Autoregressive Processes

Autoregressive processes are mathematical models that express the current value of a variable as a function of its past values and a stochastic term. In finance, these are used to model the evolution of asset prices and volatility over time.

By assuming that the future is partially determined by the past, these models provide a framework for forecasting and risk assessment. In the context of derivatives, autoregressive models help in predicting the path of underlying asset prices, which is necessary for pricing path-dependent options.

These processes allow for the inclusion of historical trends and cycles in quantitative models. They are fundamental to modern financial econometrics.

By using these tools, analysts can better understand the underlying structure of market data and improve their predictive accuracy.