Correction to: Volatility impacts on the European banking sector: GFC and COVID-19.

[This corrects the article DOI: 10.1007/s10479-022-04523-8.].

Volatility impacts on the European banking sector: GFC and COVID-19.

This paper analyses the volatility transmission between European Global Systematically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe's GSIBs and the world's most prominent measure of market "fear", the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile-where extreme negative bank returns are present-jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics.

Information Transfer between Stock Market Sectors: A Comparison between the USA and China.

Information diffusion within financial markets plays a crucial role in the process of price formation and the propagation of sentiment and risk. We perform a comparative analysis of information transfer between industry sectors of the Chinese and the USA stock markets, using daily sector indices for the period from 2000 to 2017. The information flow from one sector to another is measured by the transfer entropy of the daily returns of the two sector indices. We find that the most active sector in information exchange (i.e., the largest total information inflow and outflow) is the non-bank financial sector in the Chinese market and the technology sector in the USA market. This is consistent with the role of the non-bank sector in corporate financing in China and the impact of technological innovation in the USA. In each market, the most active sector is also the largest information sink that has the largest information inflow (i.e., inflow minus outflow). In contrast, we identify that the main information source is the bank sector in the Chinese market and the energy sector in the USA market. In the case of China, this is due to the importance of net bank lending as a signal of corporate activity and the role of energy pricing in affecting corporate profitability. There are sectors such as the real estate sector that could be an information sink in one market but an information source in the other, showing the complex behavior of different markets. Overall, these findings show that stock markets are more synchronized, or ordered, during periods of turmoil than during periods of stability.

Complexity in financial asset returns: Evidence from the compass rose.

The compass rose pattern in financial data may indicate the presence of a nonlinear, possibly chaotic, data generating mechanism. The analysis of three key financial asset and denoised returns, gold, the Great British Pound/US dollar spot exchange rate, and the Standard & Poor's 500 stock index, reveals that over four equivalent subperiods, from 1996 to 2015, the compass rose pattern changes. This finding provides an opportunity to establish how noise affects financial time series. We conclude that the compass rose pattern is unlikely the product of an underlying nonlinear structure, since there is evidence of nonlinearity in all time periods, even those where the compass rose pattern is not evident. Therefore, the compass rose patterns, seen in the denoised data, suggest that the presence of noise masks the underlying dynamics of the asset returns.

The compass rose pattern in electricity prices.

The "compass rose pattern" is known to appear in the phase portraits, or scatter diagrams, of the high-frequency returns of financial series. We first show that this pattern is also present in the returns of spot electricity prices. Early researchers investigating these phenomena hoped that these patterns signaled the presence of rich dynamics, possibly chaotic or fractal in nature. Although there is a definite autoregressive and conditional heteroscedasticity structure in electricity returns, we find that after simple filtering no pattern remains. While the series is non-normal in terms of their distribution and statistical tests fail to identify significant chaos, there is evidence of fractal structures in periodic price returns when measured over the trading day. The phase diagram of the filtered returns provides a useful visual check on independence, a property necessary for pricing and trading derivatives and portfolio construction, as well as providing useful insights into the market dynamics.