Wilkie investment model

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The Wilkie investment model, often just called Wilkie model, is a stochastic asset model developed by A. D. Wilkie that describes the behavior of various economics factors as stochastic time series. These time series are generated by autoregressive models. The main factor of the model which influences all asset prices is the consumer price index. The model is mainly in use for actuarial work and asset liability management. Because of the stochastic properties of that model it is mainly combined with Monte Carlo methods.

Stochastic process mathematical object usually defined as a collection of random variables

In probability theory and related fields, a stochastic or random process is a mathematical object usually defined as a collection of random variables. Historically, the random variables were associated with or indexed by a set of numbers, usually viewed as points in time, giving the interpretation of a stochastic process representing numerical values of some system randomly changing over time, such as the growth of a bacterial population, an electrical current fluctuating due to thermal noise, or the movement of a gas molecule. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. They have applications in many disciplines including sciences such as biology, chemistry, ecology, neuroscience, and physics as well as technology and engineering fields such as image processing, signal processing, information theory, computer science, cryptography and telecommunications. Furthermore, seemingly random changes in financial markets have motivated the extensive use of stochastic processes in finance.

Time series Sequence of data over time

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In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term ; thus the model is in the form of a stochastic difference equation. In machine learning, an autoregressive model learns from a series of timed steps and takes measurements from previous actions as inputs for a regression model, in order to predict the value of the next time step.

Wilkie first proposed the model in 1986, in a paper published in the Transactions of the Faculty of Actuaries. [1] It has since been the subject of extensive study and debate. [2] [3] Wilkie himself updated and expanded the model in a second paper published in 1995. [4] He advises to use that model to determine the "funnel of doubt", which can be seen as an interval of minimum and maximum development of a corresponding economic factor.

Components

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References

  1. Wilkie, A.D. (1986). "A stochastic investment model for Actuarial Use" (PDF). Transactions of the Faculty of Actuaries. 39: 341–403.
  2. Geoghegan, T J; Clarkson, R S; Feldman, K S; Green, S J; Kitts, A; Lavecky, J P; Ross, F J M; Smith, W J; Toutounchi, A (27 January 1992). "Report on the Wilkie investment model". Journal of the Institute of Actuaries. 119: 173–228.
  3. Şahin, Şule; Cairns, Andrew; Kleinow, Torsten; Wilkie, A. D. (12 June 2008). Revisiting the Wilkie Investment Model (PDF). International Actuarial Association, AFIR/ERM Sectional Colloquium, Rome, 2008.
  4. Wilkie, A.D. (1995). "More on a stochastic asset model for actuarial use". British Actuarial Journal.