scholarly journals Nonstationary Analysis for Bivariate Distribution of Flood Variables in the Ganjiang River Using Time-Varying Copula

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 746
Author(s):  
Tianfu Wen ◽  
Cong Jiang ◽  
Xinfa Xu
Keyword(s):  
Suspended Sediment ◽  
Flood Event ◽  
Dependence Structure ◽  
Copula Model ◽  
Time Varying ◽  
Bivariate Distributions ◽  
Marginal Distributions ◽  
The Mean ◽  
Water Stage ◽  
Dependence Structures

Nonstationarity of univariate flood series has been widely studied, while nonstationarity of some multivariate flood series, such as discharge, water stage, and suspended sediment concentrations, has been studied rarely. This paper presents a procedure for using the time-varying copula model to describe the nonstationary dependence structures of two correlated flood variables from the same flood event. In this study, we focus on multivariate flood event consisting of peak discharge (Q), peak water stage (Z) and suspended sediment load (S) during the period of 1964–2013 observed at the Waizhou station in the Ganjiang River, China. The time-varying copula model is employed to analyze bivariate distributions of two flood pairs of (Z-Q) and (Z-S). The main channel elevation (MCE) and the forest coverage rate (FCR) of the basin are introduced as the candidate explanatory variables for modelling the nonstationarities of both marginal distributions and dependence structure of copula. It is found that the marginal distributions for both Z and S are nonstationary, whereas the marginal distribution for Q is stationary. In particular, the mean of Z is related to MCE, and the mean and variance of S are related to FCR. Then, time-varying Frank copula with MCE as the covariate has the best performance in fitting the dependence structures of both Z-Q and Z-S. It is indicated that the dependence relationships are strengthen over time associated with the riverbed down-cutting. Finally, the joint and conditional probabilities of both Z-Q and Z-S obtained from the best fitted bivariate copula indicate that there are obvious nonstationarity of their bivariate distributions. This work is helpful to understand how human activities affect the bivariate flood distribution, and therefore provides supporting information for hydraulic structure designs under the changing environments.

scholarly journals Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice

2019 ◽  
Vol 23 (3) ◽  
pp. 1683-1704 ◽  
Author(s):  
Cong Jiang ◽  
Lihua Xiong ◽  
Lei Yan ◽  
Jianfan Dong ◽  
Chong-Yu Xu
Keyword(s):  
Return Period ◽  
Flood Event ◽  
Exceedance Probability ◽  
Dependence Structure ◽  
Engineering Practice ◽  
Time Varying ◽  
Marginal Distributions ◽  
Hydrologic Design ◽  
The Given ◽  
Nonstationary Conditions

Abstract. Multivariate hydrologic design under stationary conditions is traditionally performed through the use of the design criterion of the return period, which is theoretically equal to the average inter-arrival time of flood events divided by the exceedance probability of the design flood event. Under nonstationary conditions, the exceedance probability of a given multivariate flood event varies over time. This suggests that the traditional return-period concept cannot apply to engineering practice under nonstationary conditions, since by such a definition, a given multivariate flood event would correspond to a time-varying return period. In this paper, average annual reliability (AAR) was employed as the criterion for multivariate design rather than the return period to ensure that a given multivariate flood event corresponded to a unique design level under nonstationary conditions. The multivariate hydrologic design conditioned on the given AAR was estimated from the nonstationary multivariate flood distribution constructed by a dynamic C-vine copula, allowing for time-varying marginal distributions and a time-varying dependence structure. Both the most-likely design event and confidence interval for the multivariate hydrologic design conditioned on the given AAR were identified to provide supporting information for designers. The multivariate flood series from the Xijiang River, China, were chosen as a case study. The results indicated that both the marginal distributions and dependence structure of the multivariate flood series were nonstationary due to the driving forces of urbanization and reservoir regulation. The nonstationarities of both the marginal distributions and dependence structure were found to affect the outcome of the multivariate hydrologic design.

scholarly journals Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice

2018 ◽  
Author(s):  
Cong Jiang ◽  
Lihua Xiong ◽  
Lei Yan ◽  
Jianfan Dong ◽  
Chong-Yu Xu
Keyword(s):  
Return Period ◽  
Flood Event ◽  
Exceedance Probability ◽  
Dependence Structure ◽  
Engineering Practice ◽  
Time Varying ◽  
Marginal Distributions ◽  
Hydrologic Design ◽  
The Given ◽  
Nonstationary Conditions

Abstract. The multivariate hydrologic design under stationary condition is traditionally done through using the design criterion of return period, which theoretically equals to the average inter-arrival time of flood events divided by the exceedance probability of the design flood event. Under nonstationary conditions the exceedance probability of a given multivariate flood event would vary over time. This suggests that the traditional return period concept could not apply to the engineering practice under nonstationary conditions, since by such a definition a given multivariate flood event would correspond to a time-varying return period. In this paper, instead of return period, average annual reliability (AAR) is employed as the criterion for multivariate design, to ensure a given multivariate flood event would correspond to a unique design level under nonstationary conditions. The multivariate hydrologic design conditioned on the given ARR is estimated from the nonstationary multivariate flood distribution constructed by a dynamic C-vine copula, allowing for time-varying marginal distributions and dependence structure. Both the most-likely design event and confidence interval for the multivariate hydrologic design conditioned on the given AAR are identified to provide visual supporting information for designers. The multivariate flood series from the Xijiang River, China are chosen to perform a case study. The results indicate that both the marginal distributions and dependence structure of the multivariate flood series are nonstationary due to the driving force of urbanization and reservoir regulation. The nonstationarities of both the marginal distributions and dependence structure can affect the outcome of the multivariate hydrologic design.

