Constant Conditional Correlation GARCH Research Articles

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.

Volatility Transmission: Evidence from U.K. REIT & Stock Market Implied Volatility

This paper investigates volatility transmission in the U.K. REIT market. It considers how REIT volatility is related to implied volatility in both the overall stock market as well as that derived from traded options on REIT stocks. The multivariate analysis utilizes both Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC) GARCH specifications to analyse the interdependence of the data. The findings confirm the presence of volatility transmission across the implied volatility of U.K. REITs, the U.K. implied volatility index, and the U.K. REIT index. The study also applies the variance decomposition approach proposed by Diebold and Yilmaz to examine spillover effects.

Empirical Analysis of Exchange Rate Volatility in Emerging African Economies

Purpose: The study examined the empirical nature of the exchange rates volatilities in a few selected African countries, viz. Botswana, Egypt, Morocco, and South Africa. Method: The study used quarterly data (1990q to 2018q) source from the International Monetary Fund (IMF) financial archive through the Easy-Data website. Thus conducted trend analysis which includes descriptive statistics, autoregressive conditional heteroscedasticity based on the Lagrange Multiplier (ARCH-LM) test, the EGARCH model, and the Constant Conditional Correlation GARCH (CCC-GARCH) models. Additionally, test volatility and evaluate the effect of capital flows volatility on exchange rate volatility for all the countries. Further, examine the contagious effect of the exchange rates among the countries. Result: The investigations revealed that throughout the study, Botswana experienced the highest rate of exchange rate volatility while Morocco was the least in the sample countries. Further, the series are all platykurtic, i.e. there is a reasonable level of fluctuation in the series throughout the study period. In other words, positive (appreciations) are more likely to occur in Morocco, while depreciation is more likely to occur in other countries. The trend analyses also revealed the presence of relative stability for Morocco's exchange rate, while the exchange rate of the remaining countries varied significantly. Implications: These fluctuations were acute during the period 2001-2018. In this regard, the study has concluded that the exchange rate volatility of these countries was independently determined. Thus, they can ignore monetary and fiscal policy and pursue internal goals, such as full employment, stable growth, and price stability.

Mapping the stocks in MICEX: Who is central in the Moscow Stock Exchange?

AbstractIn this article, we use partial correlations to derive bi‐directional connections between major firms listed in the Moscow Stock Exchange. We obtain coefficients of partial correlation from the correlation estimates of the Constant Conditional Correlation GARCH (CCC‐GARCH) and the consistent Dynamic Conditional Correlation GARCH (cDCC‐GARCH) models. We map the graph of partial correlations using the Gaussian Graphical Model and apply network analysis to identify the most central firms in terms of both shock propagation and connectedness with others. Moreover we analyze some network characteristics over time and based on these we construct a measure of system vulnerability to external shocks. Our findings suggest that during the crisis interconnectedness between firms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected firms are the state‐owned firms Sberbank and Gazprom and the private oil company Lukoil, while in terms of the top most central systemic risk contributors, Sberbank gave its place to the NLMK Group.

Mapping the Stocks in MICEX: Who Is Central to the Moscow Stock Exchange?

In this article, we use partial correlations to derive bi‐directional connections between major firms listed in the Moscow Stock Exchange. We obtain coefficients of partial correlation from the correlation estimates of the Constant Conditional Correlation GARCH (CCC‐GARCH) and the consistent Dynamic Conditional Correlation GARCH (cDCC‐GARCH) models. We map the graph of partial correlations using the Gaussian Graphical Model and apply network analysis to identify the most central firms in terms of both shock propagation and connectedness with others. Moreover we analyze some network characteristics over time and based on these we construct a measure of system vulnerability to external shocks. Our findings suggest that during the crisis interconnectedness between firms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected firms are the state‐owned firms Sberbank and Gazprom and the private oil company Lukoil, while in terms of the top most central systemic risk contributors, Sberbank gave its place to the NLMK Group.

Whittle estimation in multivariate CCC-GARCH processes

In this paper, we explore some probabilistic properties and statistical analysis of multivariate constant conditional correlation GARCH (CCC-GARCH for short) model. So, in the first part we give the conditions for the model stationarity and its finite moments up to some orders. In the second part, the Whittle estimator is proposed for the parameters CCC-GARCH model based on a transformation. This Whittle estimator is shown to be consistent when the data have finite 4th moment, and its asymptotic normality is established when the data have finite 8th moment. Finite sample properties of this Whittle estimator are further examined through Monte-Carlo experiments.

