stochvolTMB

Likelihood estimation of stochastic volatility models

https://github.com/jenswahl/stochvoltmb

Repository

Likelihood estimation of stochastic volatility models

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# stochvolTMB


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`stochvolTMB` is a package for fitting stochastic volatility (SV) models to time series data. It is inspired by the package [stochvol](https://github.com/gregorkastner/stochvol), but parameter estimates are obtained through optimization and not MCMC, leading to significant speed up. It is built on [Template Model Builder](https://github.com/kaskr/adcomp) for fast and efficient estimation. The latent volatility is integrated out of the likelihood using the Laplace approximation and automatic differentiation (AD) is used for accurate evaluation of derivatives. 

Four distributions for the observational error are implemented: 

* **Gaussian** - The classic SV model with Gaussian noise 
* **t** - t-distributed noise for heavy tail returns 
* **Leverage** -  Extending the **Gaussian** model by allowing observed returns to be correlated with the latent volatility
* **Skew-Gaussian** - Skew-Gaussian distributed noise for asymmetric returns


## Installation
To install the current stable release from CRAN, use

```{r eval = FALSE}
install.packages("stochvolTMB")
```

To install the current development version, use

``` {r eval = FALSE}
# install.packages("remotes")
remotes::install_github("JensWahl/stochvolTMB")
```

If you would also like to build and view the vignette locally, use

```{r, eval = FALSE}
remotes::install_github("JensWahl/stochvolTMB", dependencies = TRUE, build_vignettes = TRUE)
```

## Example

The main function for estimating parameters is `estimate_parameters`:

```{r example}
library(stochvolTMB, warn.conflicts = FALSE)

# load s&p500 data from 2005 to 2018
data(spy)

# find the best model using AIC 
gaussian <- estimate_parameters(spy$log_return, model = "gaussian", silent = TRUE)
t_dist <- estimate_parameters(spy$log_return, model = "t", silent = TRUE)
skew_gaussian <- estimate_parameters(spy$log_return, model = "skew_gaussian", silent = TRUE)
leverage <- estimate_parameters(spy$log_return, model = "leverage", silent = TRUE)

# the leverage model stands out with an AIC far below the other models
AIC(gaussian, t_dist, skew_gaussian, leverage)

# get parameter estimates with standard error
estimates <- summary(leverage)
head(estimates, 10)

# plot estimated volatility with 95 % confidence interval
plot(leverage, include_ci = TRUE, dates = spy$date)


```

Given the estimated parameters we can simulate future volatility and log-returns using `predict`. 
```{r}
set.seed(123)
# simulate future prices with or without parameter uncertainty
pred = predict(leverage, steps = 10)

# Calculate the mean, 2.5% and 97.5% quantiles from the simulations
pred_summary = summary(pred, quantiles = c(0.025, 0.975), predict_mean = TRUE)

print(pred_summary)

# plot predicted volatility with 0.025 and 0.975 quantiles
plot(leverage, include_ci = TRUE, forecast = 50, dates = spy$d) +
  ggplot2::xlim(c(spy[.N, date] - 150, spy[.N, date] + 50))

```

## Shiny app 

By running `demo()` you start a shiny application where you can visually inspect the effect of choosing different models and parameter configurations 

```{r eval = FALSE}
demo()
```

![](man/figures/shinyApp.png)

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