A New EEMD‐Effective Transfer Entropy‐Based Methodology for Exchange Rate Market Information Transmission in Southern Africa Development Community

Abstract

The desire to form monetary unions among regional blocs in Africa has necessitated the need to assess the degree of financial systems interdependencies in Africa economic blocs for their suitability to have harmonised economic policies of eventual monetary unions. In this regard, SADC has pursued policies to harmonise and integrate its financial system as a precursor to its intended monetary union. However, the ensuing risk among exchange rates of economies in SADC is presumed to rise during severe uncertainties. This study examines the degree of asymmetry and nonlinear directional causality between exchange rates in SADC in the frequency domain. We employ both the ensemble empirical mode decomposition (EEMD) and the Rényi effective transfer entropy techniques to investigate the multiscale information that might be disregarded and further quantify the directional flow of information. Analysis of the study is presented for four frequency-domains: high-, medium-, and low frequencies, representing short-, medium-, and long-terms, respectively, in addition to the residue (fundamental feature). We find a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between exchange rates in SADC for the sampled period. The study reveals a significant positive information flow in the high frequency, but negative flow in the medium and low frequencies. In addition, we gauge a bidirectional significant negative information flow within all the 15 economies for the residue. This suggests a higher risk of uncertainties in exchange rates of SADC. Our findings for low probability events at multiscales have implications for the direction of the future of the SADC monetary union. This calls for further sustained policy harmonisation in the region.

Spillovers and contagion between BRIC and G7 markets: New evidence from time-frequency analysis

“…The DY-12 spillover index rather assumes a time-invariant connectedness between assets, suggesting that investors respond to spillovers similarly and that their response is unaffected by investment horizons. This is contrary to the heterogeneous markets hypothesis (HMH) [ 23 – 25 , 33 ] and adaptive market hypothesis (AMH) of Lo [ 22 ]. To get around this restriction, we employ the BK-18 spillover index, which is based on heterogeneous shock frequency responses.…”

“…Heterogeneous and time-based investor behaviour is reflected in market pricing since the market does not function in solitude. Theories that support this occurrence are Lo’s [ 22 ] AMH and the HMH recently propagated by Adam, Gyamfi, Kyei, Moyo, and Gill [ 25 ], Bossman [ 23 ], Bossman et al [ 24 ], and Owusu Junior, Frimpong et al [ 34 ]. By analysing historical and present events, the HMH hypothesises that distinct economic actors make investment decisions over different time horizons depending on their risk and return preferences.…”

“…In a static spillover paradigm, Zhang et al [ 27 ] analyse the connectedness of G7 and BRIC economies. However, the operability of markets and investor behaviour are shown to follow dynamic patterns and, hence, evidence from static connectedness measures may be insufficient for time-based investors operating across the short-, medium-, and long-term periods [ 5 , 11 , 23 – 25 , 34 ]. Analysis of safe-haven assets [ 39 ], risk-aversion comovements [ 40 ], spillovers dynamics between developed and emerging economies, and commodity futures [ 28 ] have all been studied with much emphasis on G7 markets and to some extent, BRIC economies.…”

“…This study examines the matter of spillovers and contagion effects between the BRIC index and its members, and the G7 markets while taking into account the impacts of the adaptability [ 22 ] and heterogeneity [ 23 – 25 ] of market participants, non-linearity and asymmetric qualities (of spillovers) that have not been properly considered in prior studies. As a result, the primary goal of this research is to look at the time-varying spillovers between those four fast-growing nations, their market bloc index, and the group of seven (G7) major developed stock markets.…”

Research on the MEG of Depression Patients Based on Multivariate Transfer Entropy

“…The brain can be regarded as a complex nonlinear system. As an index of nonlinear system, entropy is used to measure the complexity of nonlinear system [ 20 , 21 ]. At present, the methods of sample entropy and approximate entropy have been applied to the analysis of brain magnetic and electricity data [ 22 , 23 ].…”