The Problem With Volatility

• 1 min read

  • Neural Nets
  • Options tags: [] meta: _aioseop_keywords: Volatility, Prediction, RapidMiner, Modeling, Quant dsq_thread_id: '181042072' author:

    Is that it's too damn volatile! I've been working on the next gen version of a directional volatility model and its been both frustrating and rewarding at the same time. As long as the model is re-optimized on a weekly (or daily) basis, the predictions tend to have a 60-70% accuracy.

    The problem is that you can't forward forecast it 1 year into the future because volatility is, well, "unpredictable!" :)

    Gasp, I said unpredictable (Christian please feel free to jump in)! Yes, its true volatility is unpredictable far out in the future BUT in smaller time frames the "stickiness law" works nicely. Noble prize winner, Robert Engle, proved (link coming) that volatility tends to cluster and that times of low volatility seem to be followed by more periods of low volatility. The same "pattern" exists for times of high volatility and I call this the stickiness law.

    So predicting the direction of volatility is feasible, based on my findings, as long as you continually update your model.  Since we just saw a period of high volatility recently, we're likely to see additional periods of high volatility soon. Guess that makes sense since September and October is coming!

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