Time-Varying Copula Modelling Between Malaysia and Major Stock Markets (original) (raw)
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International Journal of Research and Scientific Innovation (IJRSI), 2024
Selecting the right dependence measure is crucial for multivariate statistical modelling. Because correlation is based on the elliptical distributions assumption of normalcy, which breaks down when there are extreme endpoints in either marginal or higher dimension, correlation is fraught with dangers. Copulas provide an alternate dependence measure that gets over correlation's drawbacks and can be used to identify if a reliance is linear, upper tail, or lower tail. In order to evaluate the efficacy of regional integration, this study investigates whether copulas are suitable for simulating bivariate reliance among five SADC stock markets. The research design used in the study was explanatory and descriptive. Because of their favourable characteristics, archimedean copulas were investigated using both parametric and non-parametric methods. Because the markets were likely to boom concurrently, non-parametric estimate produced insightful results demonstrating the suitability of the Gumbel copula in dependence modelling and suggesting that investors have opportunities for portfolio diversification throughout the region.
Analyzing Dependence Structure of Equity, Bond and Money Markets by Using Time-Varying Copulas
International Journal of Economics and Finance, 2014
In this essay, we analyze the dependence structures of equity, bond and money markets in Australia, the United States as well as the linkages between the two countries. The dependence structures have become more important for investors, risk managers and regulatory policy makers during the current period of financial crisis. Especially investors should be aware of the dependence structures which show the co-movement patterns between different markets in order to diversify and reduce the risks of their portfolios. To capture the structure linkages between different markets, we propose the combination of empirical distributions and time-varying copula models. Furthermore, we show an effective and informative way to analyze dependence between variables, especially to provide a better understanding of the co-movements of financial variables as well as the risks associated with dependence structures among them. The empirical findings provide some important implications of a wide range of areas related to investment in Australian and US financial markets.
Modeling the Dependency Structure of Stock Index Returns using a Copula Function Approach
2010
In the present study we assess the dependency structure between stock indexes by econometrically estimating the empirical copula function and the parameters of various parametric copula functions. The main finding is that the t-copula and the Gumbel-Clayton mixture copula are the most appropriate copula functions to capture the dependency structure of two financial return series. With the dependency structure given by the estimated copula functions we quantify the efficient portfolio frontier using as a risk measure CVaR (Conditional VaR) computed by Monte Carlo simulation. We find that in the case of using normal distributions for modeling individual returns the market risk is underestimated no mater what copula function is employed to capture the dependency structure.
Modelling dependence between the equity and foreign exchange markets using copulas
Applied Mathematical Sciences, 2014
Dependence between financial variables is a key consideration for portfolio diversification and risk management. Linear correlation as a measure of dependence is inadequate in capturing dependence of financial variables. In this paper we apply the semi parametric copula based multivariate dynamical model to estimate dependence structure between the equity and foreign exchange markets in Kenya. Several parametric copula models are fitted into the data and their performance in capturing the dependence compared. We find that there exists significant symmetric dependence between the variable. Besides, we find evidence of tail dependence amongst the variables. The findings of this paper are significant to global investors in their pursuit to diversify their portfolios and manage their risks 5814 Stanley O. Sewe et al.
Journal of Statistical Theory and Practice, 2011
The aim of this paper is to model the dependency among log-returns when security account prices are expressed in units of a well diversified world stock index. The paper uses the equi-weighted index EWI104s, calculated as the average of 104 world industry sector indices. The log-returns of its denominations in different currencies appear to be Student-t distributed with about four degrees of freedom. Motivated by these findings, the dependency in log-returns of currency denominations of the EWI104s is modeled using time-varying copulae, aiming to identify the best fitting copula family. The Student-t copula turns generally out to be superior to e.g. the Gaussian copula, where the dependence structure relates to the multivariate normal distribution. It is shown that merely changing the distributional assumption for the log-returns of the marginals from normal to Student-t leads to a significantly better fit. Furthermore, the Student-t copula with Student-t marginals is able to better capture dependent extreme values than the other models considered. Finally, the paper applies copulae to the estimation of the Value-at-Risk and the expected shortfall of a portfolio, constructed of savings accounts of different currencies. The proposed copula-based approach allows to split market risk into general and specific market risk, as defined in regulatory documents. The paper demonstrates that the approach performs clearly better than the RiskMetrics approach.
