinai itakos vertybiniu popieriu gražos augima. Tyrimo metu gauti rezultatai rekomenduojami finansu specialistams ir investuotojams.
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Narayan, Seema. "The Influence of Domestic and Foreign Shocks on Portfolio Diversification Gains and the Associated Risks." Journal of Risk and Financial Management 12, no. 4 (October 10, 2019): 160. http://dx.doi.org/10.3390/jrfm12040160.
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This paper evaluates the influence of foreign or domestic stock market return and return of volatility shocks on dynamic conditional correlations (DCCs) between international stock markets and correlation volatility, respectively. The correlations between markets have implications for the gains from portfolio diversification, while correlation volatilities can be seen as risks to portfolio diversification. Meanwhile, domestic shocks are sourced from the return and return volatility from 24 developed, emerging, and frontier stock markets. The US stock market is the source of foreign shocks. The domestic and foreign shocks are derived using market-based returns and under bearish market conditions. We estimate multivariate exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) models using daily and monthly MSCI based stock price data of selected developed, emerging, and frontier markets over 1993:1–2014:1. Our key results are as follows. Domestic market shocks were significant drivers of gains from portfolio diversification most of the time, although the US market effects were relatively stronger. On the other hand, the US, in terms of the number of significant cases as well as the size effects of shocks, dominated as a determinant of correlation volatility (or risks to portfolio diversification). Further, under bear market conditions, adjustments in correlations and correlation volatilities are found to be mostly US-induced. Bearish shocks, rather than market return based shocks, show strong evidence of the leverage effect. Signs of persistence of shocks are mainly noticed under bearish conditions.
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Haas, Markus, Jochen Krause, Marc S. Paolella, and Sven C. Steude. "Time-varying mixture GARCH models and asymmetric volatility." North American Journal of Economics and Finance 26 (December 2013): 602–23. http://dx.doi.org/10.1016/j.najef.2013.02.024.
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Bibi, Abdelouahab, and Ahmed Ghezal. "QMLE of periodic time-varying bilinear– GARCH models." Communications in Statistics - Theory and Methods 48, no. 13 (November 22, 2018): 3291–310. http://dx.doi.org/10.1080/03610926.2018.1476703.
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Chen, Bin, and Yongmiao Hong. "DETECTING FOR SMOOTH STRUCTURAL CHANGES IN GARCH MODELS." Econometric Theory 32, no. 3 (April 8, 2015): 740–91. http://dx.doi.org/10.1017/s0266466614000942.
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Detecting and modeling structural changes in GARCH processes have attracted increasing attention in time series econometrics. In this paper, we propose a new approach to testing structural changes in GARCH models. The idea is to compare the log likelihood of a time-varying parameter GARCH model with that of a constant parameter GARCH model, where the time-varying GARCH parameters are estimated by a local quasi-maximum likelihood estimator (QMLE) and the constant GARCH parameters are estimated by a standard QMLE. The test does not require any prior information about the alternatives of structural changes. It has an asymptotic N(0,1) distribution under the null hypothesis of parameter constancy and is consistent against a vast class of smooth structural changes as well as abrupt structural breaks with possibly unknown break points. A consistent parametric bootstrap is employed to provide a reliable inference in finite samples and a simulation study highlights the merits of our test.
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Mehrara, Mohsen, and Monire Hamldar. "Time-Varying Optimal Hedge Ratio for Brent Oil Market." International Letters of Social and Humanistic Sciences 56 (July 2015): 103–6. http://dx.doi.org/10.18052/www.scipress.com/ilshs.56.103.
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This paper examines the optimal hedging ratio (OHR) for the Brent Crude Oil Futures using daily data over the period 1990/17/8-2014/11/3. To estimate OHR, we employ multivariate BEKK MV-GARCH model. At last, the efficiency of this approach are compared with the constant OHR captured from OLS through Edrington's index.
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Lee, Sangyeol, Chang Kyeom Kim, and Sangjo Lee. "Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression." Entropy 22, no. 5 (May 20, 2020): 578. http://dx.doi.org/10.3390/e22050578.
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This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we obtain the residuals, as well as the estimates of the conditional volatility and employ these to construct the residual CUSUM of squares test. We conduct Monte Carlo simulation experiments to illustrate its validity with various linear and nonlinear GARCH models. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and Korean won/U.S. dollar (KRW/USD) exchange rate datasets is provided to exhibit its scope of application.
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Rana, Surya Bahadur. "Dynamics of Time Varying Volatility in Stock Returns: Evidence from Nepal Stock Exchange." Journal of Business and Social Sciences Research 5, no. 1 (July 21, 2020): 15–34. http://dx.doi.org/10.3126/jbssr.v5i1.30196.
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This study examines the properties of time varying volatility of daily stock returns in Nepal over the period 2011-2020 using 2059 observations on daily returns of NEPSE index series. The study examines various symmetric and asymmetric GARCH family models using several specifications of error distribution. The results of symmetric GARCH (1,1) and GARCH-M (1, 1) models indicate that there is volatility persistence in daily returns on composite NEPSE index series over the sampled period. However, the estimated results for GARCH-M (1, 1) models show that the stock returns in Nepal offer no significant risk premium to hedge against risk associated with investment in stocks. The study also demonstrates that asymmetric TGARCH (1, 1) and EGARCH (1, 1) models fail to capture the leverage effects on the volatility. Finally, study results show that GARCH (1, 1) with student’s t error distribution model is the best fitted one to capture the volatility persistence of daily returns on NEPSE index series over the sampled period. The findings from this study offers an additional insight in understanding the volatility pattern of daily stock returns in Nepal for the most recent period that helps investors in forming a sound strategy to address the risk pattern of investing in stock market of Nepal.
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Chen, Cathy W. S., Richard Gerlach, and Edward M. H. Lin. "Bayesian estimation of smoothly mixing time-varying parameter GARCH models." Computational Statistics & Data Analysis 76 (August 2014): 194–209. http://dx.doi.org/10.1016/j.csda.2013.09.019.
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Darolles, Serge, Christian Francq, and Sébastien Laurent. "Asymptotics of Cholesky GARCH models and time-varying conditional betas." Journal of Econometrics 204, no. 2 (June 2018): 223–47. http://dx.doi.org/10.1016/j.jeconom.2018.02.003.
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