Confidence Intervals for a Normal Distribution
A confidence interval is an interval in which we expect the actual outcome to fall with a given probability (confidence). Consider the following statement:
In a normal distribution, 68% of the values fall within 1 standard deviation of the mean.
So, if X is a normal random variable, the 68% confidence interval for X is -1s <= X <= 1s. This is demonstrated in the following diagram.
The four commonly used confidence intervals for a normal distribution are:
- 68% of values fall within 1 standard deviation of the mean (-1s <= X <= 1s)
- 90% of values fall within 1.65 standard deviations of the mean (-1.65s <= X <= 1.65s)
- 95% of values fall within 1.96 standard deviations of the mean (-1.96s <= X <= 1.96s)
- 99% of values fall within 2.58 standard deviations of the mean (-2.58s <= X <= 2.58s)
The confidence interval is generally represented as , where n is the number of standard deviations. For example, n=1.65 for 90% confidence interval.
A stock portfolio has mean returns of 10% per year and the returns have a standard deviation of 20%. The returns are normally distribution. Calculate the 99% confidence interval.
95% confidence interval = 10% +/- 2.58*20%.
The confidence interval is -41.6% to 61.6%. This means with 99% confidence, the returns will range from -41.6% to 61.6%.
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- 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