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A Vine Copula Approach for Analyzing Financial Risk and Co-movement of the Indonesian, Philippine and Thailand Stock Markets
Abstract
This paper aims at analyzing the financial risk and co-movement of stock markets in three countries: Indonesia, Philippine and Thailand. It consists of analyzing the conditional volatility and test the leverage effect in the stock markets of the three countries. To capture the pairwise and conditional dependence between the variables, we use the method of vine copulas. In addition, we illustrate the computations of the value at risk and the expected shortfall using Monte Carlo simulation with copula based GJR-GARCH model. The empirical evidence shows that all the leverage effects add much to the capacity for explanation of the three stock returns, and that the D-vine structure is more appropriate than the C-vine one for describing the dependence of the three stock markets. In addition, the value at risk and ES provide the evidence to confirm that the portfolio may avoid risk in significant measure.
... Studies undertaken before the COVID-19 pandemic revealed the existence of previous volatility in ASEAN stock markets. Stock markets in the ASEAN Economic Community (AEC) have also been found to have relationships with each other and other stock markets outside the region (Click and Plummer, 2005;Janor and Ali, 2007;Sriboonchitta et al., 2014;Duong and Huynh, 2020;Chitkasame and Tansuchat, 2019;Pongkongkaew et al., 2020;Lim, 2007;Jakpar et al., 2013;Lean and Smyth, 2014). ...
... Many studies have demonstrated the potential use of copulas and GARCH models. For example, Sriboonchitta et al. (2014) found stock market volatility in Indonesia, the Philippines and Thailand was related and had a tail dependence. These results were consistent with those of Duong and Huynh (2020), who found the left-and right-tail dependence on the volatility of stock markets in Vietnam, Thailand, Singapore, the Philippines, Malaysia and Indonesia. ...
Purpose
Unlimited quantitative easing (QE) is one of the monetary policies used to stimulate the economy during the coronavirus disease 2019 (COVID-19) pandemic. This policy has affected the financial markets worldwide. This empirical research aims at studying the dependence among stock markets before and after unlimited QE announcements.
Design/methodology/approach
The copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market data, on 6 ASEAN and 8 other countries outside the region. The data are divided into two periods to compare the differences in dependence.
Findings
The findings show changes in dependence among the volatility of daily returns in 14 stock markets during each period. After the unlimited QE announcement, the upper tail dependence became more apparent, while the role of the lower tail dependence was reduced. The minimum spanning tree can show the close relationships between stock markets, indicating changes in the connection network after the announcement.
Originality/value
This study allows the dependency to be compared between stock market volatility before and after the announcement of unlimited QE during the COVID-19 pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models to analyze and reveal the systemic network of the relationships.
... thus, we get 13 c , and 231 c are the pair copulas; ( ) ...
... To confirm our finding, we, then, computed the Value at Risk (VaR) and Expected Shortfall (ES) to measure the risk of two portfolios namely; Brent, DJIA, FTSE 100, N225 and gold portfolios (BDFNG-Portfolios) and Brent, DJIA, FTSE 100, N225 portfolios (BDFN-Portfolios). Following Sriboonchitta, Liu, Kreinovich, and Nguyen [13], we make use of the Monte Carlo simulation by simulated 10,000 jointlydependent uniform variates from the estimated C-vine copulas.to calculate the VaR and ES of equally weighted portfolio since C-vine copulas since this structure provide an appropriate than D-vine (lowest AIC and BIC). ...
This paper aims to analyze the co-movement and dependence of three stock markets, oil market, and gold market. These are gold prices as measured by gold future, crude oil prices as measured by Brent, and stock prices as measured by three developed stock markets comprising the U.S. Dow Jones Industrial Average, the London Stock Exchange, and the Japanese Nikkei 225 index. To capture the correlation and dependence, we employed the application of C-vine copula and D-vine copula. The results demonstrate that the C-vine copula is a structure more appropriate than the D-vine copula. In addition, we found positive dependency between the London Stock Exchange and the other markets; however, we also obtained complicated results when the London Stock Exchange, the Dow Jones Industrial Average, and Brent were given as the conditions. Finally, we found that gold might be a safe haven in this portfolios.
