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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 ...
Specific algorithms are closely guarded by their owners. Many practical algorithms are in fact quite simple arbitrages which could previously have been performed at lower frequency—competition tends to occur through who can execute them the fastest rather than who can create new breakthrough algorithms. [citation needed]
In practice, execution risk, persistent and large divergences, as well as a decline in volatility can make this strategy unprofitable for long periods of time (e.g. 2004-2007). It belongs to wider categories of statistical arbitrage, convergence trading, and relative value strategies. [57]
One possibility to "fix" the formula is use the stochastic collocation method and to project the corresponding implied, ill-posed, model on a polynomial of an arbitrage-free variables, e.g. normal. This will guarantee equality in probability at the collocation points while the generated density is arbitrage-free. [4]
In finance, volatility arbitrage (or vol arb) is a term for financial arbitrage techniques directly dependent and based on volatility. A common type of vol arb is type of statistical arbitrage that is implemented by trading a delta neutral portfolio of an option and its underlying .
Thorp wrote many articles about option pricing, Kelly criterion, statistical arbitrage strategies (6-parts series), [18] and inefficient markets. [ 19 ] In 1991, Thorp was an early skeptic of Bernie Madoff 's supposedly stellar investing returns which were proved to be fraudulent in 2008.
In finance, arbitrage pricing theory (APT) is a multi-factor model for asset pricing which relates various macro-economic (systematic) risk variables to the pricing of financial assets. Proposed by economist Stephen Ross in 1976, [ 1 ] it is widely believed to be an improved alternative to its predecessor, the capital asset pricing model (CAPM ...
Engine for Likelihood-Free Inference. ELFI is a statistical software package written in Python for Approximate Bayesian Computation (ABC), also known e.g. as likelihood-free inference, simulator-based inference, approximative Bayesian inference etc. [83] ABCpy: Python package for ABC and other likelihood-free inference schemes.