Extreme-value theorems are specifically designed to model extreme events. These theorems, such as the Fisher-Tippett theorem, are used to model the distribution of the maximum or minimum of a number of samples of a random variable. They are particularly useful in fields such as meteorology, hydrology, and environmental engineering, where it is important to model extreme events such as floods, storms, and heatwaves. These theorems provide a way to estimate the probability of such extreme events occurring, which can be used to inform risk management strategies and infrastructure design.

Choice A is incorrect. The central limit theorem is a fundamental theorem in statistics that states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the shape of the population distribution. This theorem does not specifically deal with extreme events or outliers.

Choice B is incorrect. The standard normal distribution, also known as Gaussian distribution, models data where most observations are clustered around the mean and tails are thin which implies low probability for extreme events. Therefore, it's not suitable for modeling extreme events.

Choice D is incorrect. The exponential distribution models time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. It does not focus on modeling extreme values or outliers.

Things to Remember

• Extreme events are rare occurrences that deviate significantly from the norm and can have a major impact on financial markets and risk management strategies.
• Extreme-value theory is a branch of statistics dealing with the extreme deviations from the median of probability distributions.
• Extreme events can be caused by factors such as natural disasters, market crashes, geopolitical events, and other unexpected occurrences.
• Extreme-value theorems help in estimating the likelihood of extreme events and are