1. A method for estimation of changes in key performance indicator distribution, the method comprising: establishing, in a processor, a state-space model in a computer memory, the state-space model for handling measurement errors and modeling errors as noise, wherein time-varying indices have historical data and a correlation with key performance indicators; adaptively estimating, in the processor, parameters related to the time varying indices, and obtaining residuals from the adaptive estimate of the parameters; estimating, in the processor, a probability distribution in order to generate random numbers from the probability distribution; generating, in the processor, random numbers related to the estimated probability distribution, thereby enabling prediction of future indicator distributions through a Sequential Monte Carlo method; providing, in the processor, models to input key performance indicator intensity levels for a scenario level and to predict future values for a plurality of scenarios as point estimates; and distributing and synthesizing, in the processor, the point estimation results for the plurality of scenarios based on key performance indicator estimated probability distributions, wherein enabling prediction of future indicator distributions through a Sequential Monte Carlo method, includes: generating, in the processor, random numbers using a non-Gaussian white noise probability density; performing, in the processor, an update of the noise probability density as a function of time and modeling error for financial indices; calculating, in the processor, a likelihood of a data point using a conditional distribution of a noise probability density as a function of time and independent of the non-Gaussian white noise probability density; and re-sampling, in the processor, the data points based on a financial index data point distribution.