ajd.sim.wh
Simulation of Affine Jump Diffusions via the Wu-Hu Method
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary
Last synced: 6 months ago
·
JSON representation
·
Repository
Simulation of Affine Jump Diffusions via the Wu-Hu Method
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created about 1 year ago
· Last pushed 12 months ago
Metadata Files
Readme
License
Citation
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# ajd.sim.wh
The goal of ajd.sim.wh is to **sim**ulate exactly the Heston
Stochastic Volatility (SV) model and its **A**ffine **J**ump **D**iffusion (AJD)
extensions using the **W**u-**H**u algorithm
(Wu-Hu, 2024), hence the name `ajd.sim.wh`.
The extended models include
- SVJ: SV model with jumps in the price process.
- SVCJ: SV model with contemporaneous jumps both in the price and variance
processes.
*References*:
- Kyriakou, I., Brignone, R., & Fusai, G. (2024). Unified moment-based modeling
of integrated stochastic processes. *Operations Research*, 72(4), 1630-1653.
## Installation
You can install the development version of ajd.sim.wh like so:
``` r
# library(devtools)
install_github("xmlongan/ajd.sim.wh")
```
## Example
This is a basic example which shows you how to simulate some return
(not the price) samples of the Heston SV model and plot a histogram of these
simulated returns:
```{r example}
library(ajd.sim.wh)
# Heston SV
v0 = 0.010201; k = 6.21; theta = 0.019; sigma = 0.61; rho = -0.7
r = 0.0319; tau = 1
par_hest = list(v0=v0, k=k, theta=theta, sigma=sigma, rho=rho, h=tau)
moms = rep(0, 8)
for (i in 2:8) {moms[i] = eval_mom_hest(ajd.sim.wh::fmu.hest[[i]], par_hest)}
N = 1000 # number of samples
Y = ajd.sim.wh::rpearson(1000, moms)
beta = (1 -exp(-k * tau)) / (2 * k)
Ymean = (r - theta/2) * tau - beta * (v0 - theta)
Y = Y + Ymean
hist(Y, main="Heston SV model")
```
If you want to simulate samples from the other two SV models, use:
- `price_svj()` for the SVJ model,
- `price_svcj()` for the SVCJ model.
## Pricing the European call option Using Monte Carlo simulation
If your are interested in pricing the European call option using Monte Carlo
simulation for the Heston SV, SVJ and SVCJ models. Please refer to functions
`?price_hest`, `?price_svj` and `?price_svcj`.
Owner
- Name: Yanfeng
- Login: xmlongan
- Kind: user
- Location: Shanghai
- Repositories: 2
- Profile: https://github.com/xmlongan
Ph.D. in Management Science, Fudan U.
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Wu" given-names: "Yan-Feng" orcid: "https://orcid.org/0000-0002-7105-1070" title: "ajd.sim.wh: An R Package for Simulating Affine Jump Diffusions via the Wu-Hu Method" version: 1.0.0 doi: date-released: 2025-02-10 url: "https://github.com/xmlongan/ajd.sim.wh"
GitHub Events
Total
- Push event: 27
- Create event: 1
Last Year
- Push event: 27
- Create event: 1
Dependencies
DESCRIPTION
cran
- R >= 2.10 depends
- PearsonDS * imports
- Rmpfr * imports
- ajd.sim.kbf * imports
- grDevices * imports
- graphics * imports
- stats * imports
- knitr * suggests
- rmarkdown * suggests
- testthat >= 3.0.0 suggests