Does fintech matter for financial inclusion and financial stability in BRICS markets?

DB Vuković, MK Hassan, B Kwakye… - Emerging Markets …, 2024 - Elsevier
We investigate whether fintech development expedites financial inclusion and affects the
stability of the financial sector in BRICS economies. We first seek to identify the linkage …

[HTML][HTML] Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t …

WL Leão, CA Abanto-Valle, MH Chen - Statistics and its Interface, 2017 - ncbi.nlm.nih.gov
A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic
skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation …

A locally both leptokurtic and fat-tailed distribution with application in a Bayesian stochastic volatility model

Ł Lenart, A Pajor, Ł Kwiatkowski - Entropy, 2021 - mdpi.com
In the paper, we begin with introducing a novel scale mixture of normal distribution such that
its leptokurticity and fat-tailedness are only local, with this “locality” being separately …

Endogenous Threshold Stochastic Volatility Model: An Outlook Across the Globe for Stock Market Indices

RJR Chaparro - 2023 - search.proquest.com
Asymmetries and heavy tails are well-known characteristics on compound daily returns
stock market indices. The THSV-SMN–Threshold Stochastic Volatility Model with Scale …

Threshold Stochastic Volatility Models with Heavy Tails: A Bayesian Approach

CA Abanto-Valle, HB Garrafa-Aragón - Economía, 2019 - revistas.pucp.edu.pe
This paper extends the threshold stochastic volatility (THSV) model specification proposed
in So et al.(2002) and Chen et al.(2008) by incorporating thick-tails in the mean equation …

[ΑΝΑΦΟΡΑ][C] Endogenous Threshold Stochastic Volatility Model: An Outlook Across the Globe for Stock Market Indices

RJ Robles Chaparro - 2023 - Pontificia Universidad Católica del …

[ΑΝΑΦΟΡΑ][C] Stochastic volatility with heavy-tails: An approximate Bayesian approach using Hidden Markov Models