Kurtosis Definition, Types, and Importance


What Is Kurtosis?

Kurtosis is a statistical measure used to explain a attribute of a dataset. When usually distributed knowledge is plotted on a graph, it usually takes the type of an upsidedown bell. That is referred to as the bell curve. The plotted knowledge which might be furthest from the imply of the information normally kind the tails on both sides of the curve. Kurtosis signifies how a lot knowledge resides within the tails.

Distributions with a big kurtosis have extra tail knowledge than usually distributed knowledge, which seems to carry the tails in towards the imply. Distributions with low kurtosis have fewer tail knowledge, which seems to push the tails of the bell curve away from the imply.

For traders, excessive kurtosis of the return distribution curve implies that there have been many value fluctuations prior to now (constructive or destructive) away from the common returns for the funding. So, an investor may expertise excessive value fluctuations with an funding with excessive kurtosis. This phenomenon is called kurtosis threat.

Key Takeaways

  • Kurtosis describes the “fatness” of the tails present in chance distributions.
  • There are three kurtosis classes—mesokurtic (regular), platykurtic (lower than regular), and leptokurtic (greater than regular).
  • Kurtosis threat is a measurement of how typically an funding’s value strikes dramatically.
  • A curve’s kurtosis attribute tells you the way a lot kurtosis threat the funding you are evaluating has.

Understanding Kurtosis

Kurtosis is a measure of the mixed weight of a distribution’s tails relative to the middle of the distribution curve (the imply). For instance, when a set of roughly regular knowledge is graphed by way of a histogram, it exhibits a bell peak, with a lot of the knowledge residing inside three commonplace deviations (plus or minus) of the imply. Nonetheless, when excessive kurtosis is current, the tails prolong farther than the three commonplace deviations of the conventional bell-curved distribution.

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Kurtosis is usually confused with a measure of the peakedness of a distribution. Nonetheless, kurtosis is a measure that describes the form of a distribution’s tails in relation to its general form. A distribution will be sharply peaked with low kurtosis, and a distribution can have a decrease peak with excessive kurtosis. Thus, kurtosis measures “tailedness,” not “peakedness.”

Method and Calculation of Kurtosis

Calculating With Spreadsheets

There are a number of completely different strategies for calculating kurtosis. The only means is to make use of the Excel or Google Sheets system. As an illustration, assume you’ve the next pattern knowledge: 4, 5, 6, 3, 4, 5, 6, 7, 5, and eight residing in cells A1 by means of A10 in your spreadsheet. The spreadsheets use this system for calculating kurtosis:

[ n ( n + 1 ) / ( n – 1 ) ( n – 2 ) ( n – 3 ) ] [ Σ ( xi – x̄ ) / s ]4 – [ 3 ( n – 1 ) 2 ] / [ ( n – 2 ) ( n – 3 ) ]

Nonetheless, we’ll use the next system in Google Sheets, which calculates it for us, assuming the information resides in cells A1 by means of A10:


The result’s a kurtosis of -0.1518, indicating the curve has lighter tails and is platykurtic.

Calculating By Hand

Calculating kurtosis by hand is a prolonged endeavor, and takes a number of steps to get to the outcomes. We’ll use new knowledge factors and restrict their quantity to simplify the calculation. The brand new knowledge factors are 27, 13, 17, 57, 113, and 25.

It is necessary to notice {that a} pattern dimension must be a lot bigger than this; we’re utilizing six numbers to cut back the calculation steps. An excellent rule of thumb is to make use of 30% of your knowledge for populations underneath 1,000. For bigger populations, you need to use 10%.

First, it’s essential calculate the imply. Add up the numbers and divide by six to get 42. Subsequent, use the next formulation to calculate two sums, s2 (the sq. of the deviation from the imply) and s4 (the sq. of the deviation from the imply squared). Notice—these numbers don’t signify commonplace deviation; they signify the variance of every knowledge level.

  • s2 = Σ ( yi – ȳ )2
  • s4 = Σ ( yi – ȳ )4

The place:

  • yi = the ith variable of the pattern
  • ȳ = the imply

To get s2, use every variable, subtract the imply, after which sq. the consequence. Add all the outcomes collectively:

  • (27 – 42)2 = (-15)2 = 225
  • (13 – 42)2 = (-29)2 = 841
  • (17 – 42)2 = (-25)2 = 625
  • (57 – 42)2 = (15)2 = 225
  • (113 – 42)2 = (71)2 = 5041
  • (25 – 42)2 = (-17)2 = 289
  • 225 + 841 + 625+ 225 + 5,041 + 289 = 7,246

To get s4, use every variable, subtract the imply, and lift the consequence to the fourth energy. Add all the outcomes collectively:

