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Mean reversion is a financial term for the assumption that an asset's price will tend to converge to the average price over time. [1] [2]Using mean reversion as a timing strategy involves both the identification of the trading range for a security and the computation of the average price using quantitative methods.
Additionally, the stochastic process of the underlying(s) may be specified so as to exhibit jumps or mean reversion or both; this feature makes simulation the primary valuation method applicable to energy derivatives. [11] Further, some models even allow for (randomly) varying statistical (and other) parameters of the sources of uncertainty.
In finance, statistical arbitrage (often abbreviated as Stat Arb or StatArb) is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities (hundreds to thousands) held for short periods of time (generally seconds to days). These strategies are supported by ...
Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc. When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise.
The parameter corresponds to the speed of adjustment to the mean , and to volatility. The drift factor, a ( b − r t ) {\displaystyle a(b-r_{t})} , is exactly the same as in the Vasicek model. It ensures mean reversion of the interest rate towards the long run value b {\displaystyle b} , with speed of adjustment governed by the strictly ...
Pairs Trading: Pairs trade is a trading strategy that consists of identifying similar pairs of stocks and taking a linear combination of their price so that the result is a stationary time-series. We can then compute Altman_Z-score for the stationary signal and trade on the spread assuming mean reversion: short the top asset and long the bottom ...
The pairs trade helps to hedge sector- and market-risk. For example, if the whole market crashes, and the two stocks plummet along with it, the trade should result in a gain on the short position and a negating loss on the long position, leaving the profit close to zero in spite of the large move.
The amount of information (the covariance matrix, specifically, or a complete joint probability distribution among assets in the market portfolio) needed to compute a mean-variance optimal portfolio is often intractable and certainly has no room for subjective measurements ('views' about the returns of portfolios of subsets of investable assets ...