... The standardized residuals calculated from inverse Skewed-t CDF along with the estimated parameters of the NGARCH model are later used to forecast the log returns of each asset in the portfolio. We assume all stocks are equally weighted (see Sriboonchitta et al., 2013). Finally, we calculate the value of the portfolio for each of the simulations and use the empirical quantile function to calculate VaR and ES at different significance levels. ...
In this paper, we construct vine copula models for multivariate stock portfolio returns to estimate one- day-ahead and multi-day ahead Value-at-Risk (VaR) and Expected Shortfall (ES) using Monte Carlo simulation. This is then compared with the VaR and ES using the dynamic conditional correlation (DCC) method. For the multi-day horizon, we use Monte Carlo simulation to simulate the share prices h-days ahead. The simulation-based method allows us to calculate VaR and ES for multivariate data at any horizons of interest and hence to calculate the entire term structure of risk. Using seven stocks from the DAX 30 as a case in point, we demonstrate the overall superiority of the copula-based method over the widely accepted DCC method. VaR and ES back-testing results indicate that vine copula significantly outperforms the DCC approach over a one-day horizon. This performance by the copula-based method is maintained across a multi-day horizon. Our findings suggest that institutions that use copula models to estimate their risk capital will need to set aside less capital to meet regulatory needs, than would otherwise be the case.
... There are also some linear and nonlinear modeling approaches which are specifically designed for the time series data such as autoregressive models (Abegaz and Wit, 2013), moving average models and volatility models (Engle and Patton, 2007). In terms of vine copula, there are several studies in economics that only some of them are going to be mentioned here such as the study of Patton (2012) which is a review of copula models for time series economic data and Sriboonchitta, et al. (2014) in which the D-Vine copula is used in the inference of pairwise and conditional dependence between variables in three different countries. In another study, the vine copula approach is used to perform a new production function in economic data (Constantino, et al. 2019). ...
In the stock market, the relationship between the sectorial changes can be very informative in order to predict the changes in prices of assets from each sector. In order to understand these sectorial relations, various studies have been conducted. In one of the recent studies, the construction sector in Turkey was investigated in terms of its effect in other Turkish sectors since it is one of the leading sectors in Turkey and its assets have a significant impact in stock markets. Hereby, in this study we detect the sectorial relationship of the construction sector via the Regular vine, also known as R-Vine, approach. The R-Vine copula is a specific type of copula which enables us to be flexible in the distributional assumptions of the observations while stating their joint distribution function. By this way, we can investigate the structure of the country's financial path, i.e., network, under a graphical model.
... For the studies measuring the correlations (interdependence) between different stock markets, most of which are based on Efficient Market Hypothesis (EMH), employ the methods of econometric: GARCH [27][28][29][30], Copula [31][32][33][34], VAR [35][36][37], Markov switching model [38], and Pearson correlation coefficient [39]. However, the traditional methods based on the efficient market theory cannot describe accurately the change in the nonlinear, long-range, and complex dynamic stock markets involving multiple agents and affected by multiple factors [40]. ...
Employing the tools of multifractal detrended cross-correlation analysis (MF-DCCA) and Diebold-Yilmaz spillover index (D.Y. spillover index), we examine the effect that the foreign investors have on the cross-correlations between the two-segment stock markets, that are the accessible and the inaccessible stock markets, and the other ten respective stock markets. The shares cross-listed by the same corporates on both the A-share and H-share stock markets of China serve as the best sample to compile the two stock indices, which stands for the inaccessible stock market (AHA) and the accessible stock market (AHH), respectively. Empirical results show that the cross-correlations between the two-segment stock markets and the other ten pairs are multifractal, the multifractal strength of cross-correlations is stronger in AHH than AHA, and the intensified growth of the multifractal cross-correlations in AHA can be seen as the increasing of the openness in the inaccessible market. The empirical result of D.Y. spillover index is consistent with the multifractal analysis above, and another interesting finding is that among the selected markets, the three markets with the strongest spillover effects with AHA and AHH are Taiwan, South Korea, and Singapore, respectively, and the weakest one is Australia during the sample scenarios.
... Because they only take into account dependence between pairs of variables. Thus vine copulas become more flexible since they allow us to select the different bivariate copulas [18,14]. Vine copulas have been mostly used in financial time series and firstly introduced by Aas, Czado, Frigessi, and Bakken [1]. ...
