On the Importance of CEO Effort on Firm Performance and Compensation: An Analysis Using Gaussian Copula Marginal Regression Approach

2021 ◽  
Author(s):  
John Villavicencio Mattos ◽  
Jimmy G. Torrez
Keyword(s):  
Firm Performance ◽  
Gaussian Copula ◽  
Marginal Regression ◽  
Regression Approach

scholarly journals On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1859
Author(s):  
Jong-Min Kim ◽  
Seong-Tae Kim ◽  
Sangjin Kim
Keyword(s):  
Gold Price ◽  
Gaussian Copula ◽  
Time Varying ◽  
Conditional Correlation ◽  
Copula Models ◽  
Marginal Regression ◽  
High Volatility ◽  
Asymmetric Garch ◽  
Relationship Of ◽  
The Relationship

This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.

scholarly journals Analysis of Tail Dependence between Sovereign Debt Distress and Bank Non-Performing Loans

2020 ◽  
Vol 12 (2) ◽  
pp. 747
Author(s):  
Li Liu ◽  
Yu-Min Liu ◽  
Jong-Min Kim ◽  
Rui Zhong ◽  
Guang-Qian Ren
Keyword(s):  
Sovereign Debt ◽  
Tail Dependence ◽  
Bank Loan ◽  
Gaussian Copula ◽  
Dependence Structure ◽  
Financial Policy ◽  
Copula Models ◽  
Marginal Regression ◽  
Fiscal Distress ◽  
Shed Light

We investigate the tail dependence between sovereign debt distress and bank non-performing loans (NPLs) using a large sample of developed and emerging countries in recent decades. Considering the feedback loop of sovereign debt and bank loan distress, we use three copula models to analyze the asymmetry of tail dependence structure between sovereign debt exposure and bank NPLs. We use the Gaussian copula marginal regression to control the concurrent impact of other macroeconomic variables. We provide evidence that sovereign debt indicates an important determinant of NPLs. We also find that there is tail dependence between sovereign debt distress and bank NPLs, whereas the tail dependence coefficients vary across countries. Our findings shed light on the influence of fiscal distress on bank loan distress and provide immediate implications for the design of macro prudential and financial policy.

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