Part IV: Dynamic Copula Models

2014 ◽  
pp. 187-190
Keyword(s):  
Copula Models ◽  
Dynamic Copula

scholarly journals Dynamic copula models for the spark spread

2011 ◽  
Vol 11 (3) ◽  
pp. 407-421 ◽  
Author(s):  
Fred Espen Benth ◽  
Paul C. Kettler
Keyword(s):  
Copula Models ◽  
Dynamic Copula

Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evolution

2015 ◽  
Author(s):  
Matthias D. Aepli ◽  
Karl Frauendorfer ◽  
Roland FFss ◽  
Florentina Paraschiv
Keyword(s):  
Parameter Estimation ◽  
Copula Models ◽  
Dynamic Copula

Bivariate option pricing using dynamic copula models

2005 ◽  
Vol 37 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Rob W.J. van den Goorbergh ◽  
Christian Genest ◽  
Bas J.M. Werker
Keyword(s):  
Option Pricing ◽  
Copula Models ◽  
Dynamic Copula

LOCAL ESTIMATION OF DYNAMIC COPULA MODELS

2010 ◽  
Vol 13 (02) ◽  
pp. 241-258 ◽  
Author(s):  
BEATRIZ V. M. MENDES ◽  
EDUARDO F. L. DE MELO
Keyword(s):  
Value At Risk ◽  
Risk Measures ◽  
Local Maximum ◽  
Likelihood Estimation ◽  
Copula Models ◽  
Explanatory Variables ◽  
Empirical Estimates ◽  
Dynamic Copula ◽  
Proposed Model ◽  
Local Maximum Likelihood

It has been empirically verified that the strength of dependence in stock markets usually rises with volatility. In this paper we exploit this stylized fact combined with local maximum likelihood estimation of copula models to analyze the dynamic joint behavior of series of financial log returns. Explanatory variables based on the estimated GARCH volatilities are considered as potential regressors for explaining the dynamics in the copula parameters. The proposed model can assess and discriminate how much of the strength of dependence is due just to the time-varying volatility. The final local-parametric estimates may be used to compute risk measures, to simulate portfolio behavior, and so on. We illustrate our methods using two American indexes. Results indicate that volatility does affect the strength of dependence. The in-sample Value-at-Risk based on the dynamic model outperforms those based on the empirical estimates.

Can Technical Indicators Provide Information for Future Volatility: International Evidence

2020 ◽  
Vol 8 (1) ◽  
pp. 53-66
Author(s):  
Yafeng Shi ◽  
Xiangxing Tao ◽  
Yanlong Shi ◽  
Nenghui Zhu ◽  
Tingting Ying ◽  
...  
Keyword(s):  
Moving Average ◽  
Emerging Market ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Copula Models ◽  
Technical Indicators ◽  
International Evidence ◽  
Dynamic Copula ◽  
Market Indexes ◽  
Stock Indexes

AbstractWe employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day, where the volatility is measured by daily realized volatility. Our empirical results, based on long samples of 8 well-known stock indexes, suggest that a significant and asymmetric tail dependence between the technical indicators based on moving average and the next day volatility. The level of dependence change over time in a persistent manner. And the dependence structure presents some distinct differences between emerging market indexes and developed market indexes. These results indicate that the technical indicators can provide information on the next day volatility at extremes, and are less informative at normal market.

scholarly journals Multivariate Option Pricing Using Dynamic Copula Models

2003 ◽  
Author(s):  
Rob W. J. van den Goorbergh ◽  
Christian Genest ◽  
Bas J. M. Werker
Keyword(s):  
Option Pricing ◽  
Copula Models ◽  
Dynamic Copula

scholarly journals Dynamic copula models and high frequency data

2015 ◽  
Vol 30 ◽  
pp. 120-135 ◽  
Author(s):  
Irving De Lira Salvatierra ◽  
Andrew J. Patton
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Copula Models ◽  
Dynamic Copula
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