The paper aims to measure the risk and find the optimal weights of portfolio containing three instruments: Stock Exchange of Thailand, Thai Baht gold, and Treasury 10-year bond yield. The study employs the C-D vine copulas approach to construct the dependency of each pair instruments and uses the Monte Carlo simulation technique to generate the simulated data to compute Value at Risk (VaR) and Expected Shortfall (ES). Our results show that there exists a weak significant dependency between Stock Exchange of Thailand index and Thai Baht gold and dependency between Treasury 10-year bond yield and Thai Baht gold. Moreover, we find that the desired portfolio allocation is 49.8% of SET, 18.8% of Bond, and 31.4% of Gold where risk and return of the portfolio are 2.7% and 0.05%, respectively.
... nd gas stocks in crisis periods. Same as, Autchariyapanitkuly et al. (2014) constructed portfolio of stock returns in SET50 index, Sriboonchitta et al. (2014) examine optimization portfolio in agricultural commodities, including coffee, corn, cotton, soybean, sugar,and wheat by using Monte Carlo simulation with vine copula based cross entropy, and Righi and Ceretta. (2015) who present an algorithm to compute VaR, CAViaR, ES and CARES from this serial PCC structure (vine copula). ...
The objectives of this study are to construct the optimum energy commodity portfolio investment by using Vine-copula GARCH, and to quantify their risk with Value-at-Risk and expected shortfall. The 1,979 energy commodity futures prices from 7 energy commodity products, namely crude oil, natural gas, gasoline, heating oil, diesel, ethanol, and gasoil, were collected from 1 September 2009 to 31 March 2017, traded in the New York Mercantile Exchange (NYMEX). The empirical results showed that every fitted conditional volatility models are ARMA-EGARCH with student t, and skew student t distribution, and the appropriate vine-copula model for dependence structure among three types of vine copulas is C-vine. The estimated VaR and ES of the portfolio in period t+1at 10%, 5%, and 1% level are-2.33,-3.12,-4.81 and-3.54,-4.40,-3.54 respectively. The optimum portfolio investment result suggests to focuses on the diesel, ethanol, and gas oil investment due to high investment proportion, whereas crude oil and gasoline have little investment proportion.
... As far as r set,php , r idx,php and r idx,php are concerned, we observe the presence of a positive correlation between these pairs in both the regimes. A number of studies, such as the studies conducted by Lim [21] and Sriboonchitta, Liu, Kreinovich, and Nguyen, H. T., [37] also report the positive dependence and correlation between TIP stock markets. Consequently, we can conclude that TIP stock markets are moving together and that the scope for the diversification of the TIP stock markets to reduce risk is more limited. ...
In this paper, we analyze the dependency between the Thailand, Indonesia, and the Philippine (TIP) stock markets and gold markets using dynamic copula with the Markov-switching model with 2 regimes, namely high dependence and low dependence regimes, and extend the obtained correlation to measure the market risk. We are particularly interested in examining whether or not gold serves as a hedge in the TIP stock markets. Using daily data from January 2008 to November 2014, we find that the Gaussian copula identifies a long period of high dependence of TIPGOLD returns (market downturn) which coincides with the European debt crisis. However, if we do not take gold into account, the dependence between the TIP returns is lower in both regimes, thereby leading to a higher value at risk (VaR) and expected shortfall (ES). Therefore, gold can serve as a hedging, or a safe haven, for TIP stock markets during market downturns and upturns. Additionally, the Kupiec unconditional coverage and the Christoffersen conditional coverage test are conducted for VaR and ES backtesting. The results reveal that the Gaussian Markov-switching dynamic copula is the appropriate model to estimate a dynamic VaR and ES.
... It has been used by many scholaors to represent non-linear correlation between variables In despite of its maturity in characterizing bivariate correlation, the traditional copula theory is awkward in respect of multivariate correlation. To effectively overcome this drawback, the pair-copula method [20][21][22] was introduced to estimate the correlation of multiple variables based on bivariate copula. This method decomposes a multivariate joint density function into the product of a series of pair-copula modules and marginal density functions, thereby capturing a non-linear correlation between multiple assets and effectively helping the traditional copula function in describing correlation between multiple assets. ...
