Correction to Nomenclature in Volatility Model#2978
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springcoil merged 1 commit intopymc-devs:masterfrom May 18, 2018
aaronjmaxwell:feature/nomenclature-fix-in-notebook
Merged
Correction to Nomenclature in Volatility Model#2978springcoil merged 1 commit intopymc-devs:masterfrom aaronjmaxwell:feature/nomenclature-fix-in-notebook
springcoil merged 1 commit intopymc-devs:masterfrom
aaronjmaxwell:feature/nomenclature-fix-in-notebook
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This small correction is in response to issue Stochastic Volatility Example #2566. It concerns the two different examples of the Volatility Model used in the PyMC3 notebooks to introduce users to the wonder that is Bayesian modeling. In getting_started.ipynb, under: --Case Study 1: Stochastic volatility ---The Model the model specification uses `y_i` to represent the daily precentage returns. However, later on in the `with pm.Model() as sp500_model:` block the dummy variable `r` is used to represent the daily returns as well as the tensor variable name. The second correction also concerns the useage of `s_i` to represent the volatility process in daily returns. In both Volatility Process walkthroughs (there is a stand-alone notebook dedicated to it) the model specs treat `s_i` as the standard deviation of the StudentT distribution used to model the log-returns. In the PyMC3 API docs on the StudentT, the distribution is defined with `lambda` representing the precision. This is why the `volatility_process` variable is mapped from `pm.math.exp(-2 * s)` in the `pm.Model()` block. However, when the returns were defined in the model block the **kwarg was `lambda=1/volatility_process`. This has been fixed. Thanks to @twiecki and the OP for highlighting this error.
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This small correction is in response to issue Stochastic Volatility Example #2566. It concerns the two different examples of the Volatility Model used in the PyMC3 notebooks to introduce users to the
wonder that is Bayesian modeling. In getting_started.ipynb, under:
Case Study 1: Stochastic volatility
The Model
the model specification uses
y_ito represent the daily precentage returns. However, later on inthe
with pm.Model() as sp500_model:block the dummy variableris used to represent the dailyreturns as well as the tensor variable name.
The second correction also concerns the useage of
s_ito represent the volatility process in dailyreturns. In both Volatility Process walkthroughs (there is a stand-alone notebook dedicated to it)
the model specs treat
s_ias the standard deviation of the StudentT distribution used to model thelog-returns. In the PyMC3 API docs on the StudentT, the distribution is defined with
lambdarepresenting the precision. This is why the
volatility_processvariable is mapped frompm.math.exp(-2 * s)in thepm.Model()block. However, when the returns were defined in themodel block the **kwarg was
lambda=1/volatility_process. This has been fixed.Thanks to @twiecki and the OP for highlighting this error. Alternatively, if there is apprehension to the second change since model specs will not correspond to the trace that was returned, one could simply keep
lambda=1/volatility_processand change the model specification tolog(r_i) \sim t(\nu, 0, exp(2s_{i}). Then at least the MCMC sampler would not need to be re-run (difficult considering that Google has blocked requests to the historical finance data API).