The Risk Limited Glossary
M - the Commodity Futures Symbol which represents the June Delivery Month.|
Mark-to-Market (MTM) - the act of assigning a current market value to an asset.
Market Cap or Market Capitalization - a value placed on a shareholder-owned company. It is computed by multiplying the number of outstanding shares by the current share price.
Market Risk - the potential to experience financial losses due to fluctuations in the prices at which equities, foreign currencies, interest rate linked securities, and commodities can be bought or sold.
Markov Process - a stochastic process where the future expected value of a value (such as an asset price) is dependent only on the current value. Named after the Russian mathematician Andrey Andreyevich Markov (1856-1922).
Material Adverse Change (MAC) - generally any negative event that would be considered material by creditors. MAC clauses are often used as contract provisions that trigger defined changes, rights or actions.
Mean - is often considered as the simple arithmetic average of the sum of the observed values divided by the number of observations. It is customary to represent the Mean by µ.
Mean Reversion - a tendency for a stochastic process to remain near, or return over time to a long-run average.
MMBtu - one million British thermal units.
Model Risk - the risk of financial loss due to weakness or inaccuracy of a financial model used for valuing assets or portfolios of assets and/or managing risk.
Moments [of a statistical distribution] - the shape of any distribution can be described by its various 'moments'. The first four moments are:
The first moment is the Mean [see above] which indicates the central tendency of a distribution. The second moment is the Variance which indicates the width or deviation. The third moment is the Skewness which indicates any asymmetric "leaning" to either left or right. The fourth moment is the Kurtosis which indicates the degree of central "peakedness" or, equivalently, the "fatness of the outer tails.
Monte Carlo - an analytical technique in which a large number of simulations are run using random quantities for uncertain variables and using the distribution of results to infer which values are most likely. The name comes from the Monte Carlo section of the city-state of Monaco, which is known for its casinos and gambling.
Monte Carlo Simulation - as referred to above, in which the simulation randomly generates values for uncertain variables over-and-over in order to simulate an outcome.
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