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<h2 class="font-roboto">Stochastic Volatility Model with PyMC</h2>
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<p class="mb-2 text-muted">Explore the concept of time-varying volatility in asset prices, modeled using a stochastic process. The example demonstrates the computation of this volatility based on the daily returns of the S&amp;P 500 using PyMC.</p>
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Thomas Wiecki
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<!-- <p>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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<a href="https://www.pymc.io/projects/examples/en/latest/case_studies/stochastic_volatility.html">Stochastic Volatility Model with PyMC</a></p>
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<p>If you are interested in seeing what we at PyMC Labs can do for you, then please email <a href="mailto:[email protected]">[email protected]</a>. We work with companies at a variety of scales and with varying levels of existing modeling capacity.
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