  • (27 – 42)4 = (-15)4 = 50,625
  • (13 – 42)4 = (-29)4 = 707,281
  • (17 – 42)4 = (-25)4 = 390,625
  • (57 – 42)4 = (15)4 = 50,625
  • (113 – 42)4 = (71)4 = 25,411,681
  • (25 – 42)4 = (-17)4 = 83,521
  • 50,625+707,281+390,625+50,625+25,411,681+83,521 = 26,694,358

So, our sums are:

  • s2 = 7,246
  • s4 = 26,694,358

Now, calculate m2 and m4, the second and fourth moments of the kurtosis system:

  • m2 = s2 / n, or 7,246 / 6 = 1,207.67
  • m4 = s4 / n, or 26,694,358 / 6 = 4,449,059.67

We are able to now calculate kurtosis utilizing a system discovered in lots of statistics textbooks that assumes a superbly regular distribution with kurtosis of zero:

okay = ( m4 / m22 ) – 3

The place:

  • okay = kurtosis
  • m4 = fourth second
  • m2 = second second
  • 4,449,059.67 / 1,458,466.83 = 3.05

So, the kurtosis for the pattern variables is 3.05 – 3, or .05.

Forms of Kurtosis

There are three classes of kurtosis {that a} set of knowledge can show—mesokurtic, leptokurtic, and platykurtic. All measures of kurtosis are in contrast towards a standard distribution curve.



Mesokurtic (kurtosis = 3.0)

The primary class of kurtosis is mesokurtic distribution. This distribution has a kurtosis just like that of the conventional distribution, that means the acute worth attribute of the distribution is just like that of a standard distribution. Subsequently, a inventory with a mesokurtic distribution usually depicts a average stage of threat.

Leptokurtic (kurtosis > 3.0)

The second class is leptokurtic distribution. Any distribution that’s leptokurtic shows better kurtosis than a mesokurtic distribution. This distribution seems as a curve one with lengthy tails (outliers.) The “skinniness” of a leptokurtic distribution is a consequence of the outliers, which stretch the horizontal axis of the histogram graph, making the majority of the information seem in a slim (“skinny”) vertical vary.

A inventory with a leptokurtic distribution usually depicts a excessive stage of threat however the opportunity of greater returns as a result of the inventory has sometimes demonstrated giant value actions.

Whereas a leptokurtic distribution could also be “skinny” within the middle, it additionally options “fats tails”.

Platykurtic (kurtosis < 3.0)

The ultimate kind of distribution is platykurtic distribution. A lot of these distributions have brief tails (fewer outliers.). Platykurtic distributions have demonstrated extra stability than different curves as a result of excessive value actions not often occurred prior to now. This interprets right into a less-than-moderate stage of threat.

Utilizing Kurtosis

Kurtosis is utilized in monetary evaluation to measure an funding’s threat of value volatility. Kurtosis threat differs from extra generally used measurements corresponding to alpha, beta, r-squared, or the Sharpe ratio. Alpha measures extra return relative to a benchmark index, and beta measures the volatility a inventory has in comparison with the broader market.

R-squared measures the p.c of motion a portfolio or fund has that may be defined by a benchmark, and the Sharpe ratio compares return to threat. Kurtosis measures the quantity of volatility an funding’s value has skilled usually.

For instance, think about a inventory had a mean value of $25.85 per share. If the inventory’s value swung broadly and sometimes sufficient, the bell curve would have heavy tails (excessive kurtosis). This implies that there’s a lot of variation within the inventory value—an investor ought to anticipate extensive value swings typically.

If the inventory had gentle tails (low kurtosis), the investor may anticipate that the inventory value would swing broadly solely sometimes.

Why Is Kurtosis Vital?

Kurtosis explains how typically observations in some knowledge units fall within the tails vs. the middle of a chance distribution. In finance and investing, extra kurtosis is interpreted as a kind of threat generally known as “tail threat,” or the possibility of a loss occurring as a result of a uncommon occasion, as predicted by a chance distribution. If such occasions are extra frequent than predicted by a distribution, the tails are stated to be “fats.”

What Is Extra Kurtosis?

Extra kurtosis compares the kurtosis coefficient with that of a standard distribution. Most traditional distributions are assumed to have a kurtosis of three, so extra kurtosis can be kind of than three; nevertheless, some fashions assume a standard distribution has a kurtosis of zero, so extra kurtosis can be kind of than zero.

Is Kurtosis the Similar As Skewness?

No. Kurtosis measures how a lot of the information in a chance distribution are centered across the center (imply) vs. the tails. Skewness as an alternative measures the relative symmetry of a distribution across the imply.

The Backside Line

Kurtosis describes how a lot of a chance distribution falls within the tails as an alternative of its middle. In a standard distribution, the kurtosis is the same as three (or zero in some fashions). Constructive or destructive extra kurtosis will then change the form of the distribution accordingly. For traders, kurtosis is necessary in understanding tail threat, or how continuously “rare” occasions happen, given one’s assumption concerning the distribution of value returns.