What is the Spillover Effect of the U.S. Equity and Money Market on the Key Latin American Agricultural Exports?

This paper employs a multivariate constant conditional correlation (CCC) GARCH model and the VAR-AGARCH model to examine whether the U.S. equity and money market have a volatility spillover effect on the returns of the most important agricultural export products of Latin America over the turbulent 2005–2016 period. These results indicate the strengthening of crossmarket linkages between U.S. equity and money market and agricultural raw material commodities (notably sugar and soy) during the period of an upward trend and financial turmoil.

Forecasting market risk of portfolios: copula-Markov switching multifractal approach

ABSTRACTThis paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christoffersen [1998. “Evaluating Interval Forecasts.” International Economic Review 39: 841–862], the GMM duration-based test by Candelon et al. [2011. “Backtesting Value at Risk: A GMM Duration-based Test.” Journal of Financial Econometrics 9: 314–343] and the superior predictive ability (SPA) test by Hansen [2005. “A Test for Superior Predictive Ability.” Journal of Business and Economic Statistics 23, 365–380] we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance–covariance, RiskMetrics, copula-GARCH and constant conditional correlation GARCH (CCC-GARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction.

A Lagrange multiplier test for testing the adequacy of constant conditional correlation GARCH model

ABSTRACTA Lagrange multiplier test for testing the parametric structure of a constant conditional correlation-generalized autoregressive conditional heteroskedasticity (CCC-GARCH) model is proposed. The test is based on decomposing the CCC-GARCH model multiplicatively into two components, one of which represents the null model, whereas the other one describes the misspecification. A simulation study shows that the test has good finite sample properties. We compare the test with other tests for misspecification of multivariate GARCH models. The test has high power against alternatives where the misspecification is in the GARCH parameters and is superior to other tests. The test is not greatly affected by misspecification in the conditional correlations and is therefore well suited for considering misspecification of GARCH equations.

Volatılıty Spıllovers from The Internatıonal Capıtal Inflows to Economıc Growth in Turkey

This paper empirically investigates the volatility interactions between the international capital inflows to Turkey and Turkish economic growth using the post-financial-liberalization era data. With an Extended Constant Conditional Correlation GARCH model, it is shown that there are volatility spillovers from the capital inflows to growth in Turkey. Some earlier studies in literature have already established a positive relationship between the capital inflows and economic growth in Turkey. According to their results, as the mean value of capital inflows to Turkey increases, so does the conditional mean value of Turkish economic growth. This study is important for it shows that as the volatility of capital inflows to Turkey increases, so does the volatility of Turkish economic growth.

Volatility Spillovers from the International Capital Inflows to Economic Growth in Turkey

This paper empirically investigates the volatility interactions between the international capital inflows to Turkey and Turkish economic growth using the post-financial-liberalization era data. With an Extended Constant Conditional Correlation GARCH model, it is shown that there are volatility spillovers from the capital inflows to growth in Turkey. Some earlier studies in literature have already established a positive relationship between the capital inflows and economic growth in Turkey. According to their results, as the mean value of capital inflows to Turkey increases, so does the conditional mean value of Turkish economic growth. This study is important for it shows that as the volatility of capital inflows to Turkey increases, so does the volatility of Turkish economic growth.

Volatilidad del Mercado Integrado Latinoamericano: un enfoque multivariado

This paper describes Latin American Integrated Market (MILA) major stock indices yields´ volatility and addresses the degree of dependence among such stock indices, by means of a multivariate non normal constant conditional correlation GARCH model for 2009-2016. Evidence was found that there is a low degree of dependence among the yields and that segmentation exists among some of its members. The degree of integration is higher in Chile and Mexico followed by Peru and finally Colombia. Despite the differentiated volatility between members of the MILA, the dependency level among their yields has been stable over the reference period, suggesting long term market integration

Inference and testing on the boundary in extended constant conditional correlation GARCH models

We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and score statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.

