Which return regime induces overconfidence behavior?Artificial intelligence and a nonlinear approach

Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes.

Nonlinear correlation between RMB internationalization and nonferrous metal prices

The correlation between Renminbi(RMB) internationalization and nonferrous metal prices was studied using the nonlinear Granger causality test and the dynamic conditional correlation-generalized autoregressive conditional heteroskedastic(DCC-GARCH) model. The results indicate that the relationship between RMB internationalization and nonferrous metal prices reflects a complex nonlinear mechanism. There was no mutual influence between RMB internationalization and nonferrous metal prices prior to the trials of the RMB settlement in the cross-border trade in July 2009. Since then, however, a bidirectional causal relationship between RMB internationalization and the price of copper and a unidirectional causal relationship from the price of aluminum to RMB internationalization were examined. In addition, due to the impact of extreme events, such as economic and financial crises, RMB internationalization and nonferrous metal prices are not always positively correlated but are rather occasionally negatively correlated.

Understanding the Soil Temperature Variability at Different Depths:Effects of Surface Air Temperature,Snow Cover,and the Soil Memory

The soil temperature(ST)is closely related to the surface air temperature(AT),but their coupling may be affected by other factors.In this study,significant effects of the AT on the underlying ST were found,and the time taken to propagate downward to 320 cm can be up to 10 months.Besides the AT,the ST is also affected by memory effects-namely,its prior thermal conditions.At deeper depth(i.e.,320 cm),the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season.At shallower layers(i.e.,<80 cm),the effects of the AT may be blocked by the snow cover,resulting in a poorly synchronous correlation between the AT and the ST.In northeastern China,this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring.Due to the thermal insulation effect of the snow cover,the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period.These findings may be instructive for better understanding ST variations,as well as land−atmosphere interactions.

The Relationship between Carbon Dioxide Emission Intensity and Economic Growth in China:Cointegration,Linear and Nonlinear Granger Causality

This study is to use cointegration, linear and non-linear Granger causality test to investigate the relationship between carbon dioxide （CO2） emissionand economic growth （GDP） in China for the period 1961-2010. Our analysis shows that CO2 emission and GDP are balanced in the long-run. The results suggest that there is evidence that economic development can improve environmental degradation in the long-run. Moreover, the result of linear and non-linear Granger causality test indicates a long-run unidirectional causality running from GDP to CO2 emissions. The study suggests that in the long run, economic growth may have an adverse effect on the CO2 emissions in China. Government should take into account the environment in their current policies, which may be of great importance for policy decision-makers to develop economic policies to preserve economic growth while curbing of carbon emissions.

TESTING LINEAR AND NONLINEAR GRANGER CAUSALITY IN CSI300 FUTURES AND SPOT MARKETS BASED ON NEW CONCEPTS OF NONLINEAR POSITIVE/NEGATIVE SPILLOVER

supported by the National Natural Science Foundation of China under Grant Nos.71125005 70871108 and 70810107020;; Outstanding Talents Funds of Organization Department Beijing Committee of CPC

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.

Data-based prediction and causality inference of nonlinear dynamics

Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.