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

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.

Testing for a common Phillips curve in common monetary area of Southern Africa

PurposeThis paper sets out to investigate whether the four members of the common monetary area (CMA) regime experience similar inflation-unemployment dynamics as explained by the Phillips Curve phenomenon.Design/methodology/approachThis study uses a combination of seemingly unrelated regression (SUR) and Copula based marginal regression techniques to investigate existence of a common Phillips curve (PC) between members of the CMA. Model estimation was done using country specific annual time series data for inflation, unemployment and imports spanning from 1980 to 2014.FindingsWe find evidence of contemporaneous correlation between the residuals of individual CMA PC equations and a statistically significant trade-off between inflation and unemployment for all CMA countries. Wald test results of cross-equation restrictions reveal a 9.94% chance of a common unemployment coefficient for CMA countries.Originality/valueTogether, the results of the SUR and Gaussian Copula techniques provide mixed and inconclusive evidence to support the existence of a common PC among CMA member states. This study is the first of its kind in examining this phenomenon for currency board regimes like CMA, and one of the very few among emerging market economies.

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

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.