English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In many physical, social or economical phenomena we observe changes of astudied quantity only in discrete, irregularly distributed points in time. Thestochastic process used by physicists to describe this kind of variables is theContinuous Time Random Walk (CTRW). Despite the popularity of this type ofstochastic processes and strong empirical motivation, models with a long-termmemory within the sequence of time intervals between observations are missing.Here, we fill this gap by introducing a new family of CTRWs. The memory isintroduced to the model by the assumption that many consecutive time intervalscan be the same. Surprisingly, in this process we can observe a slowly decayingnonlinear autocorrelation function without a fat-tailed distribution of timeintervals. Our model applied to high-frequency stock market data cansuccessfully describe the slope of decay of nonlinear autocorrelation functionof stock market returns. The model achieves this result with no dependencebetween consecutive price changes. It proves the crucial role of inter-eventtimes in the volatility clustering phenomenon observed in all stock markets.

Continuous Time Random Walk with correlated waiting times. The crucial role of inter-trade times in volatility clustering