Showing 5 of total 5 results (show query)
cdriveraus
ctsem:Continuous Time Structural Equation Modelling
Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.
Maintained by Charles Driver. Last updated 27 days ago.
stochastic-differential-equationstime-seriescpp
42 stars 9.58 score 366 scripts 1 dependents
sciml
diffeqr:Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs). 'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information, see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.
Maintained by Christopher Rackauckas. Last updated 4 months ago.
daeddedelay-differential-equationsdifferential-algebraic-equationsdifferential-equationsodeordinary-differential-equationsscientific-machine-learningscimlsdestochastic-differential-equations
143 stars 8.42 score 31 scripts
jirotubuyaki
Jdmbs:Monte Carlo Option Pricing Algorithms for Jump Diffusion Models with Correlational Companies
Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.
Maintained by Masashi Okada. Last updated 5 years ago.
black-scholesbrownian-motioncomputational-financederivativesfinancefinancial-analysisfinancial-engineeringjump-diffusionmonte-carlooptionoption-pricingsdestochastic-differential-equationsstochastic-processesstock-market
27 stars 5.13 score 6 scripts
ogarciav
resde:Estimation in Reducible Stochastic Differential Equations
Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.
Maintained by Oscar Garcia. Last updated 2 years ago.
estimationstochastic-differential-equations
2 stars 4.00 score 2 scripts
techtonique
esgtoolkit:Toolkit for Monte Carlo Simulations
A toolkit for Monte Carlo Simulations in Finance, Economics, Insurance, Physics. Multiple simulation models can be created by combining building blocks provided in the package.
Maintained by T. Moudiki. Last updated 2 months ago.
diffusion-modeldiffusion-modelsmonte-carlo-methodsmonte-carlo-simulationmontecarlo-simulationscenario-analysisscenario-creatorscenario-generationsimulationstochastic-differential-equationsstochastic-processesstochastic-simulationcpp
11 stars 3.97 score 28 scripts