Results 41 to 50 of about 7,868 (207)
Shock‐Triggered Asymmetric Response Stochastic Volatility
Journal of Forecasting, Volume 45, Issue 1, Page 217-240, January 2026.ABSTRACT We propose a novel asymmetric stochastic volatility model (STAR‐SV) in which the leverage parameter adjusts to the magnitude of past shocks. This flexible specification captures both the leverage effects and their propagation more effectively than standard asymmetric volatility models.
J. Miguel Marin, Helena Veiga
wiley +1 more source
An Academic Response to Basel 3.5
Risks, 2014 Recent crises in the financial industry have shown weaknesses in the modeling of Risk-Weighted Assets (RWAs). Relatively minor model changes may lead to substantial changes in the RWA numbers.
Paul Embrechts +4 more
doaj +1 more source
International Journal of Network Management, Volume 36, Issue 1, January/February 2026.The results of this paper show that incorporating ML predictions with a confidence ratio significantly improves profitability, achieving a 258.5% profit and ~60% increase in total balance compared with traditional non‐ML strategies. By leveraging ML algorithms like multilayer perceptron, this approach enhances decision‐making and outperforms competing ...
Kristína Okasová +2 more
wiley +1 more source
Early Detection Techniques for Market Risk Failure [PDF]
, 2008 The implementation of appropriate statistical techniques for monitoring conditional VaR models, i.e, backtesting, reported by institutions is fundamental to determine their exposure to market risk.
Olmo, J., Pouliot, W.
core
Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz
, 2015 Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI).
Mainik, Georg +2 more
core +1 more source
Partial Observability of Implied Volatility Matrices: Identification and Covolatilities Filtering
Mathematical Finance, Volume 36, Issue 1, Page 48-66, January 2026.ABSTRACT Whereas data on implied volatilities are available for a large number of assets, this is less frequently the case of implied covolatilities. We introduce a new approach based on static and dynamic Wishart models to solve this problem of missing data.
Christian Gouriéroux, Yang Lu
wiley +1 more source
Nonlinear Dependence Structure Between BRICS Stock Markets, Gold, and Cryptocurrencies
The Manchester School, Volume 94, Issue 1, Page 75-89, January 2026.ABSTRACT This study aims to conduct an in‐depth analysis of the complex nonlinear dependence relationships between cryptocurrencies and gold within the stocks of BRICS countries. The study employs a GARCH‐EVT‐Vine‐Copula and wavelet coherence models to evaluate the interconnectedness, tail risk and Co‐movement pattern of these assets before and after ...
Jiale Yan
wiley +1 more source
Sustainable Dissemination of Digital Music Artworks on TikTok: A Social Media Analysis
Complexity, Volume 2026, Issue 1, 2026.This study examines sustainable dissemination of digital music on TikTok by linking musical‐aspect discourse and network positioning to engagement and recommendation performance, using 1200 TikTok songs/videos, each represented by 52 platform and interaction features.
Wan na +4 more
wiley +1 more source
Backtesting lambda value at risk [PDF]
The European Journal of Finance, 2017 A new risk measure, the lambda value at risk (Lambda VaR), has been recently proposed from a theoretical point of view as a generalization of the value at risk (VaR). The Lambda VaR appears attractive for its potential ability to solve several problems of the VaR. In this paper we propose three nonparametric backtesting methodologies for the Lambda VaR
Corbetta, J., Peri, Ilaria
openaire +2 more sources
International Journal of Mathematics and Mathematical Sciences, Volume 2026, Issue 1, 2026.Loss data may often exhibit features such as multimodality and skewness that render single distributions incapable of capturing all these features. Insurance data often comprise of extremely large losses, of which single distributions may inadequately capture their varying features of different sizes.
Williams Kumi +4 more
wiley +1 more source