With its growing popularity in the international arena of structure studies, the pair-copula method is used to analyze the social benefits arising from recent expansion of state-owned enterprises under the “expanding domestic demand” background. An empirical analysis was conducted with the financial data of top 100 state-owned listed companies during 2002-2013. The results show that, state-owned companies have been witnessing profound expansion since the release of the Ten Actions for Expanding Domestic Demand and Promoting Economic Growth promulgated by the State Council in November 2008. Monopoly expansion is not significantly correlated with financial performance but retains a significant positive correlation with social benefits. This indicates that monopoly expansion does not necessarily contribute to better financial performance but spurs state-owned enterprises to assume more social responsibilities.
Understanding sectoral dynamic dependence across equity indexes is crucial for investment decisions and designing economic policy. This study examines the sectoral dependence among 82 Pakistani companies using a vine copula approach and daily data from July 1, 2014, to December 17, 2019. Vine copulas represent a flexible dependence approach capable of capturing the dynamic and complex relationships among equity sectors stemming from the nonlinear patterns of asymmetric behavior of returns that cannot be captured by linear dependence models. The results show that the Pakistani stock market is driven by the oil and gas marketing sector and oil and gas exploration sector, with high dependence on commercial banks, fertilizers, power generation and distribution, and automobile parts and accessories. The findings help explain the tail-dependence structure among equity stocks and sectors in the context of understudied emerging markets such as Pakistan, which is vital for portfolio management and designing sector-level policies.
The aim of this study is twofold. First, it aims to show how to overcome some of the shortcomings of the standard risk measurement methods using value-at-risk (VaR) in the context of extreme events and propose an alternative empirical method. Second, it aims to fill the research gap in analysis of risk in the Middle East and North Africa (MENA) region. In this regard, for six daily stock indices in the MENA region, from January 3, 2005 to December 31, 2014, we employ a vine copula-based generalized autoregressive conditional heteroskedastic (GARCH) method and the extreme value theory (EVT) to model the dependence between the marginal distributions of returns and forecast the VaR. Based on backtesting, we assess the efficiency of the standard risk measurement models from the following families—the exponentially weighted moving average, the historical simulation, and the GARCH. By implementing the GARCH-EVT-C-vine method in the MENA region, we find that, empirically, standard methods overestimate (underestimate) the violation ratio, implying an underestimation (overestimation) of the risk, and therefore a misallocation of the capital covering the risk. The method also provides better VaR estimates for the MENA stock markets than that of the standard methods. We also verify that the greater the openness of the capital accounts and the flexibility of the exchange rate regimes, the greater will be the conditional dependence of the MENA countries on the developed markets. Finally, the empirical method we propose has an important implication for the MENA countries in that it can be adopted by these countries to forecast VaR effectively.
This paper aims to investigate the crisis linkage and transmission channels within the housing, stock, interest rate and the currency markets in the U.S. and China in the past decade since the 2008 Subprime Mortgage Crisis. Two hybrid models, namely the SWARCH-EVT-Copula and the Bivariate SWARCH-EVT models, are proposed and applied in order to take into account (A) the high/low volatility regimes, (B) the interdependence structure inherited from the joint tail behaviours, as well as, (C) the risk spillover dynamics among financial sectors during market turmoils. We empirically show that the housing and stock markets share the strongest linkage and play central roles in the spreading of shocks. With a highly integrated system, the American financial sectors are under greater exposure to risk contagion and systemic risk during crises than the Chinese markets. Nevertheless, the exchange rate risk of Renminbi remains at an intensive level since its “crawl-like arrangement” and leads to increasing co-movements in the stock and interest rate markets since 2014.
This paper aims to investigate the impact of oil price on the stock price of developed countries (represented by the Group of seven) and the stock price of developing countries (represented by BRICS) by using C-vine copula approach. A C-vine based GJR-GARCH is used to measure the conditional dependence between stock price and oil price and investigate the difference between developed countries and developing countries. The empirical evidence shows that G7 and BRICS countries have a positive and significant dependence between oil returns, and there is an even stronger dependence between oil returns for the G7 countries. Some countries in the developed and the developing countries have the tail relationship in the low market or in bear market, further, both groups do not have a relationship with oil return when the stock is in a boom market.
