# Forecasting¶

Multi-period forecasts can be easily produced for ARCH-type models using forward recursion, with some caveats. In particular, models that are non-linear in the sense that they do not evolve using squares or residuals do not normally have analytically tractable multi-period forecasts available.

All models support three methods of forecasting:

• Analytical: analytical forecasts are always available for the 1-step ahead forecast due to the structure of ARCH-type models. Multi-step analytical forecasts are only available for model which are linear in the square of the residual, such as GARCH or HARCH.

• Simulation: simulation-based forecasts are always available for any horizon, although they are only useful for horizons larger than 1 since the first out-of-sample forecast from an ARCH-type model is always fixed. Simulation-based forecasts make use of the structure of an ARCH-type model to forward simulate using the assumed distribution of residuals, e.g., a Normal or Student’s t.

• Bootstrap: bootstrap-based forecasts are similar to simulation based forecasts except that they make use of the standardized residuals from the actual data used in the estimation rather than assuming a specific distribution. Like simulation-base forecasts, bootstrap-based forecasts are only useful for horizons larger than 1. Additionally, the bootstrap forecasting method requires a minimal amount of in-sample data to use prior to producing the forecasts.

This document will use a standard GARCH(1,1) with a constant mean to explain the choices available for forecasting. The model can be described as

\begin{eqnarray} r_t & = & \mu + \epsilon_t \\ \epsilon_t & = & \sigma_t e_t \\ \sigma^2_t & = & \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma^2_{t-1} \\ e_t & \sim & N(0,1) \end{eqnarray}

In code this model can be constructed using data from the S&P 500 using

from arch import arch_model
import datetime as dt
start = dt.datetime(2000,1,1)
end = dt.datetime(2014,1,1)
sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
returns = 100 * sp500['Adj Close'].pct_change().dropna()
am = arch_model(returns, vol='Garch', p=1, o=0, q=1, dist='Normal')


The model will be estimated using the first 10 years to estimate parameters and then forecasts will be produced for the final 5.

split_date = dt.datetime(2010,1,1)
res = am.fit(last_obs=split_date)


## Analytical Forecasts¶

Analytical forecasts are available for most models that evolve in terms of the squares of the model residuals, e.g., GARCH, HARCH, etc. These forecasts exploit the relationship $$E_t[\epsilon_{t+1}^2] = \sigma_{t+1}^2$$ to recursively compute forecasts.

Variance forecasts are constructed for the conditional variances as

\begin{eqnarray} \sigma^2_{t+1} & = & \omega + \alpha \epsilon_t^2 + \beta \sigma^2_{t-1} \\ \sigma^2_{t+h} & = & \omega + \alpha E_{t}[\epsilon_{t+h-1}^2] + \beta E_{t}[\sigma^2_{t+h-1}] \, h \geq 2 \\ & = & \omega + \left(\alpha + \beta\right) E_{t}[\sigma^2_{t+h-1}] \, h \geq 2 \end{eqnarray}
forecasts = res.forecast(horizon=5, start=split_date)
forecasts.variance[split_date:].plot()


## Simulation Forecasts¶

Simulation-based forecasts use the model random number generator to simulate draws of the standardized residuals, $$e_{t+h}$$. These are used to generate a pre-specified number of paths of the variances which are then averaged to produce the forecasts. In models like GARCH which evolve in the squares of the residuals, there are few advantages to simulation-based forecasting. These methods are more valuable when producing multi-step forecasts from models that do not have closed form multi-step forecasts such as EGARCH models.

Assume there are $$B$$ simulated paths. A single simulated path is generated using

\begin{eqnarray} \sigma^2_{t+h, b} & = & \omega + \alpha \epsilon_{t+h-1, b}^2 + \beta \sigma^2_{t+h-1, b} \\ \epsilon_{t+h, b} & = & e_{t+h, b} \sqrt{\sigma^2_{t+h, b}} \end{eqnarray}

where the simulated shocks are $$e_{t+1, b}, e_{t+2, b},\ldots, e_{t+h, b}$$ where $$b$$ is included to indicate that the simulations are independent across paths. Note that the first residual, $$\epsilon_{t}$$, is in-sample and so is not simulated.

The final variance forecasts are then computed using the $$B$$ simulations

$$E_t[\epsilon^2_{t+h}] = \sigma^2_{t+h} = B^{-1}\sum_{b=1}^B \sigma^2_{t+h,b}.$$
forecasts = res.forecast(horizon=5, start=split_date, method='simulation')


## Bootstrap Forecasts¶

Bootstrap-based forecasts are virtually identical to simulation-based forecasts except that the standardized residuals are generated by the model. These standardized residuals are generated using the observed data and the estimated parameters as

$$\hat{e}_t = \frac{r_t-\hat{\mu}}{\hat{\sigma}_t}$$

The generation scheme is identical to the simulation-based method except that the simulated shocks are drawn (i.i.d., with replacement) from $$\hat{e}_{1}, \hat{e}_{2},\ldots, \hat{e}_{t}$$. so that only data available at time $$t$$ are used to simulate the paths.

