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

As a newly emerged asset class, cryptocurrency is evidently more volatilecompared to the traditional equity markets. Due to its mostly unregulatednature, and often low liquidity, the price of crypto assets can sustain asignificant change within minutes that in turn might result in considerablelosses. In this paper, we employ an approach for encoding market informationinto images and making predictions of short-term realized volatility byemploying Convolutional Neural Networks. We then compare the performance of theproposed encoding and corresponding model with other benchmark models. Theexperimental results demonstrate that this representation of market data with aConvolutional Neural Network as a predictive model has the potential to bettercapture the market dynamics and a better volatility prediction.

Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow