Semi-parametric modelling in finance: theoretical foundations
From MaRDI portal
Publication:4646785
DOI10.1088/1469-7688/2/4/201zbMath1408.62171OpenAlexW1971053858MaRDI QIDQ4646785
Rüdiger Kiesel, Nicholas H. Bingham
Publication date: 14 January 2019
Published in: Quantitative Finance (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1088/1469-7688/2/4/201
Applications of statistics to actuarial sciences and financial mathematics (62P05) Brownian motion (60J65)
Related Items
The multivariate tail-inflated normal distribution and its application in finance ⋮ Testing for multivariate volatility functions using minimum volume sets and inverse regression ⋮ Dimension-wise scaled normal mixtures with application to finance and biometry ⋮ Systematic scenario selection: stress testing and the nature of uncertainty ⋮ Regular variation and probability: The early years ⋮ The risk of tensor Stein-rules in elliptically contoured distributions ⋮ Inference and mixture modeling with the elliptical Gamma distribution ⋮ Leptokurtic moment-parameterized elliptically contoured distributions with application to financial stock returns ⋮ Semiparametric estimation in the normal variance-mean mixture model ⋮ Testing high-dimensional covariance matrices under the elliptical distribution and beyond ⋮ Calibration for weak variance-alpha-gamma processes ⋮ A note on Stein's lemma for multivariate elliptical distributions ⋮ Translation-invariant and positive-homogeneous risk measures and optimal portfolio management in the presence of a riskless component ⋮ The skewness of mean-variance normal mixtures ⋮ Models of asset returns: changes of pattern from high to low event frequency ⋮ Bivariate normal mixture spread option valuation ⋮ Wang's capital allocation formula for elliptically contoured distributions. ⋮ On the tail mean-variance optimal portfolio selection ⋮ Portfolio optimization when asset returns have the Gaussian mixture distribution ⋮ Semi-parametric modelling in finance: theoretical foundations ⋮ A semi-parametric approach to risk management ⋮ Stein's lemma for elliptical random vectors ⋮ Chebyshev-type inequalities for scale mixtures ⋮ Inference for vast dimensional elliptical distributions ⋮ On the generalization of Esscher and variance premiums modified for the elliptical family of distributions ⋮ Generalized Post-Widder inversion formula with application to statistics ⋮ On the generalization of Stein's lemma for elliptical class of distributions ⋮ A Black–Litterman asset allocation model under Elliptical distributions ⋮ Self-decomposability of weak variance generalised gamma convolutions ⋮ On selfdecomposable Stieltjes transforms ⋮ Gram-Charlier-like expansions of the convoluted hyperbolic-secant density ⋮ Description of an ecological niche for a mixed local/nonlocal dispersal: an evolution equation and a new Neumann condition arising from the superposition of Brownian and Lévy processes ⋮ A new class of multivariate distributions: scale mixture of Kotz-type distributions
Cites Work
- Breakdown points of affine equivariant estimators of multivariate location and covariance matrices
- On the theory of elliptically contoured distributions
- Statistical properties of the generalized inverse Gaussian distribution
- Smoothing techniques. With implementation in S
- First hitting time models for the generalized inverse Gaussian distribution
- Testing for ellipsoidal symmetry of a multivariate density
- Processes of normal inverse Gaussian type
- Asymptotics of reweighted estimators of multivariate location and scatter
- Hyperbolic distributions in finance
- Random walks with spherical symmetry
- Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
- Coherent Measures of Risk
- On a theorem of Kłosowska about generalised convolutions
- On simulation from infinitely divisible distributions
- Normal Variance-Mean Mixtures and z Distributions
- Self-decomposability of the generalized inverse Gaussian and hyperbolic distributions
- Infinite divisibility of the hyperbolic and generalized inverse Gaussian distributions
- Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling
- Some stationary processes in discrete and continuous time
- The normal inverse gaussian lévy process: simulation and approximation
- The Semiparametric Normal Variance‐Mean Mixture Model
- Semi-parametric modelling in finance: theoretical foundations
- The Variance Gamma Process and Option Pricing
- Generalized convolutions
- Factorization Theory and Domains of Attraction for Generalized Convolution Algebras
- Infinite Divisibility and Variance Mixtures of the Normal Distribution
- Fourier Transforms. (AM-19)
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Semi-parametric modelling in finance: theoretical foundations