This paper aims at evaluating the performance of Asian Credit Default Swap (CDS) index in risk measurement and portfolio optimization by using several multivariate copulas-GARCH models with Expected Shortfall and Sharpe ratio. Multivariate copula-GARCH models consider the volatility and dependence structures of financial assets so that they are conductive to accurately predict risk and optimal portfolio. We find that vine copulas have better performance than other multivariate copulas in model estimation, while the multivariate T copulas have better performance than other kinds of copulas in risk measurement and portfolio optimization. Therefore, the model estimation, risk measurement, and portfolio optimization in empirical study should use different copula models. More importantly, the empirical results give evidences that Asian CDS index can reduce risk.
This paper used the copula based ARMA-GARCH to examine the dependence structure between the weekly prices of two commodities, namely Crude oil and Crude palm oil. We found evidence of a weak positive dependence between two commodities prices. These findings suggest that the crude oil market of the Middle East and the crude palm oil market of Malaysia are linked together. This information is useful for decision making in various area, such as the risk management in financial field and the international trade in agricultural commodities.
This paper aimed to measure the welfare of the Thai rice market and provided a new estimation in welfare measurement. We applied the Markov Switching approach to the Seemingly Unrelated Regression model and adopted the Bayesian approach as an estimator for our model. Thus, we have the MS-BSUR model as an innovative tool to measure the welfare. The results showed that the model performed very well in estimating the demand and supply equations of two different regimes; namely, high growth and low growth. The equations were extended to compute the total welfare. Then, the expected welfare during the studied period was determined. We found that a mortgage scheme may lead the market to gain a high level of welfare. Eventually, the forecasts of demand and supply were estimated for 10 months, and we found demand and supply would tend to increase in the next few months before dropping around March, 2015.
This paper aims to find the optimal Global Healthcare Portfolios at different levels of risks and returns to obtain the efficient frontier. The risks are measured by expected shortfall. The dependency of selected stocks in portfolios cannot be ignored. The high-dimension copula-models are used to capture the dependency parameters of the selected stocks. Five largest market capitalization stocks in the global healthcare sector are selected for this analysis. According to the Akaike Information Criterion (AIC), the empirical results show that t-copula is better fitted between the t- and the Gaussian copulas. Based on the t-copula, the result of this study which is the efficient frontier of the global healthcare portfolios is finally shown in Table 4 for related decision makers.
In this paper, the multiple directional dependence between response variable and covariates using non-exchangeable copulas based regression is introduced. The general measure for the multiple directional dependence in the joint behavior is provided. Several multivariate non-exchangeable copula families including skew normal copula, and the generalized Farlie-Gumbel-Morgenstern copula models are investigated. For the illustration of main results, several examples are given.
This study found the evidences of the dependence between the volatility of stock price index returns and the volatility of exchange rate returns measured against US Dollar and Japanese Yen, and the independence between the volatility of stock price index returns and the volatility of exchange rate returns measured against Euro, in both Thailand and Singapore, under the operation of QE programs. It also found that all bivariate copula of the volatility of stock price index returns—the volatility of Thai Baht/US Dollar exchange rate returns, and the volatility of stock price index returns—the volatility of Thai Baht/Japanese Yen of Thailand, had a degree of dependence greater than that of Singapore. This can be explained that the QE programs can affect capital flows to Thailand and Singapore, and also may have different effects on the volatility of each exchange rate returns and the volatility of stock price index returns, of the individual country. This information can be useful for policy makers and investors so that they can directly focus on avoiding adverse implications from the operation of QE programs, in terms of the risks incurred from the volatility of exchange rate returns and the volatility of stock price index returns.
Flexible multivariate distributions are needed in many areas. The popular multivariate Gaussian distribution is however very restrictive and cannot account for features like asymmetry and heavy tails. Therefore dependence modeling using copulas is nowadays very common to account for such patterns. The use of copulas is however challenging in higher dimensions, where standard multivariate copulas suffer from rather inflexible structures. Vine copulas overcome such limitations and are able to model complex dependency patterns by benefiting from the rich variety of bivariate copulas as building blocks. This article presents the R package CDVine which provides functions and tools for statistical inference of canonical vine (C-vine) and D-vine copulas. It contains tools for bivariate exploratory data analysis and for bivariate copula selection as well as for selection of pair-copula families in a vine. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and graphical methods are also included.
