Mode of Lognormal Distribution

Mode of Lognormal Distribution
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Suppose $$y=e^{x}$$ where x is normal with mean mu and variance sigma. Then I see how to derive mode of f(y) (distribution of y), as we need to find the value y that makes $$f'(y)==0$$ However, why is mode not simply $$e^{\mu}$$?

y is a monotonic function of x, and so when x reaches its mode, then y should also reach its mode. The mode of x is its mean (mu) hence y's mode should be $$e^{\mu}$$ what mistake have I made?

Thanks

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1 Answer

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$X$ has lognormal distribution if $X=e^Z$ where Z has normal distribution, $Z \sim N(\mu, \sigma^2)$. However, the density of X is then given by:

$f(x)=\frac{1}{x\sqrt{2\pi \sigma^2}} e^{-\frac{1}{2 \sigma^2}\left(\ln(x)-\mu\right)^2}$

Differentiating the density with respect to $x$ we get

$-\frac{1}{x^2\sqrt{2\pi \sigma^2}} e^{-\frac{1}{2 \sigma^2}\left(\ln(x)-\mu\right)^2} - \frac{1}{x\sqrt{2\pi \sigma^2}}e^{-\frac{1}{2 \sigma^2}\left(\ln(x)-\mu\right)^2} \frac{\ln(x)-\mu}{\sigma^2} \frac{1}{x} $

The mode is the value of x that maximizes the density. Thus, equating the above derivative to zero and simplifying, we get

$-1-\frac{\ln(x)-\mu}{\sigma^2}=0$

or

$x= e^{(\mu-\sigma^2)}$

To sum up, your mistake is that you have not used the correct density but the equation for the transformation of variables that gives us the random variable with log-normal distribution.

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David Miller
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David Miller

David Miller brings 15 years of experience in global economics, personal finance strategy, and market dynamics. He specializes in turning complex economic trends into actionable insights for everyday readers.