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Local Volatility

The traditional calculation of volatility involves computing the standard deviation of returns,
which is based on the mean return. However, when the asset price exhibits a trending behavior,
the mean return could be significantly different from zero, and changing the length of the time
window used for the calculation could result in artificially high volatility values. This is because
more returns would be further away from the mean, leading to a larger sum of squared deviations.
To address this issue, our Local Volatility measure computes the standard deviation of the
differences between consecutive asset prices, rather than their returns. This provides a measure of
how much the price changes from one tick to the next, irrespective of the overall trend.
~ arxiv.org/abs/2308.14235
which is based on the mean return. However, when the asset price exhibits a trending behavior,
the mean return could be significantly different from zero, and changing the length of the time
window used for the calculation could result in artificially high volatility values. This is because
more returns would be further away from the mean, leading to a larger sum of squared deviations.
To address this issue, our Local Volatility measure computes the standard deviation of the
differences between consecutive asset prices, rather than their returns. This provides a measure of
how much the price changes from one tick to the next, irrespective of the overall trend.
~ arxiv.org/abs/2308.14235
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本著TradingView的真正精神,此腳本的創建者將其開源,以便交易者可以查看和驗證其功能。向作者致敬!雖然您可以免費使用它,但請記住,重新發佈程式碼必須遵守我們的網站規則。
免責聲明
這些資訊和出版物並不意味著也不構成TradingView提供或認可的金融、投資、交易或其他類型的意見或建議。請在使用條款閱讀更多資訊。