Multivariate copulas are commonly used in economics, finance and risk management. They allow for very flexible dependency structures, even though they are applied to transformed financial data after marginal time dependencies are removed. This is necessary to facilitate statistical parameter estimation. In this paper we consider a very flexible class of mixed C-vines, which allows the variables to be ordered according to their influence. Vines are built from bivariate copulas only and the term 'mixed' refers to allowing the pair-copula family to be chosen individually for each term. In addition there are many C-vine structure specifications possible and therefore we propose a novel data driven sequential selection procedure, which selects both the C-vine structure and its attached pair-copula families with parameters. After the model selection maximum likelihood (ML) estimation of the parameters is facilitated using the sequential estimates as starting values. An extensive simulation study shows a satisfactory performance of ML estimates in small samples. Finally an application involving US-exchange rates demonstrates the need for mixed C-vine models.
Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper, we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favor of the Student’s t copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the Student’s t copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant.
A new graphical model, called a vine, for dependent random variables is introduced. Vines generalize the Markov trees often used in modelling high-dimensional distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence. Vines can be used to specify multivariate distributions in a straightforward way by specifying various marginal distributions and the ways in which these marginals are to be coupled. Such distributions have applications in uncertainty analysis where the objective is to determine the sensitivity of a model output with respect to the uncertainty in unknown parameters. Expert information is frequently elicited to determine some quantitative characteristics of the distribution such as (rank) correlations. We show that it is simple to construct a minimum information vine distribution, given such expert information. Sampling from minimum information distr...
It is known that there is one equivalence class of regular vines for n = 3 and two equivalence classes for n = 4. Through an enumeration, we show that there are six equivalence classes for n = 5 with boundary classes of C-vines and D-vines, so that the dimension of n = 5 is manageable in terms of studying the intermediate regular vines. For vine copulae, which obtain from associating a bivariate copula with each pair in the vine, we develop an approach that leads to algorithms for C-vines, D-vines and intermediate vines. Some similarities between different vine copulae can be seen from how they are simulated. For Gaussian and non-Gaussian vines, we compare the marginal bivariate dependence of different vine copulae when the level l bivariate linking copulae are all set at Cl, l = 1,…, n - 1. © 2011 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.
This paper aims to estimate the dependency between the growth rates of tourist arrivals of Thailand and Singapore from China, and also analyze their conditional volatilities. Firstly, we assume that both margins are skewed-t distribution, and then make use of ARMA-GARCH model to fit monthly time series data. Secondly, fifteen types of static copulas are used to fit static dependence between tourist arrivals to Thailand and Singapore from China. We take the AIC, BIC and the two tests based on Kendall’s transform as criterions for goodness of fit test. Moreover, we apply time-varying copulas that described the dynamic Kendall’s tau process. Results show that each growth rate of tourist arrivals has a long-run persistence of volatility, and the time-varying Gaussian copula has the highest explanatory power of all the dependence structures between tourist arrivals to Thailand and Singapore from China in terms of AIC and BIC values.
With the emergence of new capital markets and liberalization of stock markets in recent years, there has been an increase in investors' interest in international diversification. This is so because international diversification allows investors to have a larger basket of foreign securities to choose from as part of their portfolio assets, so as to enhance the reward-to-volatility ratio. This paper, thus, studies the issue of co-movement between Asian emerging stock markets and developed economies using the concept of co-integration. Furthermore, it has been observed that there has been increasing interdependence between most of the developed and emerging markets since the 1987 Stock Market Crash. This interdependence intensified after the 1997 Asian Financial Crisis. With this phenomenon of increasing co-movement between developed and emerging stock markets, the benefits of international diversification become limited. We have seen that stock markets behavior is random. Several researches have shown that stock markets moves in random and does not affected by any fundamentals. Some authors describe that global sentiments and fundamentals does not prove fruitful in studying the movement of stock markets. Several investment bankers and speculators daily predict the stock market movements of one economy on the basis of stock market movements of another economy. Researches have been conducted with the purpose of finding out the potential for investors to gain from investments in different economies. The paper analyzes the interdependence (if any) of developing or emerging Asian economies and United States of America. And, thus these trends can help the investors to diversify their portfolios. This study is conducted with the objective of finding out the potential for diversification in selected Asian countries and United States of America by studying correlations in the index returns.
