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www.pymc-labs.io
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<title>Stochastic Volatility Model with PyMC - PyMC Labs</title>
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<h2class="font-roboto">Stochastic Volatility Model with PyMC</h2>
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<smallclass="text-muted">AUTHORED BY</small>
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Thomas Wiecki
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<!-- <p>2022-01-15</p> -->
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<pclass="font-lighter">2022-01-15</p>
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<hr><p>Asset prices have time-varying volatility (variance of day over day returns). In some periods, returns are highly variable, while in others very stable. Stochastic volatility models model this with a latent volatility variable, modeled as a stochastic process. In this example, we compute the time-varying volatility based on daily returns of the S&amp;P 500.</p>
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<p>For the full example, see:<br>
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<ahref="https://www.pymc.io/projects/examples/en/latest/case_studies/stochastic_volatility.html">Stochastic Volatility Model with PyMC</a></p>
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<h2class="font-roboto">Work with PyMC Labs</h2>
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<p>If you are interested in seeing what we at PyMC Labs can do for you, then please email<ahref="mailto:info@pymc-labs.io">info@pymc-labs.io</a>. We work with companies at a variety of scales and with varying levels of existing modeling capacity.
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We also run<ahref="https://www.pymc-labs.io/workshops/">corporate workshop training events</a> and can provide sessions ranging from introduction to Bayes to more advanced topics.
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