Kurtosis investopedia forex
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Excess kurtosis means the distribution of event outcomes have lots of instances of outlier results, causing fat tails on the bell-shaped distribution curve. Normal distributions have a kurtosis of three. Excess kurtosis can, therefore, be calculated by subtracting kurtosis by three.
Since normal distributions have a kurtosis of three, excess kurtosis can be calculated by subtracting kurtosis by three. Excess kurtosis is an important tool in finance and, more specifically, in risk management. With excess kurtosis, any event in question is prone to extreme outcomes. It is an important consideration to take when examining historical returns from a particular stock or portfolio.
The higher the kurtosis coefficient is above the normal level—or the fatter the tails on the return distribution graph—the more likely that future returns will be either extremely large or extremely small. Stock prices with a higher likelihood of outliers on either the positive or negative side of the mean closing price can be said to have either positive or negative skewness, which can be related to kurtosis. Types of Excess Kurtosis The values of excess kurtosis can be either negative or positive.
When the value of an excess kurtosis is negative, the distribution is called platykurtic. This kind of distribution has a tail that's thinner than a normal distribution. When applied to investment returns, platykurtic distributions—those with negative excess kurtosis—generally produce results that won't be very extreme, which are great for investors who don't want to take a lot of risk. When excess kurtosis is positive, it has a leptokurtic distribution.
The tails on this distribution is heavier than that of a normal distribution, indicating a heavy degree of risk. The returns on an investment with a leptokurtic distribution or positive excess kurtosis will likely have extreme values. Investors who are willing and able to take a lot of risk will probably want to invest in a vehicle with a positive excess kurtosis.
Excess kurtosis can be at or near zero as well, so the chance of an extreme outcome is rare. This is known as a mesokurtic distribution. The tails of this kind of distribution is similar to that of a normal distribution. Example of Excess Kurtosis Let's use a hypothetical example of excess kurtosis. Suppose that a zoologist is interested in the distribution of elephant birth weights, so she contacts zoos and sanctuaries around the world and asks them to share their data.
She collects birth weight data for female baby elephants: From the graph, we can see that the frequency distribution shown by the gray bars approximately follows a normal distribution shown by the green curve.
Normal distributions are mesokurtic. The zoologist calculates the kurtosis of the sample. She finds that the kurtosis is 3. Mesokurtic distributions have outliers that are neither highly frequent, nor highly infrequent, and this is true of the elephant birth weights. Occasionally, a female baby elephant will be born weighing less than or more than lbs. Prevent plagiarism, run a free check. What is a platykurtic distribution? A platykurtic distribution is thin-tailed, meaning that outliers are infrequent.
Platykurtic distributions have less kurtosis than a normal distribution. In other words, platykurtic distributions have: A kurtosis of less than 3 An excess kurtosis of less than 0 Platykurtosis is sometimes called negative kurtosis, since the excess kurtosis is negative. Although many platykurtic distributions have a flattened peak, some platykurtic distributions have a pointy peak.
Platykurtic distribution example A sociologist is studying the social media use of students at a small high school. Instead, it approximately follows a uniform distribution shown by the purple curve. Uniform distributions are platykurtic. The sociologist calculates that the kurtosis of the sample is 1. He concludes that the distribution is platykurtic.
Platykurtic distributions have a low frequency of outliers.
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A Simple Explanation of Forex - Investopedia AcademyFull Bio Pete Rathburn is a freelance writer, copy editor, and fact-checker with expertise in economics and personal finance.
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| Kurtosis investopedia forex | Journal of Financial Economics, September Instead, the VaR calculation of a portfolio containing nonlinear exposures is usually calculated using Monte Carlo VaR simulations of options pricing models to estimate the VaR of the portfolio. We also reference original research from other reputable publishers where appropriate. As a result, investors tend to experience abnormally kurtosis investopedia forex and low periods of performance. Distributions that are characterized by fat tails are often seen when looking at hedge fund returns, for example. |
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