## Forecasting Options¶

The forecast() method is attached to a model fit result.

• params - The model parameters used to forecast the mean and variance. If not specified, the parameters estimated during the call to fit the produced the result are used.

• horizon - A positive integer value indicating the maximum horizon to produce forecasts.

• start - A positive integer or, if the input to the mode is a DataFrame, a date (string, datetime, datetime64 or Timestamp). Forecasts are produced from start until the end of the sample. If not provided, start is set to the length of the input data minus 1 so that only 1 forecast is produced.

• align - One of ‘origin’ (default) or ‘target’ that describes how the forecasts aligned in the output. Origin aligns forecasts to the last observation used in producing the forecast, while target aligns forecasts to the observation index that is being forecast.

• method - One of ‘analytic’ (default), ‘simulation’ or ‘bootstrap’ that describes the method used to produce the forecasts. Not all methods are available for all horizons.

• simulations - A non-negative integer indicating the number of simulation to use when method is ‘simulation’ or ‘bootstrap’

## Understanding Forecast Output¶

Any call to forecast() returns a ARCHModelForecast object with has 3 core attributes and 1 which may be useful when using simulation- or bootstrap-based forecasts.

The three core attributes are

• mean - The forecast conditional mean.

• variance - The forecast conditional variance.

• residual_variance - The forecast conditional variance of residuals. This will differ from variance whenever the model has dynamics (e.g. an AR model) for horizons larger than 1.

Each attribute contains a DataFrame with a common structure.

print(forecasts.variance.tail())


which returns

                 h.1       h.2       h.3       h.4       h.5
Date
2013-12-24  0.489534  0.495875  0.501122  0.509194  0.518614
2013-12-26  0.474691  0.480416  0.483664  0.491932  0.502419
2013-12-27  0.447054  0.454875  0.462167  0.467515  0.475632
2013-12-30  0.421528  0.430024  0.439856  0.448282  0.457368
2013-12-31  0.407544  0.415616  0.422848  0.430246  0.439451


The values in the columns h.1 are one-step ahead forecast, while values in h.2, …, h.5 are 2, …, 5-observation ahead forecasts. The output is aligned so that the Date column is the final data used to generate the forecast, so that h.1 in row 2013-12-31 is the one-step ahead forecast made using data up to and including December 31, 2013.

By default forecasts are only produced for observations after the final observation used to estimate the model.

day = dt.timedelta(1)
print(forecasts.variance[split_date - 5 * day:split_date + 5 * day])


which produces

                h.1       h.2       h.3       h.4       h.5
Date
2009-12-28       NaN       NaN       NaN       NaN       NaN
2009-12-29       NaN       NaN       NaN       NaN       NaN
2009-12-30       NaN       NaN       NaN       NaN       NaN
2009-12-31       NaN       NaN       NaN       NaN       NaN
2010-01-04  0.739303  0.741100  0.744529  0.746940  0.752688
2010-01-05  0.695349  0.702488  0.706812  0.713342  0.721629
2010-01-06  0.649343  0.654048  0.664055  0.672742  0.681263


The output will always have as many rows as the data input. Values that are not forecast are nan filled.

## Output Classes¶

class arch.univariate.base.ARCHModelForecast(index, mean, variance, residual_variance, simulated_paths=None, simulated_variances=None, simulated_residual_variances=None, simulated_residuals=None, align='origin')[source]

Container for forecasts from an ARCH Model

Parameters
• index ({list, ndarray}) –

• mean (ndarray) –

• variance (ndarray) –

• residual_variance (ndarray) –

• simulated_paths (ndarray, optional) –

• simulated_variances (ndarray, optional) –

• simulated_residual_variances (ndarray, optional) –

• simulated_residuals (ndarray, optional) –

• align ({'origin', 'target'}) –

mean

Forecast values for the conditional mean of the process

Type

DataFrame

variance

Forecast values for the conditional variance of the process

Type

DataFrame

residual_variance

Forecast values for the conditional variance of the residuals

Type

DataFrame

class arch.univariate.base.ARCHModelForecastSimulation(values, residuals, variances, residual_variances)[source]

Container for a simulation or bootstrap-based forecasts from an ARCH Model

Parameters
values

Simulated values of the process

Type

DataFrame

residuals

Simulated residuals used to produce the values

Type

DataFrame

variances

Simulated variances of the values

Type

DataFrame

residual_variances`

Simulated variance of the residuals

Type

DataFrame