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- W3187836003 abstract "The purpose of this notebook is to demonstrate a simple use case for MCMC (Markov Chain Monte Carlo) methods for solving for parameters of a known relationship in a system which is prone to black swan events and large jumps. This is only a test of principal, using simulated data, for the purpose of better understanding how MCMC reacts to outliers. This test was motivated by the use of MCMC in jump diffusion models, which face the problem of differentiating between movements caused by “jumps” (news events, politics, or some other abnormal factor) and attempting to fit the “normal” dynamics of a time series. In this notebook, I will create a stochastic process y which depends on both an observable variable xx as well as a random component. For the sake of simplicity (and because I am using OLS as a benchmark) y will have no autocorrelation and will be homoscedastic and its relationship to x will be known ahead of time to the model, however the parameters of this relationship θ must be estimated. The notebook will conclude by comparing the ability for MCMC to interpolate this stochastic process against using OLS." @default.
- W3187836003 created "2021-08-16" @default.
- W3187836003 creator A5086016731 @default.
- W3187836003 date "2018-01-01" @default.
- W3187836003 modified "2023-09-27" @default.
- W3187836003 title "MCMC vs OLS As a Tool for Interpolating Stochastic Processes" @default.
- W3187836003 doi "https://doi.org/10.2139/ssrn.3100153" @default.
- W3187836003 hasPublicationYear "2018" @default.
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