Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling-Reference-Cited by-同舟云学术

Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling

Published:2020-07 Issue: Volume:527 Page:1-26
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Cerqueti Roy,Giacalone Massimiliano^ORCID,Mattera Raffaele

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference48 articles.

1. Estimating stock market volatility using asymmetric GARCH models;Alberg;Appl. Financ. Econ.,2008

2. Estimation of flexible fuzzy GARCH models for conditional density estimation;Almeida;Inf. Sci.,2014

3. A non-linear time series approach to modelling asymmetry in stock market indexes;Amendola;Stat. Methods Appl.,2002

4. Pricing of minimum guarantees in life insurance contracts with fuzzy volatility;Anzilli;Inf. Sci.,2018

5. Bitcoins as an investment or speculative vehicle? A first look;Baek;Appl. Econ. Lett.,2015

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