Volatility and dependence structure are two main sources of uncertainty in many economic issues, such as exchange rates, future prices and agricultural product prices etc. who fully embody uncertainty among relationship and variation. This paper aims at estimating the dependency between the percentage changes of the agricultural price and agricultural production indices of Thailand and also their conditional volatilities using copula-based GARCH models. The motivation of this paper is twofold. First, the strategic department of agriculture of Thailand would like to have reliable empirical models for the dependency and volatilities for use in policy strategy. Second, this paper provides less restrictive models for dependency and the conditional volatility GARCH. The copula-based multivariate analysis used in this paper nested the traditional multivariate as a special case (Tae-Hwy and Xiangdong, 2009) [13]. Appropriate marginal distributions for both, the percentage changes of the agricultural price and agricultural production indices were selected for their estimation. Static as well as time varying copulas were estimated. The empirical results were found that the suitable margins were skew t distribution and the time varying copula i.e., the time varying rotate Joe copula (270°) was the choice for the policy makers to follow. The one-period ahead forecasted-growth rate of agricultural price index conditional on growth rate of agricultural production index was also provided as an example of forecasting it using the resulted margins and time-varying copula based GARCH model.
In this survey we introduce and discuss the pair-copula construction method to build flexible multivariate distributions.
This class includes drawable (D), canonical (C) and regular vines developed in [5] and [4]. Estimation and model selection
methods are studied both in a classical as well as in a Bayesian setting. This flexible class of multivariate copulas can
be applied to model complex dependencies. Literature to applications in modeling financial data as well as Bayesian belief
networks are provided. It closes with a section on open problems.
This paper is concerned with the statistical modeling of the dependence structure of multivariate financial data using the
copula, and the application of copula functions in VaR valuation. After the introduction of the pure copula method and the
maximum and minimum mixture copula method, authors present a new algorithm based on the more generalized mixture copula functions
and the dependence measure, and apply the method to the portfolio of Shanghai stock composite index and Shenzhen stock component
index. Comparing with the results from various methods, one can find that the mixture copula method is better than the pure
Gaussian copula method and the maximum and minimum mixture copula method on different VaR level.
The objective of this study is to analyze the degree of long-term and short-term co-movements in the stock markets of five ASEAN countries and to shed some light on the long-term and short-term market efficiency/inefficiency in the region. We observe a long-run relationship among the stock markets of Indonesia, Malaysia, Singapore, and Thailand, but the Philippine market does not share this relationship. Thus, four cointegrated markets reveal long-term market inefficiency in the region but the Philippine market can be considered efficient. Next, given that the four indices are cointegrated, the number of common cycles are investigated in these markets and each stock index series is decomposed into its trend and cyclical components. The trend–cycle decomposition reveals that in the short-run four ASEAN markets can be categorized into two efficient groups, i.e., Malaysian and Singaporean markets in one group and Indonesian and Thai markets in the other. However, the markets within each group are inefficient.
Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate distribution and propose a method for performing inference. The model construction is hierarchical in nature, the various levels corresponding to the incorporation of more variables in the conditioning sets, using pair-copulae as simple building blocks. Pair-copula decomposed models also represent a very flexible way to construct higher-dimensional copulae. We apply the methodology to a financial data set. Our approach represents the first step towards the development of an unsupervised algorithm that explores the space of possible pair-copula models, that also can be applied to huge data sets automatically.
A copula approach is used to examine the extreme return–volume relationship in six emerging East-Asian equity markets. The empirical results indicate that there is significant and asymmetric return–volume dependence at extremes for these markets. In particular, extremely high returns (large gains) tend to be associated with extremely large trading volumes, but extremely low returns (big losses) tend not to be related to either large or small volumes.