Anomaly in Stock-Bond Correlations: The Role of Monetary Policy

The paper estimates constant conditional correlation (CCC) GARCH models to test whether the dramatic changes in stock-bond market correlations can be explained by monetary policy variables such as OIS interest rate shocks or volatility regimes. We find that both specifications are empirically relevant: Correlations decrease after positive monetary shocks (decreasing rates) as well as in times with large central bank activity (high rate volatility regimes).

Inference and Testing on the Boundary in Extended Constant Conditional Correlation GARCH Models

We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and LM statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.

TARGETING ESTIMATION OF CCC-GARCH MODELS WITH INFINITE FOURTH MOMENTS

As an alternative to quasi-maximum likelihood, targeting estimation is a much applied estimation method for univariate and multivariate GARCH models. In terms of variance targeting estimation, recent research has pointed out that at least finite fourth moments of the data generating process is required, if one wants to perform inference in GARCH models by relying on asymptotic normality of the estimator. Such moment conditions may not be satisfied in practice for financial returns, highlighting a potential drawback of variance targeting estimation. In this paper, we consider the large-sample properties of the variance targeting estimator for the multivariate extended constant conditional correlation GARCH model when the distribution of the data generating process has infinite fourth moments. Using nonstandard limit theory, we derive new results for the estimator stating that, under suitable conditions, its limiting distribution is multivariate stable. The rate of consistency of the estimator is slower than$\sqrt T$and depends on the tail shape of the data generating process. A simulation study illustrates the derived properties of the targeting estimator.

Inference and Testing on the Boundary in Extended Constant Conditional Correlation GARCH Models

Inference and Testing on the Boundary in Extended Constant Conditional Correlation GARCH Models

Testing causality between two vectors in multivariate GARCH models

The family of Constant Conditional Correlation GARCH models is used to model the risk associated with financial time series and to make inferences about Granger-causal relationships between second conditional moments. The restrictions for second-order Granger noncausality between two vectors of variables are derived and assessed using posterior odds ratios. This Bayesian method constitutes an alternative to classical tests and can be employed regardless of the form of the restrictions on the parameters of the model. This approach enables the assumptions about the existence of higher-order moments of the processes that are required in classical tests to be relaxed. In the empirical example, a bidirectional second-order causality between the pound-to-Euro and US dollar-to-Euro exchange rates is found.

Volatility spillovers across daytime and overnight information between China and world equity markets

This article explores the transmission of daytime and overnight information in terms of returns and volatility between Chinese and Asian, European and North American main stock markets. We propose a bivariate analysis with China as benchmark. By testing the constancy of the conditional correlations, we use an extended constant or dynamic conditional correlation GARCH model. The empirical findings show that across the daytime information transmissions, the relationships between China and Asian markets are closer than China and non-Asian markets, whereas through the overnight information transmissions these relationships are inverse. The analysis provides, before the crisis, that the overnight volatility spillover effects are from China to the United States and the United Kingdom. During the crisis, China affects the United Kingdom in terms of daytime volatility spillovers, whereas in terms of overnight volatility spillovers China affects the United States and is influenced by Japan. After the crisis, daytime volatility spillovers are from Taiwan to China, whereas the overnight volatility spillover effects are from China to the United States and the United Kingdom.

A dynamic double asymmetric copula generalized autoregressive conditional heteroskedasticity model: application to China's and US stock market

Modeling the relationship between multiple financial markets has had a great deal of attention in both literature and real-life applications. One state-of-the-art technique is that the individual financial market is modeled by generalized autoregressive conditional heteroskedasticity (GARCH) process, while market dependence is modeled by copula, e.g. dynamic asymmetric copula-GARCH. As an extension, we propose a dynamic double asymmetric copula (DDAC)-GARCH model to allow for the joint asymmetry caused by the negative shocks as well as by the copula model. Furthermore, our model adopts a more intuitive way of constructing the sample correlation matrix. Our new model yet satisfies the positive-definite condition as found in dynamic conditional correlation-GARCH and constant conditional correlation-GARCH models. The simulation study shows the performance of the maximum likelihood estimate for DDAC-GARCH model. As a case study, we apply this model to examine the dependence between China and US stock markets since 1990s. We conduct a series of likelihood ratio test tests that demonstrate our extension (dynamic double joint asymmetry) is adequate in dynamic dependence modeling. Also, we propose a simulation method involving the DDAC-GARCH model to estimate value at risk (VaR) of a portfolio. Our study shows that the proposed method depicts VaR much better than well-established variance–covariance method.