roarkemc

Within VaR modeling for quantitative market risk management, what is the difference between confidence levels and intervals? Also, compare to other similar types of statistical measures such as credible and prediction intervals, and confidence bands and regions.

Key Terms

• Confidence Level: probability that the interval contains the true parameter (e.g., 95% means 95% confidence).
• Confidence Interval: range of values derived from the sample data that is expected to contain the true parameter with a specified confidence level.
• Credible Interval: used in Bayesian statistics, reflecting probability distributions rather than frequentist confidence.
• Prediction Interval: estimates the range for future observations based on existing data.
• Confidence Band: type of confidence interval for entire functions or curves rather than single estimates.
• Confidence Region: generalization of confidence intervals for multiple parameters, indicating a range in multivar

roarkemc

Is there a VS Code extension for Pyink?

No, but with a bit of a workaround, you can use the Black Formatter extension.

After installing Pyink and the extension, you can set these in VSCode's settings.json:

{
    "[python]": {
 "editor.defaultFormatter": "ms-python.black-formatter"

roarkemc

import numpy as np
import numpy.typing as npt
import pandas as pd
import plotly.graph_objects as go
import plotly.figure_factory as ff
import statsmodels.api as sm
import statsmodels.tsa.api as smt
from plotly.subplots import make_subplots

roarkemc

Get-ChildItem -Recurse -Depth 2 -Force | 
 Where-Object { $_.Mode -match "h" -and $_.FullName -like "*\.git" } |
 ForEach-Object {
    cd $_.FullName
    cd ../
    git pull --rebase
    cd ../
 }

roarkemc

ipython --ipython-dir=/tmp

roarkemc

pip install --upgrade --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org numpy

roarkemc

import matplotlib.pyplot as plt
from scipy import stats as scs
import statsmodels.tsa.api as smt
import statsmodels.api as sm


def tsplot(y, lags=None, figsize=(10, 8), style='bmh'):
    if not isinstance(y, pd.Series):
        y = pd.Series(y)
    with plt.style.context(style):    

roarkemc

def var_cf(
    hpr: pd.Series | pd.DataFrame, var_p: float = 0.01
) -> float | pd.Series:
    """Estimate Value-at-Risk (VaR) using the Cornish-Fisher approximation.

    Parameters
    ----------
    hpr : Series or DataFrame
        Holding period returns (HPR), noncumulative.
    var_p : float, default 0.01

roarkemc

def cvar_cf(
    hpr: pd.Series | pd.DataFrame, var_p: float = 0.01
) -> float | pd.Series:
    """Estimate Conditional Value-at-Risk (CVaR) using the Cornish-Fisher approximation.

    Parameters
    ----------
    hpr : Series or DataFrame
        Holding period returns (HPR), noncumulative.
    var_p : float, default 0.01

roarkemc

import numpy as np

EPSILON = 1e-10


def _error(actual: np.ndarray, predicted: np.ndarray):
    """ Simple error """
    return actual - predicted