- What is a Probability Distribution
- Discrete Vs. Continuous Random Variable
- Cumulative Distribution Function
- Discrete Uniform Random Variable
- Bernoulli and Binomial Distribution
- Stock Price Movement Using a Binomial Tree
- Tracking Error and Tracking Risk
- Continuous Uniform Distribution
- Normal Distribution
- Univariate Vs. Multivariate Distribution
- Confidence Intervals for a Normal Distribution
- Standard Normal Distribution
- Calculating Probabilities Using Standard Normal Distribution
- Shortfall Risk
- Safety-first Ratio
- Lognormal Distribution and Stock Prices
- Discretely Compounded Rate of Return
- Continuously Compounded Rate of Return
- Option Pricing Using Monte Carlo Simulation
- Historical Simulation Vs Monte Carlo Simulation
Lognormal Distribution and Stock Prices
The concept of lognormal distribution is very closely related to the concept of normal distribution.
Let’s say we have a random variable Y. This variable Y will have a lognormal distribution if the natural log of Y (ln Y) is normally distributed. So, we check if the natural logarithm of a random variable is normally distributed or not. If it is, then the random variable itself will have a lognormal distribution.
A lognormal distribution has two important characteristics:
- It has a lower bound of zero.
- The distribution is skewed to the right, i.e., it has a long right tail.
Note that this is in contrast with a normal distribution which has zero skew and can take both negative and positive values. Just like a normal distribution, a lognormal distribution is also described by just two parameters, namely, m and s.
A lognormal distribution is commonly used to describe distributions of financial assets such as share prices. A lognormal distribution is more suitable for this purpose because asset prices cannot be negative. An important point to note is that when the continuously compounded returns of a stock follow normal distribution, then the stock prices follow a lognormal distribution. Even in cases where returns do not follow a normal distribution, stock prices are better described by a lognormal distribution.
Consider the expression Y = exp(X).
Exp(X) or ex is the opposite of taking logs. If we take log on both side, we will have ln y = X
So, if we assume that X has normal distribution, then Y has lognormal distribution (because ln Y is normally distributed).
We can compare this with how stock prices move. Let’s say that the initial stock price is S0 and the stock price after period t is St. If the rate of return r is continuously compounded then the future stock price can be expressed as:
St = S0*EXP(r)
S0 is a known quantity and is a constant. This expression is the same as Y = exp(X).
Therefore, if r is normally distributed, the stock price will be lognormally distributed.