The hyperbolic distribution is characterized by the logarithm of the probability density function (PDF) being represented by a hyperbola. In layman’s terms, this means that the distribution decreases exponentially, though more slowly than the normal distribution.
The hyperbolic distribution is suitable for modeling phenomena where numerically large values are more probable than what is seen in the normal distribution. Some examples of this include returns from financial assets and turbulent wind speeds. The hyperbolic distributions form a subclass of the generalised hyperbolic distributions.
In this blog post, we will take a more in-depth look at the hyperbolic distribution, discussing what it is and why it matters. We’ll also provide some examples to illustrate key points. By the end of this post, you should have a better understanding of the hyperbolic distribution and its role in statistics.
What is the Hyperbolic Distribution?
As we noted earlier, the hyperbolic distribution is characterized by the logarithm of the probability density function being a hyperbola. Put more simply, it’s a continuous probability distribution in which values decrease exponentially—but not as quickly as they do in the normal distribution. Essentially, this type of distribution models situations where large values are more probable than what is typically seen in nature.
To visualize this concept, let’s consider an example involving dice rolls. If we were to roll two six-sided dice, we would expect to see most results cluster around 7 (the median value), with fewer results falling above and below that number. However, if we were to roll 100 dice instead of just two, we would expect to see more results clustered around 7—but we would also expect to see some extremely high numbers, such as 50 or 60. In other words, when you increase the number of trials, you also increase the probability of seeing outliers—and that’s precisely what the hyperbolic distribution models.
Why Does the Hyperbolic Distribution Matter?
The hyperbolic distribution arises naturally in many situations where large values are more probable than small ones. For instance, returns from financial assets tend to follow a hyperbolic pattern: investors typically see smaller returns early on (e.g., in their first year or two), after which returns gradually increase until they plateau or even decline slightly as retirement approaches. The same is true for turbulent wind speeds: early gusts tend to be calmer, after which wind speeds gradually ramp up until they reach their peak—at which point they start to die down again.
In both cases, it’s important to have a model that captures this pattern accurately; otherwise, essential information could be missed. For instance, if an investor only looked at data from their first few years on the job (when returns are relatively low), they might make suboptimal decisions about how much to save for retirement. Similarly, if meteorologists only looked at data from calm days (when wind speeds are low), they might not be able to effectively predict and prepare for major storms. By understanding and using the hyperbolic distribution accordingly, however, these issues can be avoided entirely.
The hyperbolic distribution is a continuous probability distribution characterized by values that decrease exponentially—but not as quickly as in the normal distribution. This type of distribution is commonly used to model situations where large values are more probable than what is typically seen in nature. The benefits of using the hyperbolic distribution include improved accuracy for predictions and decisions related to financial assets and turbulent wind speeds (among other things).