Results 91 to 100 of about 22,553 (296)
Impact of rough stochastic volatility models on long-term life insurance pricing. [PDF]
Eur Actuar J, 2023 Dupret JL, Barbarin J, Hainaut D.
europepmc +1 more source
Advanced Science, EarlyView.A nanoporous SiO2 memristor enabling reconfigurable volatile and non‐volatile switching within a single device is demonstrated. The dual‐mode functionality supports both physical reservoir dynamics and synaptic weight storage, allowing unified hardware implementation of reservoir computing for temporal information processing, including image and ...
Bohao Ding +5 more
wiley +1 more source
On leverage in a stochastic volatility model [PDF]
Journal of Econometrics, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors [PDF]
The efficient Bayesian estimation method using Markov chain Monte Carlo is proposed for a multivariate stochastic volatility model that is a natural extension of the univariate stochastic volatility model with leverage and heavy-tailed errors, where we ...
Yasuhiro Omori, Tsunehiro Ishihara
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Higher-order dynamic effects of uncertainty risk under thick-tailed stochastic volatility. [PDF]
Financ Innov, 2022 Gong XL, Lu JY, Xiong X, Zhang W.
europepmc +1 more source
Advanced Science, EarlyView.Nanomaterials offer dual applications in allergy management. For diagnosis, nanomaterials enhance analytical sensitivity and improve detection in specific IgE and functional assays such as the basophil activation test (BAT). For allergen‐specific immunotherapy, nanomaterials enable allergen masking and controlled release, and effectively modulate the ...
Madiha Habib +8 more
wiley +1 more source
Stochastic Volatility Driven by Large Shocks [PDF]
This paper presents a new model of stochastic volatility which allows for infrequent shifts in the mean of volatility, known as structural breaks. These are endogenously driven from large innovations in stock returns arriving in the market. The model has
George Kapetanios, Elias Tzavalis
core
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Advanced Science, EarlyView.Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source
CEV MODEL WITH STOCHASTIC VOLATILITY
Journal of Vasyl Stefanyk Precarpathian National University, 2019 This paper develops a systematic method for calculating approximate prices for a wide range of securities implying the tools of spectral analysis, singular and regular perturbation theory. Price options depend on stochastic volatility, which may be multiscale, in the sense that it may be driven by one fast-varying and one slow-varying factor. The found
BURTNYAK, IVAN, MALYTSKA, ANNA
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"Exact" and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study. [PDF]
Entropy (Basel), 2021 Shapovalova Y.
europepmc +1 more source