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A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques.
Actuarial credibility describes an approach used by actuaries to improve statistical estimates. Although the approach can be formulated in either a frequentist or Bayesian statistical setting, the latter is often preferred because of the ease of recognizing more than one source of randomness through both "sampling" and "prior" information.
The Bornhuetter–Ferguson method was introduced in the 1972 paper "The Actuary and IBNR", co-authored by Ron Bornhuetter and Ron Ferguson. [4] [5] [7] [8]Like other loss reserving techniques, the Bornhuetter–Ferguson method aims to estimate incurred but not reported insurance claim amounts.
In credibility theory, a branch of study in actuarial science, the Bühlmann model is a random effects model (or "variance components model" or hierarchical linear model) used to determine the appropriate premium for a group of insurance contracts. The model is named after Hans Bühlmann who first published a description in 1967.
Wilkie, A. D. (1984) "A stochastic investment model for actuarial use", Transactions of the Faculty of Actuaries, 39: 341-403 Østergaard, Søren Duus (1971) "Stochastic Investment Models and Decision Criteria", The Swedish Journal of Economics, 73 (2), 157-183 JSTOR 3439055
Heath–Jarrow–Morton model and its application, Vladimir I Pozdynyakov, University of Pennsylvania; An Empirical Study of the Convergence Properties of the Non-recombining HJM Forward Rate Tree in Pricing Interest Rate Derivatives, A.R. Radhakrishnan New York University; Modeling Interest Rates with Heath, Jarrow and Morton.
Geometric Brownian motion is used to model stock prices in the Black–Scholes model and is the most widely used model of stock price behavior. [4] Some of the arguments for using GBM to model stock prices are: The expected returns of GBM are independent of the value of the process (stock price), which agrees with what we would expect in ...
Calculating option prices, and their "Greeks", i.e. sensitivities, combines: (i) a model of the underlying price behavior, or "process" - i.e. the asset pricing model selected, with its parameters having been calibrated to observed prices; and (ii) a mathematical method which returns the premium (or sensitivity) as the expected value of option ...