Changes In Unconditional Variance Research Articles

The performance of the euro area banking system: the pandemic in perspective

AbstractThis research explores the performance of the euro area banking system between 2002:Q3 and 2021:Q1 using banks’ return on assets as a gauge. It finds that despite the Covid-19 pandemic, the banking sector’s performance has not been negatively affected in a statistically significant way. In addition, it shows that while the global financial crisis and the sovereign crisis both introduced significant uncertainty in the performance of the banking system in the euro area, such uncertainty has not been replicated during the worst part of the Covid-19 pandemic. From a purely technical viewpoint, uncertainty in the banking system’s performance in the euro area highlights the relevance of using methodological approaches that control for the endogeneity of most bank-specific determinants of return on assets and that are robust to changes in unconditional variance due to regime changes, to future random shocks or both.

Testing Mean Stability of Heteroskedastic Time Series

Time series models are often fitted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fitting models with spurious dynamics that lead to unsupported and controversial conclusions about time dependence, causality, and the effects of unanticipated shocks. In spite of what may seem as obvious differences between a time series of independent variates with changing variance and a stationary conditionally heteroskedastic (GARCH) process, such processes may be hard to distinguish in applied work using basic time series diagnostic tools. We develop and study some practical and easily implemented statistical procedures to test the mean and variance stability of uncorrelated and serially dependent time series. Application of the new methods to analyze the volatility properties of stock market returns leads to some unexpected surprising findings concerning the advantages of modeling time varying changes in unconditional variance.

Stylized Facts and Simulating Long Range Financial Data

We propose a new method (implemented in an R-program) to simulate long-range daily stock-price data. The program reproduces various stylized facts much better than various parametric models from the extended GARCH-family. In particular, the empirically observed changes in unconditional variance are truthfully mirrored in the simulated data.