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autoregression

11 Jan 2016, 04:06 UTC
autoregression
(200 words excerpt, click title or image to see full post)

When it rains it pours.
And in California, for the past several years, it mostly hasn’t. This summer, the creeks in the Santa Cruz Mountains were reduced to slight trickles, which was sufficiently alarming to cause me to start watching the USGS’s real-time web-based flow monitor for the San Lorenzo River. The growing drought is evident in the nadirs of this plot of the streamflow for the past four years:

This summer and last, the mighty San Lorenzo was scraping by at about five feet per cubic second, which was thousands of times less than the peak flow at the end of 2012. Stream flow depends on a number of known factors — watershed characteristics, rainfall, ground saturation, etc. etc., all of which allow for an excellent short-term predictive model.
There is a provocative at-a-glance similarity of the stream flow process and the stock market volatility process, which is conveniently measured by the VIX index:

Analogies springing from the superficial commonality might be something interesting to think about when one is constructing predictive models for volatility, and indeed, the idea seems a bit more urgent at the beginning of this week than it was at the beginning of last…
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