Volatility and dependence structures of Latin American stock markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guilherme Cardoso ◽  
Karem Ribeiro ◽  
Luciano Carvalho
Keyword(s):  
Latin America ◽  
Latin American ◽  
Stock Markets ◽  
Dependence Structure ◽  
Subprime Crisis ◽  
Time Varying ◽  
Content Type ◽  
Macroeconomic Effects ◽  
The Relationship ◽  
Dependence Structures

PurposeRisk management has been crucial to investors and regulators for pursuing market diversification opportunities and developing strategies to ensure market stability. This study examines the dependence structures of volatility, related to co-movements and macroeconomic effects, among Latin American stock markets and the risk–return spectrum benefits in the Latin American market using time-varying returns and volatility forecasts within a multivariate structure.Design/methodology/approachThe sample comprised the largest stock markets in Latin America during the period from January 2000 to December 2017 and copulas and multivariate models were applied.FindingsThe results indicated that the copula with the best fit for modeling the dependence structure of the markets was symmetric Joe-Clayton with time-varying parameters. The dependence volatility structure was higher in the positive (upper tail) than in the negative (lower tail) returns, which may indicate that the Latin American markets had diversification benefits during downturns. Evidence of market coupling was found during times of the global crisis (subprime crisis) in Latin America. The presence of monetary and temporal effects over the dependence structures suggests that investors may obtain gains in a multivariate structure with copula distributions.Originality/valueThe findings will be of interest to researchers and practitioners for several reasons. First, this study contributes to the growing literature on the relationship between market dependence and volatility. Second, it indicates that the Latin American markets may present diversification advantages during downturns. Third, it informs the influence of macroeconomic effects on Latin American markets. The models that included the nonnormal and asymmetric characteristics of the financial market yielded better results in terms of less information loss and data adherence.

scholarly journals Combined risk assessment of nonstationary monthly water quality based on Markov chain and time-varying copula

2016 ◽  
Vol 75 (3) ◽  
pp. 693-704 ◽  
Author(s):  
Wei Shi ◽  
Jun Xia
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Marginal Distribution ◽  
Dependence Structure ◽  
Time Varying ◽  
Class V ◽  
Marginal Distributions ◽  
Huai River ◽  
Risk Probability ◽  
Class Iv

Water quality risk management is a global hot research linkage with the sustainable water resource development. Ammonium nitrogen (NH3-N) and permanganate index (CODMn) as the focus indicators in Huai River Basin, are selected to reveal their joint transition laws based on Markov theory. The time-varying moments model with either time or land cover index as explanatory variables is applied to build the time-varying marginal distributions of water quality time series. Time-varying copula model, which takes the non-stationarity in the marginal distribution and/or the time variation in dependence structure between water quality series into consideration, is constructed to describe a bivariate frequency analysis for NH3-N and CODMn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class Vw, Class IV and Class III will occur easily in the water body of Bengbu Sluice. Both marginal distribution and copula models are nonstationary, and the explanatory variable time yields better performance than land cover index in describing the non-stationarities in the marginal distributions. In modelling the dependence structure changes, time-varying copula has a better fitting performance than the copula with the constant or the time-trend dependence parameter. The largest synchronous encounter risk probability of NH3-N and CODMn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH3-N and CODMn is inferior to class V and class IV water quality standards, respectively.

scholarly journals A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons

2014 ◽  
Vol 26 (9) ◽  
pp. 2025-2051 ◽  
Author(s):  
Babak Shahbaba ◽  
Bo Zhou ◽  
Shiwei Lan ◽  
Hernando Ombao ◽  
David Moorman ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Bayesian Model ◽  
Latent Variable ◽  
Process Model ◽  
Logistic Function ◽  
Dependence Structure ◽  
Joint Probability Distribution ◽  
Copula Model ◽  
Marginal Distributions ◽  
Firing Rates

We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows. The nonparametric component (i.e., the gaussian process model) provides a flexible framework for modeling the underlying firing rates, and the parametric component (i.e., the copula model) allows us to make inferences regarding both contemporaneous and lagged relationships among neurons. Using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of a dependence structure among variables. Our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high-dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas.

