Volatility-Based Envelopes and Their Calculation Ambiguities
Summary
The document reviews a proposed volatility-based envelope indicator and identifies uncertainties in translating its described steps into a reproducible calculation. The outlined method uses the standard deviation of daily percentage changes, combines that measure with an average return and a recent price to form upper and lower bands, then smooths the raw bands with a centered weighted moving average. A multiplier controls the band width, but the source question is unsure how to interpret the weighting and forecast procedure.
The author asks which daily price changes should be measured, whether the average return uses the same lookback as volatility, and how the centered average can be applied when it needs future observations. The final forecasting step is especially unclear: the described correlations lack a clear definition of the paired series and how missing smoothed values are produced. No implementation, backtest, or performance evidence is supplied, so the document is best read as a record of specification questions rather than validation that the indicator works.
Key ideas
- The proposed bands use recent price, average percentage change, and return volatility.
- The upper and lower bands are separated using a volatility multiplier whose value is treated as a parameter.
- A centered weighted moving average requires future observations at the end of a series.
- The proposed method for forecasting missing values is not sufficiently specified to reproduce confidently.
- The document supplies no empirical test of the indicator's predictive value.
Tags
Full text
# Best practice when computing beta coefficient # Best practice when computing beta coefficient I was wondering how one should choose parameters such as "frequency" of returns (daily, monthly etc.), "time frame" (1 or 3 or 5 years of historical data etc), benchmark (same of the portfolio or the specific one of the market of each asset etc. - i.e. AAPL.US and ^GSPC.US, LUX.MI and FTSEMIB.MI) in computing beta coefficient of a given asset against a given benchmark (i.e. AAPL.US and ^GSPC.US) with simple linear regression model. Different data providers show different beta coefficient of a same asset so is there a best practice maybe related to the personal "investment horizon"? Please bare in mind this from the view point of estimating returns via CAPM for a better mean-variance portfolio optimization. ## Answer by nbbo2 (score 3) https://quant.stackexchange.com/a/48812 A widely accepted method to estimate Beta is the Vasicek (1973) method, which computes a preliminary estimate of Beta by linear regression and then "shrinks it" (adjusts it) towards 1 to compensate for the fact that the OLS Betas tend to be too extreme (too far from 1) in the cross section. I consider it the standard. Recently Ivo Welch has published a new method which is relatively simple and he claims is superior to a variety of other methods, including the Vasicek method. It has the potential to become a new standard. His paper is Simpler Better Market Betas (SSRN link) and includes an exhaustive (somewhat exhausting) discussion of previously known methods to calculate Beta. ## Answer by jason m (score 0) https://quant.stackexchange.com/a/45751 There is no single answer here as beta is just a statistics. Do not let the qualitative-finance world tell you what beta is (or isn't). Beta, in corporate finance, can be stated somewhat differently - so know your audience. Beta over 3-months will change more quickly, and a 5-year beta will change more slowly. You need to think about the question you are trying to answer, and decide from there what is correct for your problem.
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.