HEAVY-TAILED DISTRIBUTIONS AND THE CANADIAN STOCK MARKET RETURNS

David Eden, Paul Huffman and John Holman ()
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John Holman: Bank of Canada

Copernican Journal of Finance & Accounting, 2017, vol. 6, issue 2, 9-21

Abstract: Many of financial engineering theories are based on so-called “complete markets” and on the use of the Black-Scholes formula. The formula relies on the assumption that asset prices follow a log-normal distribution, or in other words, the daily fluctuations in prices viewed as percentage changes follow a Gaussian distribution. On the contrary, studies of actual asset prices show that they do not follow a log-normal distribution. In this paper, we investigate several widely-used heavy-tailed distributions. Our results indicate that the Skewed t distribution has the best empirical performance in fitting the Canadian stock market returns. We claim the results are valuable for market participants and the financial industry.

Keywords: Value at Risk; GSPTSE; Skewed t distribution (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:cpn:umkcjf:v:6:y:2017:i:2:p:9-21

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