Difference between revisions of "Statistical Models"
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== Definition == | == Definition == | ||
− | '''Statistical Models''' denotes a very broad category of models that are primarily based on empirical (historical) data. The class encompasses econometric models, regression models, | + | '''Statistical Models''' denotes a very broad category of models that are primarily based on empirical (historical) data. The class encompasses econometric models, regression models, [[Machine Learning]], [[Predictive Model | predictive models ]], typical VaR models etc. |
The behaviour of the data is systematized in a mathematical model, usually by making some essential assumptions about what is the underlying distribution (parametric models) but with minimal assumptions if sufficient volumes of data are available (non-parametric models) | The behaviour of the data is systematized in a mathematical model, usually by making some essential assumptions about what is the underlying distribution (parametric models) but with minimal assumptions if sufficient volumes of data are available (non-parametric models) | ||
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* Forward looking applicability | * Forward looking applicability | ||
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[[Category:Risk Models]] | [[Category:Risk Models]] |
Latest revision as of 16:21, 11 March 2023
Definition
Statistical Models denotes a very broad category of models that are primarily based on empirical (historical) data. The class encompasses econometric models, regression models, Machine Learning, predictive models , typical VaR models etc.
The behaviour of the data is systematized in a mathematical model, usually by making some essential assumptions about what is the underlying distribution (parametric models) but with minimal assumptions if sufficient volumes of data are available (non-parametric models)
Examples on non-statistical models are the variety of Structural Models and the No-arbitrage pricing Models where various logical constructs (e.g. causal relations, constraints) are included in the model as opposed to be inferred from data.
Issues and Challenges
- Availability and quality of historical data
- Correlation versus Causation
- Forward looking applicability