This is not the definition of Value at Risk. Value at Risk (VaR) measures the potential loss in value of a risky asset or event over a defined period for a given confidence interval. It is based on the assumption that the possible outcome of the event is represented by a normal distribution (bell curve). With a normal distribution, we know that 95% of the results will lie within 1.96 standard deviations of the mean, and that 99% of the results will lie within 2.57 standard deviations of the mean. Using this information, we can predict what the range of results will be with a measured level of confidence.
This is not the definition of Value at Risk. Value at Risk (VaR) measures the potential loss in value of a risky asset or event over a defined period for a given confidence interval. It is based on the assumption that the possible outcome of the event is represented by a normal distribution (bell curve). With a normal distribution, we know that 95% of the results will lie within 1.96 standard deviations of the mean, and that 99% of the results will lie within 2.57 standard deviations of the mean. Using this information, we can predict what the range of results will be with a measured level of confidence.
Value at Risk (VaR) measures the potential loss in value of a risky asset or event over a defined period for a given confidence interval. It is based on the assumption that the possible outcome of the event is represented by a normal distribution (bell curve). With a normal distribution, we know that 95% of the results will lie within 1.96 standard deviations of the mean, and that 99% of the results will lie within 2.57 standard deviations of the mean. Using this information, we can predict what the range of results will be with a measured level of confidence.
This is not the definition of Value at Risk. Value at Risk (VaR) measures the potential loss in value of a risky asset or event over a defined period for a given confidence interval. It is based on the assumption that the possible outcome of the event is represented by a normal distribution (bell curve). With a normal distribution, we know that 95% of the results will lie within 1.96 standard deviations of the mean, and that 99% of the results will lie within 2.57 standard deviations of the mean. Using this information, we can predict what the range of results will be with a measured level of confidence.
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