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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Against the Norm: Modeling Daily Stock Returns with the Laplace Distribution · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1906.10325/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1906.10325/feed.xml" rel="self" type="application/rss+xml" /><item><title>Against the Norm: Modeling Daily Stock Returns with the Laplace Distribution — mentioned by nwallin</title><link>https://news.ycombinator.com/item?id=27424345</link><guid isPermaLink="false">urn:hackerpapers:mention:1906.10325:27424345</guid><pubDate>Mon, 07 Jun 2021 16:21:06 GMT</pubDate><description>&lt;p&gt;The uniform distribution is the wrong distribution for this. A normal distribution is better, but also wrong- the normal distribution significantly under-predicts extreme events relative to the stock market. Can you try it with the Laplace distribution? It&amp;#x27;s a bell curve like the normal distribution, but has fat tails. Extreme events aren&amp;#x27;t common, but much more common than with a normal distribution. https://arxiv.org/pdf/1906.10325.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1906.10325/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-fin.MF</category></item></channel></rss>