scholarly journals A Time-Varying Multivariate Noncentral Contaminated Normal Copula Model and Its Application to the Visualized Dependence Analysis of Hong Kong Stock Markets

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Zhenyu Xiao ◽  
Jie Wang ◽  
Teng Yuan Cheng ◽  
Kuiran Shi
Keyword(s):  
Hong Kong ◽  
Financial Market ◽  
Stock Markets ◽  
Financial Data ◽  
Copula Model ◽  
Dependence Analysis ◽  
Time Varying ◽  
Conditional Correlation ◽  
Normal Copula ◽  
Dependence Structures

Financial data usually have the features of complexity and interdependence structure, such as asymmetric, tail, and time-varying dependence. This study constructs a new multivariate skewed fat-tailed copula, namely, noncentral contaminated normal (NCCN) copula, to analyze the dependent structure of financial market data. The dynamic conditional correlation (DCC) model is also incorporated into constructing the time-varying NCCN copula model. This study comprehensively examines the effects of the DCC-NCCN copula and related models on fitting dependence structures of Hong Kong stock markets. The results show that the DCC-NCCN copula model can better depict the dependence structures of returns. Considering the flexibility and complexity, the DCC-NCCN copula model is a relatively ideal, time-varying, multivariate skewed fat-tailed copula model.

scholarly journals Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C-Vine Copula and Time-Varying t-Copula

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huizi Ma ◽  
Lin Lin ◽  
Han Sun ◽  
Yue Qu
Keyword(s):  
Spillover Effect ◽  
Spillover Effects ◽  
Dependence Structure ◽  
Copula Model ◽  
Time Varying ◽  
Financial Products ◽  
T Copula ◽  
Dependence Relationship ◽  
Vine Copula ◽  
Risk Spillover Effects

Internet money funds (IMFs) are the most widely involved products in the Internet financial products market. This research utilized the C-vine copula model to study the risk dependence structure of IMFs and then introduces the time-varying t-copula model to analyze the risk spillover of diverse IMFs. The results show the following: (1) The risks of Internet-based IMFs, bank-based IMFs, and fund-based IMFs have obvious dependence structure, and the degree of risk dependence among different categories of IMFs is significantly different. (2) There are risk spillover effects among diverse IMFs, and their risk dependence relationship is characterized by cyclical feature. (3) The risk spillover effect among diverse IMFs is pronounced, and dynamic risk dependence between IMFs is characterized by synchronization.

Dependence structure between money and economic activity: A Markov-switching copula VEC approach

2021 ◽  
pp. 1-17
Author(s):  
Apostolos Serletis ◽  
Libo Xu
Keyword(s):  
Error Correction ◽  
Economic Activity ◽  
Regime Switching ◽  
Markov Switching ◽  
Tail Dependence ◽  
Error Correction Model ◽  
Dependence Structure ◽  
Copula Models ◽  
Correction Model ◽  
Dependence Structures

Abstract This paper examines correlation and dependence structures between money and the level of economic activity in the USA in the context of a Markov-switching copula vector error correction model. We use the error correction model to focus on the short-run dynamics between money and output while accounting for their long-run equilibrium relationship. We use the Markov regime-switching model to account for instabilities in the relationship between money and output, and also consider different copula models with different dependence structures to investigate (upper and lower) tail dependence.

scholarly journals Asymmetric Dependence Analysis of International Crude Oil Spot and Futures Based on the Time Varying Copula-GARCH

2015 ◽  
Vol 8 (1) ◽  
pp. 463-467
Author(s):  
He Xin ◽  
Zhang Jun
Keyword(s):  
Parameter Estimation ◽  
Crude Oil ◽  
Significant Influence ◽  
Tail Dependence ◽  
Sliding Window ◽  
Dependence Structure ◽  
Dependence Analysis ◽  
Time Varying ◽  
Daily Return ◽  
Asymmetric Dependence

Taking daily return of international crude oil spot and futures as sample, this paper analyzed the time varying and asymmetric dependence structure of them by time varying Copula-GARCH model based on sliding window and semi parameter estimation. This paper analyzed the regular changing between dependence structure of crude oil spot and futures and the return fluctuation, and confirmed that there is significant time varying asymmetric tail dependence. This paper found that the size of the sliding window had no significant influence on the conclusion, and the data of weekly return is more suitable for analysis of the trend of dependence structure of spot.

scholarly journals Predicting the early depleting transmission dynamics of COVID-19: A time-varying SIR model

2020 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M Peariasamy ◽  
Balvinder Singh Gill ◽  
Sarbhan Singh Lakha Singh ◽  
Bala Murali Sundram ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Model Fitting ◽  
Sir Model ◽  
Transmission Dynamics ◽  
Time Varying ◽  
Force Of Infection ◽  
Planning And Control ◽  
The Mean ◽  
And Control ◽  
Absolute Percent Error

Abstract The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I), and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, βt, and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily, and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4·7% each day with a decreased capacity of 40%. For 7–day and 14–day projections, the modified SIR model accurately predicted I total, I, and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.

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