# Confidence Intervals¶

The confidence interval function allows three types of confidence intervals to be constructed:

• Nonparametric, which only resamples the data

• Semi-parametric, which use resampled residuals

• Parametric, which simulate residuals

Confidence intervals can then be computed using one of 6 methods:

• Basic (basic)

• Percentile (percentile)

• Studentized (studentized)

• Asymptotic using parameter covariance (norm, var or cov)

• Bias-corrected (bc, bias-corrected or debiased)

• Bias-corrected and accelerated (bca)

## Setup¶

All examples will construct confidence intervals for the Sharpe ratio of the S&P 500, which is the ratio of the annualized mean to the annualized standard deviation. The parameters will be the annualized mean, the annualized standard deviation and the Sharpe ratio.

import datetime as dt

import pandas as pd

start = dt.datetime(1951, 1, 1)
end = dt.datetime(2014, 1, 1)
sp500 = web.DataReader('^GSPC', 'yahoo', start=start, end=end)
low = sp500.index.min()
high = sp500.index.max()
monthly_dates = pd.date_range(low, high, freq='M')
monthly = sp500.reindex(monthly_dates, method='ffill')
returns = 100 * monthly['Adj Close'].pct_change().dropna()


The main function used will return a 3-element array containing the parameters.

def sharpe_ratio(x):
mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
return np.array([mu, sigma, mu / sigma])


Note

Functions must return 1-d NumPy arrays or Pandas Series.

## Confidence Interval Types¶

Three types of confidence intervals can be computed. The simplest are non-parametric; these only make use of parameter estimates from both the original data as well as the resampled data. Semi-parametric mix the original data with a limited form of resampling, usually for residuals. Finally, parametric bootstrap confidence intervals make use of a parametric distribution to construct “as-if” exact confidence intervals.

### Nonparametric Confidence Intervals¶

Non-parametric sampling is the simplest method to construct confidence intervals.

This example makes use of the percentile bootstrap which is conceptually the simplest method - it constructs many bootstrap replications and returns order statistics from these empirical distributions.

from arch.bootstrap import IIDBootstrap

bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')


Note

While returns have little serial correlation, squared returns are highly persistent. The IID bootstrap is not a good choice here. Instead a time-series bootstrap with an appropriately chosen block size should be used.

## Confidence Interval Methods¶

Note

conf_int can construct two-sided, upper or lower (one-sided) confidence intervals. All examples use two-sided, 95% confidence intervals (the default). This can be modified using the keyword inputs type ('upper', 'lower' or 'two-sided') and size.

### Basic (basic)¶

Basic confidence intervals construct many bootstrap replications $$\hat{\theta}_b^\star$$ and then constructs the confidence interval as

$\left[\hat{\theta} + \left(\hat{\theta} - \hat{\theta}^{\star}_{u} \right), \hat{\theta} + \left(\hat{\theta} - \hat{\theta}^{\star}_{l} \right) \right]$

where $$\hat{\theta}^{\star}_{l}$$ and $$\hat{\theta}^{\star}_{u}$$ are the $$\alpha/2$$ and $$1-\alpha/2$$ empirical quantiles of the bootstrap distribution. When $$\theta$$ is a vector, the empirical quantiles are computed element-by-element.

from arch.bootstrap import IIDBootstrap

bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='basic')


### Percentile (percentile)¶

The percentile method directly constructs confidence intervals from the empirical CDF of the bootstrap parameter estimates, $$\hat{\theta}_b^\star$$. The confidence interval is then defined.

