Pair copula constructions to determine the dependence structure of treasury bond yields

Righi, Marcelo Brutti; Schlender, Sergio Guilherme; Ceretta, Paulo Sergio

doi:10.1016/j.iimb.2015.10.003

Cited by 4 publications

(4 citation statements)

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“…However, instead of emerging markets, this research's scope mainly focuses on the US stock market, which sometimes involves very high return as well as volatility. Furthermore, most of the recent studies concentrate on explaining the dependence modeling in developed countries, such as Liu et al (2013) or Righi et al (2015). Furthermore, commodities and energy sources are also important factors for estimation, as in studies from Masters and White (2011), Tang and Xiong (2012), and Adams and Glück (2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

Duong

Huynh

2020

Financ Innov

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This study contributes a rich set of quantitative methodologies including a nonparametric approach (Chi-plots and K-plots) as well as copulas (traditional and timevarying with Student's t-copulas) to the existing literature in terms of determining the dependence structure in ASEAN stock markets. Drawing on the emerging ASEAN equity returns of six countries from January 2001 to December 2017, we found that Student's t-copulas under time-varying approach is the most appropriate approach to explain these co-movements. Among all research return pairs, the dependence between Vietnam and other ASEAN equity indices has the lowest value. Meanwhile, all couples show left-and right-tail dependence by each pair for pre-and postfinancial shocks. Hence, diversification across these pairs of equity markets from ASEAN is still adequate for international investors, though it might trigger contagion risks.

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Section: Literature Reviewmentioning

confidence: 99%

Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

Duong

Huynh

2020

Financ Innov

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“…Next, the predictors directly connected (parent variables) to the target variable are used to develop the prediction model discarding the independent and conditionally independent variables, as identified by the graph structure. The prediction model is developed using C‐Vine copula approach, in which a sequence of trees is identified to develop the conditional distribution of the target variable given the parents (Xiao, ; Bauer et al ., ; Liu et al ., ; Righi et al ., ; Dalla Valle et al ., ). The selection of each tree is based on a maximum spanning tree algorithm, where edge weights are chosen to reflect the dependencies and the final tree can be used for the prediction of the target variable given the input variables (Dutta and Maity, ).…”

Section: Methodsmentioning

confidence: 99%

Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan

Maity

Chanda

Dutta

et al. 2020

Meteorological Applications

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Hydroclimatic teleconnections between global sea surface temperature (SST) anomaly fields and monthly rainfall over east and west Japan (divided along 138° E longitude) are identified for summer (June–August) and winter (December–February) using the concept of global climate pattern (GCP). The analysis indicates that the hydroclimatic teleconnections over both regions vary at both intra‐ and inter‐seasonal time scales. In addition, the teleconnections over the two regions have differing origins. The teleconnection features associated with rainfall anomalies over west Japan have origins in the tropical Pacific and Indian oceans, whereas those over east Japan are associated with high‐latitude SST anomalies. The early summer (winter) rainfall over west Japan is linked to the El Niño Modoki (La Niña Modoki) phenomena, whereas the early summer and winter rainfall anomalies over east Japan are associated with the SST anomaly over the eastern subtropical Pacific and South Pacific oceans, respectively. Having identified the teleconnections, prediction model approaches—a machine‐learning approach, namely support vector regression (SVR), and a hybrid graphical modelling/C‐Vine copula (GM‐Copula)—were developed to forecast the rainfall over both east and west Japan. The predictors were derived from the monthly SST anomalies at different lags (1–6 months), and whereas the hidden, nonlinear relationship was well captured by the SVR approach, the complex association was decidedly better captured by the GM‐Copula approach. Hence, it is recommended for forecasting the rainfall over east and west Japan.

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“…The recent literature provides strong and rich evidence of return correlations among stocks and between stocks and bonds (Kim et al, 2006). According to Righi et al (2015), the bivariate copula framework offers more flexibility than the traditional methodologies like the correlation, VaR. In this work, we utilize some of the most popular families of copulas to model dependencies between pairs of stock returns listed on HoSE.…”

Section: Copulamentioning

confidence: 99%

On some nonlinear dependence structure in portfolio design

Son¹,

Uyen

Thien

2018

Sci. Tech. Dev. J. Econ. Law. Manag.

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Constructing portfolios with high returns and low risks is always in great demand. Markowitz (1952) utilized correlation coefficients between pairs of stocks to build portfolios satisfying different levels of risk tolerance. The correlation coefficient describes the linear dependence structure between two stocks, but cannot capture a lot of nonlinear independence structures. Therefore, sometimes, portfolio performances are not up to investors' expectations. In this paper, based on the theory of copula by Sklar (see [19]), we investigate several new methods to detect nonlinear dependence structures. These new methods allow us to estimate the density of the portfolio which leads to calculations of some popular risk measurements like the value at risk (VaR) of investment portfolios. As for applications, making use of the listed stocks on the Ho Chi Minh city Stock Exchange (HoSE), some Markowitz optimal portfolios are constructed together with their risk measurements. Apparently, with nonlinear dependence structures, the risk evaluations of some pairs of stocks have noticeable twists. This, in turn, may lead to changes of decisions from investors.

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Pair copula constructions to determine the dependence structure of treasury bond yields

Cited by 4 publications

References 0 publications

Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan

On some nonlinear dependence structure in portfolio design

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