Revisiting the Dependence between Financial Markets with Copulas
SSRN Electronic Journal, 2000
We consider the problem of modelling the dependence between financial markets. In financial economics, the classical tool is the Pearson (or linear correlation) to compare the dependence structure. We show that this coefficient does not give a precise information on the dependence structure. Instead, we propose a conceptual framework based on copulas. Two applications are proposed. The first one concerns the study of extreme dependence between international equity markets. The second one concerns the analysis of the East Asian crisis.
Social Science Research Network, 2012
This paper develops a dependence-switching copula model to examine dependence and tail dependence for four different market statuses, namely, rising-stocks/appreciating-currency, falling-stocks/depreciating-currency, rising-stocks/depreciating-currency, and falling-stocks/appreciating-currency. The model is then applied to daily stock returns and exchange rate changes for six major industrial countries over the 1990-2010 period. The dependence and tail dependence among the above four market statuses are asymmetric for most countries in the negative correlation regime, but symmetric in the positive correlation regime. These results enrich the findings in the existing literature and suggest that analyzing crossmarket linkages within a time-invariant copula framework may not be appropriate.
Vine Copula Approach to Understand the Financial Dependence of the Istanbul Stock Exchange Index
Computational economics, 2024
Recently, the complex dependence patterns among various stocks gained more importance. Measuring the dependency structure is critical for investors to manage their portfolio risks. Since the global financial crisis, researchers have been more interested in studying the dynamics of dependency within stock markets by using novel methodologies. This study aims to investigate a Regular-Vine copula approach to estimate the interdependence structure of the Istanbul Stock Exchange index (ISE100). For this purpose, we consider 32 stocks related to 6 sectors belonging to ISE100. To reflect the time-varying impacts of the 2008-2009 global financial crisis, the dependence analysis is conducted over pre-, during-, and post-global financial crisis periods. Portfolio analysis is considered via a rolling window approach to capture the changes in the dependence. We compare the Regular-Vine-based generalized autoregressive conditional heteroskedasticity (GARCH) against the conventional GARCH model with different innovations. Value at risk and expected shortfall risk measures are used to validate the models. Additionally, for the constructed portfolios, return performance is summarized using both Sharpe and Sortino ratios. To test the ability of the considered Regular-Vine approach on ISE100, another evaluation has been done during the COVID-19 pandemic crisis with various parameter settings. The main findings across different risky periods illustrate the suitability of using the Regular-vine GARCH approach to model the complex dependence among stocks in emerging market conditions. Keywords R-Vine copula • Global financial crisis • Istanbul stock exchange • Valueat-risk • Expected shortfall methods to predict and control market risks. Understanding the dynamic of interdependence between stocks is vital for investors to manage their portfolio's risk and forecast returns (Liu et al., 2017). Copulas are a popular tool for modeling dependence and risk in finance. They offer a flexible way to model the joint distribution of two or more random variables, which is especially common in stock markets. Besides, copula modeling can be beneficial when detecting the symmetric and asymmetric dependence patterns for financial data in times of stress. Correlation is a traditionally used measure of dependence, applicable only in the elliptical world, for example, when the returns follow a multivariate Gaussian or Student's t-distribution. When there are non-linear relationships between returns, the correlation may not adequately describe the type of dependency, thus leading to an underestimation of the joint risk of extreme events (Junker et al., 2005). Copulas ask a different question, such as "How do two variables act together and how strong is this simultaneous movement at various points in the distribution" (Vuolo, 2015), rather than how variable X affects variable Y. In this context, the advantage of using copula in the co-movement analysis is multifaceted (Ning, 2010). The motivation behind the copula is that it allows a separation of the dependence structure from its margins and captures the non-linear dependency patterns. Copula also allows for asymmetric dependence, which has