It has been shown that vine copulas constructed from bivariate t copulas can provide good fits to multivariate financial asset return data. However, there might be stronger tail dependence of returns in the joint lower tail of assets than the upper tail. To this end, vine copula models with appropriate choices of bivariate reflection asymmetric linking copulas will be used to assess such tail asymmetries. Comparisons of various vine copulas are made in terms of likelihood fit and forecasting of extreme quantiles.
The objective of this paper is to re-examine the weak-form efficiency of 10 Asian emerging stock markets. Using a battery of nonlinearity tests, the statistical results reveal that all the returns series still contain predictable nonlinearities even after removing linear serial correlation from the data. The next stage of sub-sample analysis using the Hinich [Hinich, M., 1996. Testing for dependence in the input to a linear time series model. Journal of Nonparametric Statistics 6, 205–221] bicorrelation test shows that the 10 Asian series follow a pure noise process for long periods of time, only to be interspersed with brief periods of strong nonlinear dependence. The exploratory investigation found that the cross-country differences in nonlinear departure from market efficiency can be explained by market size and trading activity, while the transient burst of nonlinear periods in each individual market can be attributed largely to the occurrence of economic and political events.
Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its extreme value copulas, is shown to be completely determined by its tail dependence functions. For a vine copula built from a set of bivariate copulas, its tail dependence function can be expressed recursively by the tail dependence and conditional tail dependence functions of lower-dimensional margins. The effect of tail dependence of bivariate linking copulas on that of a vine copula is also investigated.
We present a flexible class of hierarchical copulas capable of modelling multidimensional joint distributions of asset returns with a richer rank correlation structure than existing models. We derive estimators and simulation techniques. The methods are applied to an illustrative portfolio consisting of a subset of DAX stocks.
For multivariate copula-based models for which maximum likelihood is computationally difficult, a two-stage estimation procedure has been proposed previously; the first stage involves maximum likelihood from univariate margins, and the second stage involves maximum likelihood of the dependence parameters with the univariate parameters held fixed from the first stage. Using the theory of inference functions, a partitioned matrix in a form amenable to analysis is obtained for the asymptotic covariance matrix of the two-stage estimator. The asymptotic relative efficiency of the two-stage estimation procedure compared with maximum likelihood estimation is studied. Analysis of the limiting cases of the independence copula and Frechet upper bound help to determine common patterns in the efficiency as the dependence in the model increases. For the Frechet upper bound, the two-stage estimation procedure can sometimes be equivalent to maximum likelihood estimation for the univariate parameters. Numerical results are shown for some models, including multivariate ordinal probit and bivariate extreme value distributions, to indicate the typical level of asymptotic efficiency for discrete and continuous data.
A class of multivariate distributions that are mixtures of the positive powers of a max-infinitely divisible distribution are studied. A subclass has the property that all weighted minima or maxima belong to a given location or scale family. By choosing appropriate parametric families for the mixing distribution and the distribution being mixed, families of multivariate copulas with a flexible dependence structure and with closed form cumulative distribution functions are obtained. Some dependence properties of the class, as well as some characterizations, are given. Conditions for max-infinite divisibility of multivariate distributions are obtained.
The authors find support for a negative relation between conditional expected monthly return and conditional variance of monthly return using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, they also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility, whereas negative unanticipated returns result in an upward revision of conditional volatility. Copyright 1993 by American Finance Association.
When aggregating financial risk on a portfolio level, the specification of the dependence structure between the risk factors plays an important role. Promising parametric models are often based on a so-called copula approach. Case studies of market crashes suggest the application of concepts allowing for extremal dependence. We present a transformed copula as a new model that both fits the data and allows for exact prediction in the tails. It turns out that the new model improves benchmark models like the t- or Clayton copula with respect to risk measures like VaR or Expected Shortfall. By performing different goodness-of-fit tests, the quality of the estimation is examined. Copyright 2005 Royal Economic Society
Monte Carlo simulation of vine dependent random variables for applications in uncertainty analysis
- T Bedford
- R M Cooke
Dependence comparisons of vine copulae with four or more variables Dependence Modeling: Vine Copula Handbook
- H Joe
Vine copulas with asymmetric tail dependence and applications to financial return data
- A K Nikoloulopoulos
- A.K. Nikoloulopoulos