Income inequality and financial crises:evidence from the bootstrap rolling window

This study aims to investigate the validity of the Rajan hypothesis,which argues that increasing income inequality plays a key role in the outbreak of financial crises.The relationship between income inequality and credit booms are examined in 10 developed countries:Australia,Canada,Denmark,Finland,France,the United Kingdom,Japan,Norway,Sweden,and the United States.In doing so,a bootstrap rolling-window estimation procedure is used to detect any possible causal link between inequality and credit booms in financial crisis sub-periods.The results reveal that the Rajan hypothesis is supported for the 1989 crisis in Australia,the 1991 and 2007 crises in the United Kingdom,and the 1929 and 2007 crises in the United States.Therefore,increasing income inequality has positive predictive power on credit booms in Anglo-Saxon countries.However,the hypothesis is not confirmed for Scandinavian and continental European countries.Our study is novel in its use of the bootstrap rolling-window procedure,which allows us to detect the possible relationship between inequality and credit booms in financial crises.The findings suggest that a progressive taxation policy or investments to accumulate human capital and increase the labor force are more beneficial than temporary solutions.

Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Stock profiling using time–frequency‑varying systematic risk measure

This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness.Furthermore,we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics.We use a daily panel of French stocks from 2012 to 2022.Results show that varying systematic risk varies in time and frequency,and that its short and long-run evolutions differ.We observe differences in short and long dynamics,indicating that a stock’s betas differently fluctuate to early announcements or signs of events.However,short-run and long-run betas exhibit similar dynamics during persistent shocks.Betas are more volatile during times of crisis,resulting in greater or lesser robustness of risk profiles.Significant differences exist in short-run and longrun risk profiles,implying a different asset allocation.We conclude that the standard CAPM assumes short-run investment.Then,investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.

High frequency multiscale relationships among major cryptocurrencies:portfolio management implications

This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin,Ethereum,Monero,Dash,Ripple,and Litecoin.We apply nonlinear Granger causality and rolling window wavelet correlation(RWCC)to 15 min-data.Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies,especially between Bitcoin,Ethereum,and Monero.The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs.We advance evidence to improve portfolio risk assessment,and hedging strategies.

Cryptocurrencies,gold,and WTI crude oil market efficiency:a dynamic analysis based on the adaptive market hypothesis

This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies(Bitcoin,Ethereum,and Ripple),gold,and West Texas Intermediate(WTI)crude oil.The data coverage of daily returns was from August 2015 to April 2019.We applied two alternative tests to examine linear and nonlinear dependency,i.e.,automatic portmanteau and generalized spectral tests.The analysis of observed results validated the adaptive market hypothesis(AMH)in all markets,but the degree of adaptability between the data was different.In this study,we also analyzed the existence of evolutionary behavior in the market.To achieve this goal,we checked the results by applying the rolling-window method with three different window lengths(50,100,and 150 days)on the test statistics,which was consistent with the findings of AMH.

Sub-optimality analysis of mobile robot rolling path planning

Rolling planning is an efficient method for path planning in uncertain environment. In this paper, the general principle and algorithm of mobile robot path planning based on rolling windows are studied. The sub-optimality of rolling path planning is analyzed in details and explained with a concrete example.