Multifrequency-based non-linear approach to analyzing implied volatility transmission across global financial markets

Abstract

Research background: The contagious impact of the COVID-19 pandemic has heightened financial market's volatility, nonlinearity, asymmetric and nonstationary dynamics. Hence, the existing relationship among financial assets may have been altered. Moreover, the level of investor risk aversion and market opportunities could also alter in the pandemic. Predictably, investors in the heat of the moment are concerned about minimizing losses. In order to determine the level of hedge risks between implied volatilities in the COVID-19 pandemic through information flow, it is required to take into account the increased vagueness of economic projections as well as the increased uncertainty in asset values as a result of the pandemic. Purpose of the article: The study aims to examine the transmission of information between the VIX-implied volatility index for S&P 500 and fifteen other implied volatility indices in the COVID-19 pandemic. Methods: We relied on daily changes in the VIX and fifteen other implied volatility indices from commodities, currencies, and stocks. The study employed the improved complete ensemble empirical mode decomposition with adaptive noise which is in line with the heterogeneous expectations of market participants to denoise the data and extract intrinsic mode functions (IMFs). Subsequently, we clustered the IMFs based on common features into high, low, and medium frequencies. The analysis was carried out using Rényi transfer entropy (RTE), which allowed for the evaluation of both linear and non-linear, as well as varied distributions of the market dynamics. Findings & value added: Findings from the RTE revealed a bi-directional flow of negative information amid the VIX and each of the volatility indices, particularly in the long term. We found this behavior of the markets to be consistent at varying levels of investors' risk aversion. The findings help investors with their portfolio strategies in the time of the pandemic, which has resulted in fluctuating levels of risk aversion. Our findings characterize global financial markets to be ?non-linear heterogeneous evolutionary systems?. The results also lend support to the emerging delayed volatility of market competitiveness and external shocks hypothesis.

Conditional effects of local and global risk factors on the co-movements between economic growth and inflation: Insights into G8 economies

“…Accordingly, the roles of exchange rate and interest rate can be factored to contribute to the understanding of the macroeconomic modelling. Considering the rapid fluctuations in financial time series leading to their nonlinearities, analyses can also be performed using robust decomposition techniques coupled with the quantification of the amount of information that flows through the macroeconomic variables [ [76] , [77] , [78] , [79] , [80] , [81] , [82] ]. The sample period is limited to August 2021 due to limited data availability for some variables for the G8 nations but enough for the subject matter to be investigated.…”

The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

“…that have the potential to make these dairy prices more volatile. On the one hand, the post-2020 time period can be associated with increasing levels of risk aversion (Boateng et al, 2022), whereas other authors point out increased price volatility and interconnectedness among financial markets starting in early 2020 (Zeng et al, 2023). In our study, we employ the Granger causality test for the causal effects of speculation on price movements and the GARCH model to test for its impact on conditional return volatility including the post-2020 timeframe.…”

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

“…The most pronounced effect of the S&P 500 index decline is the expansion of the unemployment rate. Nonetheless, investors need to consider the anticipated developments of the unemployment rate (Hu et al 2018;Jiao and Ye 2022), interest rate (Bhar et al 2015;Fougue and Saporito 2018), inflation rate (Pineiro-Chousa et al 2018;Golitsis et al 2022;Soydemir et al 2017), and volatility index (Belas and Rahman 2023;Boateng et al 2022;Biardi et al 2020), when deciding whether to invest in the S&P 500 stock index as also confirmed by the specified studies. Moreover, investors should also access the historical data, trading volumes, and some other relevant market indicators, e.g., evaluate the risk characteristics of the active strategy compared to the passive index.…”