Measuring systemic risk and dependence structure between real estates and banking sectors in China using a CoVaR ‐copula method

2020 ◽  
Author(s):  
Yufei Cao
Keyword(s):  
Systemic Risk ◽  
Dependence Structure ◽  
Copula Method ◽  
Real Estates

TIN-Copula bias correction of climate modeled daily maximum temperature in the MENA region

2021 ◽  
Author(s):  
Georgia Lazoglou ◽  
George Zittis ◽  
Panos Hadjinicolaou ◽  
Jos Lelieveld
Keyword(s):  
Hot Spot ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Dependence Structure ◽  
Daily Maximum ◽  
Model Output ◽  
Mena Region ◽  
Data Set ◽  
Variable Nature ◽  
Copula Method

<p>Over the last decades, the use of climate models in the projection and assessment of future climate conditions, both on global and regional scales, has become common practice. However, inevitable biases between the simulated model output and observed conditions remain, mainly due to the variable nature of the atmospheric system, and limitations in representing sub-grid-scale processes that need to be parameterized. The present study aims to test a new approach for increasing the accuracy of daily climate model output. We apply the recently introduced TIN-Copula statistical method to the results of a state-of-the-art global Earth System Model (Hadley Centre Global Environmental Model version 3 - HadGEM3). The TIN-Copula approach is a combination of Triangular Irregular Networks and Copulas that focuses on modeling the whole dependence structure of the studied variables. The study area of the current application is the Middle East and North Africa (MENA) region, a prominent global climate change hot-spot. Considering the lack of accurate and consistent observational records in the MENA, we used the ERA5 reanalysis dataset as a reference. The results of the study reveal that the TIN-Copula method significantly improves the simulation of maximum temperature, both on annual and seasonal time scales. Specifically, the HadGEM3 model tends to overestimate the ERA5 temperature data in the major part of the MENA region. This overestimation is mainly evident for the lower values of the studied data sets during all seasons, while in summer the overestimation is found in the whole data set. However, after the use the TIN-Copula method, the differences between the simulated maximum temperature and the ERA5 data were minimized in more than the 85% of the studied grids.</p>

scholarly journals Construction of Dependence Structure for Rainfall Stations by Joining Time Series Models with Copula Method

2021 ◽  
Vol 17 (4) ◽  
pp. 306-320
Author(s):  
Rahmah Mohd Lokoman ◽  
Fadhilah Yusof ◽  
Nor Eliza Alias ◽  
Zulkifli Yusop
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Dependence Structure ◽  
Time Series Models ◽  
Stationary Condition ◽  
Hydrological Data ◽  
Copula Method ◽  
Stationary Conditions ◽  
Stationarity Conditions

Copula model has applied in various hydrologic studies, however, most analyses conducted does not considering the non-stationary conditions that may exist in the time series. To investigate the dependence structure between two rainfall stations at Johor Bahru, two methods have been applied. The first method considers the non-stationary condition that exists in the data, while the second method assumes stationarity in the time series data.  Through goodness-off-fit (GOF) and simulation tests, performance of both methods are compared in this study. The results obtained in this study highlight the importance of considering non-stationarity conditions in the hydrological data.

scholarly journals Dependence Measure of Daily versus Weekly Returns

2018 ◽  
Vol 7 (3.20) ◽  
pp. 329
Author(s):  
Nurul Hanis Aminuddin ◽  
Ruzanna Ab Razak ◽  
Noriszura Ismail
Keyword(s):  
Portfolio Management ◽  
Composite Index ◽  
Dependence Structure ◽  
Frequency Data ◽  
Kuala Lumpur ◽  
Bivariate Data ◽  
Dependence Measure ◽  
Daily Data ◽  
Copula Method ◽  
Daily Returns

The copula method has been popular among researchers, especially in measuring the overall dependence and extreme dependence of multivariate data. Many copula studies have been focusing on examining the correlation of bivariate daily, monthly or weekly returns to explain the co-movement between financial markets and possible financial implications on portfolio management. Differently from past studies, this paper investigates whether different frequency of bivariate data (daily and weekly returns) possesses different dependence structures. The data from Kuala Lumpur Composite Index (KLCI) and Bursa Malaysia Hijrah Shariah Index (FBMHS) for the sample period of 2008 Q1 to 2017 Q1 are used for studying the dependency. The findings from this study reveal that both daily and weekly bivariate returns have the same dependence structure but different degree of dependence. Bivariate weekly returns showed stronger dependence compared to bivariate daily returns. This paper also highlights the statistical properties of weekly and daily data. The evidence from this research draws inferences for further study that lower frequency data such as monthly or quarterly returns data may have higher degree of dependence while higher frequency data may have lower degree of dependence and different copula structure.

scholarly journals Constructing a multivariate distribution function with a vine copula: towards multivariate luminosity and mass functions

2020 ◽  
Vol 498 (3) ◽  
pp. 4365-4378
Author(s):  
Tsutomu T Takeuchi ◽  
Kai T Kono
Keyword(s):  
Distribution Function ◽  
Higher Dimensions ◽  
Mass Function ◽  
Multivariate Distribution ◽  
Dependence Structure ◽  
Systematic Method ◽  
Multidimensional Distribution ◽  
Special Cases ◽  
Copula Method ◽  
Vine Copula