important implications when calculating portfolio risks (Nelsen, 2007; Patton, 2013; Prince & Anokye, 2020). Therefore, copula adapts well to the dependency of financial data, making it a good choice for incorporating dependence into the model (Embrechts et al., 1999). Although copulas are widely-used in finance and economics, they are not practical for high-dimensional data. Vine copulas (or Vines) are tree-based models to overcome such limitations of multivariate copulas (Cooke, 1997; Bedford & Cooke, 2001, 2002). Vines, also called as pair copula construction (PCCs), rely on the use of bivariate copulas. Each pair captures the dependence between two variables sequantially. Vine copulas offer better flexibility than standard multivariate copula models due to the wide selection of bivariate copula models (Heinen & Valdesogo, 2008; Kurowicka & Joe, 2010). Additionally, Vines can overcome the limiting features of alternative measures of dependency and correlation, such as Pearson, Spearman, and Kendall (Hernandez, 2015). Bedford and Cooke (2001) and Bedford and Cooke (2002) graphically explored the pair-copula constructions, regular vines (R-Vines), and developed two main sub-classes, called canonical vines (C-Vines) and drawable vines (D-Vines). C-and D-Vines are beneficial for specific tree structures whereas R-Vines are more flexible framework. The 2008-2009 financial crisis provides an example of how financial institutions and their markets are interconnected and how shocks in one industry can threaten the stability of the other sectors or the entire system. In Turkey, there is a gap in the dependence analysis of stocks, covering both the financial and other sectors. In this direction, the contributions of this study are twofold. First, we examine the codependencies of 32 stocks with the R-Vine copula model. The duration of data is selected as 01.01.2005-31.12.2013 to investigate the effects of the pre-, during, and post-GFC periods. Within the 32 stocks of ISE100, we study the sub-sector varying dependencies by focusing on R-Vines. A general understanding of the structure of co-dependency between sectors is critical in measuring a portfolio's risk. Secondly,
Journal of Banking & Finance, 2013
This paper develops a dependence-switching copula model to examine dependence and tail dependence for four different market statuses, namely, rising-stocks/appreciating-currency, falling-stocks/depreciating-currency, rising-stocks/depreciating-currency, and falling-stocks/appreciating-currency. The model is then applied to daily stock and foreign-exchange returns for six major industrial countries over the period 1990-2010. It is found that the dependence and tail dependence among the above four market statuses are asymmetric, for most countries, in the negative correlation regime, but symmetric in the positive correlation regime. These results enrich findings in existing literature and suggest that analyzing cross-market linkages within a time-invariant copula framework may not be appropriate.
Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange)
International Journal of Science and Research (IJSR), 2020
This study examines the dependence structure of Ghana's financial market using copula methods and the correlation method. Modeling multivariate probability distributions can be difficult if the marginal probability density functions of the random variables of the components differ. Most microeconomic modeling situations have marginal distributions that cannot be easily combined into joint distributions. Since there are few or no joint parametric distributions based on the margins of different families, the copula method provides a simple and general approach to building joint distributions in these situations. Financial markets are concerned with whether prices of different assets exhibit dependence. For these reasons, copulas have become very important as a technique for modeling these non-constant correlations. This has been a great blessing for financial engineering because it is possible to flexibly model these nonlinear relationships. Copula is a suitable tool for modeling dependence between random variables with any marginal distributions. This is why the copula method will be used to study how the various selected stocks move together. How can the Copula method be used on a stock exchange market? This report introduces the idea of a copula, consisting of correlation and dependence, completes the basic mathematics behind its composition and the applications in financial engineering, in particular the structure of dependency in the Ghanaian financial market (promotions). This report examines the linear and non-linear dependency (structure) between the stocks selected on the Ghanaian stock market using the Joe Clayton Copula.