$\left[\hat{\theta}^{\star}_{l}, \hat{\theta}^{\star}_{u} \right]$

where $$\hat{\theta}^{\star}_{l}$$ and $$\hat{\theta}^{\star}_{u}$$ are the $$\alpha/2$$ and $$1-\alpha/2$$ empirical quantiles of the bootstrap distribution.

from arch.bootstrap import IIDBootstrap

bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')


### Asymptotic Normal Approximation (norm, cov or var)¶

The asymptotic normal approximation method estimates the covariance of the parameters and then combines this with the usual quantiles from a normal distribution. The confidence interval is then

$\left[\hat{\theta} + \hat{\sigma}\Phi^{-1}\left(\alpha/2\right), \hat{\theta} - \hat{\sigma}\Phi^{-1}\left(\alpha/2\right), \right]$

where $$\hat{\sigma}$$ is the bootstrap estimate of the parameter standard error.

from arch.bootstrap import IIDBootstrap

bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='norm')


### Studentized (studentized)¶

The studentized bootstrap may be more accurate than some of the other methods. The studentized bootstrap makes use of either a standard error function, when parameter standard errors can be analytically computed, or a nested bootstrap, to bootstrap studentized versions of the original statistic. This can produce higher-order refinements in some circumstances.

The confidence interval is then

$\left[\hat{\theta} + \hat{\sigma}\hat{G}^{-1}\left(\alpha/2\right), \hat{\theta} + \hat{\sigma}\hat{G}^{-1}\left(1-\alpha/2\right), \right]$

where $$\hat{G}$$ is the estimated quantile function for the studentized data and where $$\hat{\sigma}$$ is a bootstrap estimate of the parameter standard error.

The version that uses a nested bootstrap is simple to implement although it can be slow since it requires $$B$$ inner bootstraps of each of the $$B$$ outer bootstraps.

from arch.bootstrap import IIDBootstrap

bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='studentized')


In order to use the standard error function, it is necessary to estimate the standard error of the parameters. In this example, this can be done using a method-of-moments argument and the delta-method. A detailed description of the mathematical formula is beyond the intent of this document.

def sharpe_ratio_se(params, x):
mu, sigma, sr = params
y = 12 * x
e1 = y - mu
e2 = y ** 2.0 - sigma ** 2.0
errors = np.vstack((e1, e2)).T
t = errors.shape[0]
vcv = errors.T.dot(errors) / t
D = np.array([[1, 0],
[0, 0.5 * 1 / sigma],
[1.0 / sigma, - mu / (2.0 * sigma**3)]
])
avar = D.dot(vcv /t).dot(D.T)
return np.sqrt(np.diag(avar))


The studentized bootstrap can then be implemented using the standard error function.

from arch.bootstrap import IIDBootstrap
bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='studentized',
std_err_func=sharpe_ratio_se)


Note

Standard error functions must return a 1-d array with the same number of element as params.

Note

Standard error functions must match the patters std_err_func(params, *args, **kwargs) where params is an array of estimated parameters constructed using *args and **kwargs.

### Bias-corrected (bc, bias-corrected or debiased)¶

The bias corrected bootstrap makes use of a bootstrap estimate of the bias to improve confidence intervals.

from arch.bootstrap import IIDBootstrap
bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='bc')


The bias-corrected confidence interval is identical to the bias-corrected and accelerated where $$a=0$$.

### Bias-corrected and accelerated (bca)¶

Bias-corrected and accelerated confidence intervals make use of both a bootstrap bias estimate and a jackknife acceleration term. BCa intervals may offer higher-order accuracy if some conditions are satisfied. Bias-corrected confidence intervals are a special case of BCa intervals where the acceleration parameter is set to 0.

from arch.bootstrap import IIDBootstrap

bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='bca')


The confidence interval is based on the empirical distribution of the bootstrap parameter estimates, $$\hat{\theta}_b^\star$$, where the percentiles used are

$\Phi\left( \Phi^{-1}\left(\hat{b}\right)+\frac{\Phi^{-1}\left(\hat{b}\right) +z_{\alpha}}{1-\hat{a}\left(\Phi^{-1}\left(\hat{b}\right)+z_{\alpha}\right)} \right)$

where $$z_{\alpha}$$ is the usual quantile from the normal distribution and $$b$$ is the empirical bias estimate,

$\hat{b}=\#\left\{ \hat{\theta}_{b}^{\star}<\hat{\theta}\right\} / B$

$$a$$ is a skewness-like estimator using a leave-one-out jackknife.