HEAVY-TAILED DISTRIBUTIONS AND THE CANADIAN STOCK MARKET RETURNS
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 foll...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nicolaus Copernicus University in Toruń
2017-12-01
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| Series: | Copernican Journal of Finance & Accounting |
| Subjects: | |
| Online Access: | https://apcz.umk.pl/CJFA/article/view/15266 |
| Summary: | 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. |
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| ISSN: | 2300-1240 2300-3065 |