scholarly journals Time series copula models using d-vines and v-transforms

2021 ◽  
Author(s):  
Martin Bladt ◽  
Alexander J. McNeil
Keyword(s):  
Time Series ◽  
Copula Models

scholarly journals Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2156
Author(s):  
George Pouliasis ◽  
Gina Alexandra Torres-Alves ◽  
Oswaldo Morales-Napoles
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Copula Models ◽  
Multiple Processes ◽  
Spatial Dependencies ◽  
Probabilistic Dependence ◽  
Temporal And Spatial ◽  
Vine Copula ◽  
Synthetic Time Series ◽  
Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

scholarly journals Dynamic Factor Copula Models with Estimated Cluster Assignments

2021 ◽  
Vol 2021 (026) ◽  
pp. 1-52
Author(s):  
Dong Hwan Oh ◽  
◽  
Andrew J. Patton ◽  
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
High Dimensional ◽  
Dynamic Factor ◽  
Finite Sample ◽  
Copula Models ◽  
Finite Sample Properties ◽  
Out Of Sample ◽  
Individual Variables

This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.

scholarly journals Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

2011 ◽  
Vol 15 (7) ◽  
pp. 2401-2419 ◽  
Author(s):  
P. Laux ◽  
S. Vogl ◽  
W. Qiu ◽  
H. R. Knoche ◽  
H. Kunstmann
Keyword(s):  
Time Series ◽  
Complex Terrain ◽  
Large Scale ◽  
Extreme Values ◽  
Regional Climate ◽  
Tail Dependence ◽  
Copula Models ◽  
Climate Simulations ◽  
Weather Situation ◽  
The Common

Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

scholarly journals Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

2011 ◽  
Vol 8 (2) ◽  
pp. 3001-3045 ◽  
Author(s):  
P. Laux ◽  
S. Vogl ◽  
W. Qiu ◽  
H. R. Knoche ◽  
H. Kunstmann
Keyword(s):  
Time Series ◽  
Complex Terrain ◽  
Large Scale ◽  
Extreme Values ◽  
Regional Climate ◽  
Copula Models ◽  
Climate Simulations ◽  
Weather Situation ◽  
The Common ◽  
The Impact

Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continous time series. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. The dependence structures between modelled and observed precipitation are conditioned on the prevailing large-scale weather situation. The impact of the altitude of the stations and their distance to the surrounding modelled grid cells is analyzed. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

scholarly journals Patent Keyword Analysis using Time Series and Copula Models

2019 ◽  
Vol 9 (19) ◽  
pp. 4071 ◽  
Author(s):  
Kim ◽  
Yoon ◽  
Hwang ◽  
Jun
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Patent Data ◽  
Analysis Model ◽  
Copula Models ◽  
Keyword Analysis ◽  
Logit Function ◽  
Patent Documents ◽  
Major Factors ◽  
Over Time

The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.

scholarly journals Co-Movement of Forex Rate and Share Price of Pakistan Stock Exchange - An Application of Copula Models

2020 ◽  
Vol V (III) ◽  
pp. 78-87
Author(s):  
Muhammad Nouman Latif ◽  
Nasir Ali ◽  
Anjum Shahzad
Keyword(s):  
Time Series ◽  
Joint Distribution ◽  
Stock Exchange ◽  
Tail Dependence ◽  
Share Price ◽  
Series Data ◽  
Dependence Structure ◽  
Complex Nature ◽  
Copula Models ◽  
The Relationship

This paper examines the relationship between the forex rate and the share price of the Pakistan Stock Exchange. The study provides additional understating of the complex nature of the relationship among bi-variate time series using the Copula model. Copula models are best suited to find the co-movement of time series data integrating the possible latent structure of the relationship through estimation of joint distribution with the help of marginal distribution of each time series variable. Alike from the traditional time series analysis, Copula models are best suited to estimate the complex relationship, specifically the tail dependence structure of joint distribution of the variables. Results of the study highlight a significant two-sided tail dependence structure between the Forex rate and share price of the Pakistan Stock Exchange.

Time-varying copula models for financial time series

2016 ◽  
Vol 48 (A) ◽  
pp. 159-180
Author(s):  
Rüdiger Kiesel ◽  
Magda Mroz ◽  
Ulrich Stadtmüller
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Local Maximum ◽  
Time Varying ◽  
Monte Carlo Simulation Study ◽  
Copula Models ◽  
Copula Theory ◽  
Statistical Homogeneity ◽  
Financial Time

AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.

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