https://github.com/mlysy/svcommon

Fast inference for a common-factor stochastic volatility model
https://github.com/mlysy/svcommon

Last synced: 2 months ago
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Fast inference for a common-factor stochastic volatility model

• Host: GitHub
• URL: https://github.com/mlysy/svcommon
• Owner: mlysy
• License: gpl-3.0
• Created: 2020-06-15T11:24:52.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-09-10T14:57:03.000Z (4 months ago)
• Last Synced: 2024-09-11T01:20:11.656Z (4 months ago)
• Language: R
• Homepage: https://mlysy.github.io/svcommon/
• Size: 1.83 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# svcommon: Fast Inference for Common-Factor Stochastic Volatility Models

*Martin Lysy*

[![R-CMD-check](https://github.com/mlysy/svcommon/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/mlysy/svcommon/actions/workflows/R-CMD-check.yaml)

---

### Description

Provides various tools for estimating the parameters of the common-factor multivariate stochastic volatility model of [Fang et al (2020)](https://onlinelibrary.wiley.com/doi/full/10.1002/cjs.11536) and extensions.  In particular, the complete-data likelihood implementation scales linearly in the number of assets, and latent volatilities are efficiently marginalized using the Laplace approximation in the R package [**TMB**](https://CRAN.R-project.org/package=TMB) with very high accuracy.  Combined with a carefully initialized block coordinate descent algorithm, maximum likelihood estimation can be conducted two orders of magnitude faster than with alternative parameter inference algorithms.

### Installation

Install the R package [**devtools**](https://CRAN.R-project.org/package=devtools) and run

```r

devtools::install_github("mlysy/svcommon", INSTALL_opts = "--install-tests")

```

### Usage

Please see package example code in `vignette("svcommon")`.