ABSTRACT The need for a method to construct multidimensional distribution function is increasing recently, in the era of huge multiwavelength surveys. We have proposed a systematic method to build a bivariate luminosity or mass function of galaxies by using a copula. It allows us to construct a distribution function when only its marginal distributions are known, and we have to estimate the dependence structure from data. A typical example is the situation that we have univariate luminosity functions at some wavelengths for a survey, but the joint distribution is unknown. Main limitation of the copula method is that it is not easy to extend a joint function to higher dimensions (d > 2), except some special cases like multidimensional Gaussian. Even if we find such a multivariate analytic function in some fortunate case, it would often be inflexible and impractical. In this work, we show a systematic method to extend the copula method to unlimitedly higher dimensions by a vine copula. This is based on the pair-copula decomposition of a general multivariate distribution. We show how the vine copula construction is flexible and extendable. We also present an example of the construction of a stellar mass–atomic gas–molecular gas three-dimensional mass function. We demonstrate the maximum likelihood estimation of the best functional form for this function, as well as a proper model selection via vine copula.

Dependence structure between LIBOR rates by copula method

2014 ◽  
Vol 10 (1) ◽  
pp. 147-183
Author(s):  
Yijun Wu ◽  
Zhi Zheng ◽  
Shulin Zhou ◽  
Jingping Yang
Keyword(s):  
Dependence Structure ◽  
Copula Method

scholarly journals Comparing Different Systemic Risk Measures for European Banking System

2018 ◽  
Vol 12 (1) ◽  
pp. 35 ◽  
Author(s):  
Annalisa Di Clemente
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Risk Measures ◽  
Banking System ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
European Banking ◽  
European Banks

This research examines and compares the performances in terms of systemic risk ranking for three different systemic risk metrics based on daily frequency publicly available data, specifically: Marginal Expected Shortfall (ES), Component Expected Shortfall (CES) and Delta Conditional Value-at-Risk (ΔCoVaR). We compute ΔCoVaR, MES and CES by utilizing EVT principles for modelling marginal distributions and Student’s t copula for describing the dependence structure between every bank and the banking system. Our objective is to attest whether different systemic risk metrics detect the same banks as systemically dangerous institutions with refer to a sample of European banks over the time span 2004-2015. For each bank in the sample we also calculate three traditional market risk measures, like Market VaR, Sharpe’s beta and the correlation between every bank and the banking system (European STOXX 600 Banks Index). Another aim is to explore the existence of a link among systemic risk measures and traditional risk metrics. In addition, the classification results obtained by the different risk metrics are compared with the ranking in terms of systemic riskiness (for European banks) calculated by Financial Stability Board (2015) using end-2014 data and collected in its list of Global Systemically Important Banks (G-SIBs). With refer to the entire sample period, we find a good coherence of ranking results among the three different systemic risk metrics, in particular between CES and ΔCoVaR. Moreover, we find for MES and ΔCoVaR a strong linkage with beta and correlation metrics respectively. Finally, CES metric shows the highest level of concordance with the list of G-SIBs by FSB with refer to European banks.

scholarly journals Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study

2018 ◽  
Author(s):  
Yiguo Sun ◽  
Ximing Wu
Keyword(s):  
Tail Dependence ◽  
Dependence Structure ◽  
Market Returns ◽  
Dependence Relation ◽  
Feedback Effects ◽  
Conditional Dependence ◽  
Market Return ◽  
Volatility Index ◽  
Copula Method ◽  
Volatility Feedback

This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. We find that: (a) the two series exhibit strong, negative, extreme tail dependence; (b) the negative dependence is stronger in extreme bearish markets than in extreme bullish markets; (c) the dependence gradually weakens as the market return moves toward the center of its distribution, or in quiet markets. The unique dependence structure supports the VIX as a barometer of markets' mood in general. Moreover, applying the proposed method to the S&P 500 returns and the implied variance (VIX²), we find that the nonparametric leverage effect is much stronger than the nonparametric volatility feedback effect, although, in general, both effects are weaker than the dependence relation between the market returns and the log-increments of the VIX.

ASYMPTOTICS FOR SYSTEMIC RISK WITH DEPENDENT HEAVY-TAILED LOSSES

2021 ◽  
pp. 1-35
Author(s):  
Jiajun Liu ◽  
Yang Yang
Keyword(s):  
Asymptotic Behavior ◽  
Significant Role ◽  
Systemic Risk ◽  
Risk Model ◽  
Dependence Structure ◽  
Entire System ◽  
Simulation Experiments ◽  
Heavy Tailed ◽  
Ideal Framework

Abstract Systemic risk (SR) is considered as the risk of collapse of an entire system, which has played a significant role in explaining the recent financial turmoils from the insurance and financial industries. We consider the asymptotic behavior of the SR for portfolio losses in the model allowing for heavy-tailed primary losses, which are equipped with a wide type of dependence structure. This risk model provides an ideal framework for addressing both heavy-tailedness and dependence. As some extensions, several simulation experiments are conducted, where an insurance application of the asymptotic characterization to the determination and approximation of related SR capital has been proposed, based on the SR measure.

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