Financial Time Series Monte Carlo Simulation, ARMA
The value of a financial asset is measured over time by a sequence of data points called a financial time series.
It is possible to track an asset’s performance, make predictions about future prices, and assess risk using these data points.
A Monte Carlo simulation is a statistical technique for generating random samples from a probability distribution. To estimate the probability of different outcomes in financial time series, this technique can be used to model the uncertainty inherent in them.
The ARMA model is a type of time series model that fits a linear relationship between the current value of a financial asset and its past value. An investment can be assessed for risk and forecasted using these models.
Analyzing financial time series using Monte Carlo simulation and ARMA models will be discussed in this article. We will discuss ways to monitor performance, predict outcomes, and assess risks using these techniques.
Furthermore, we will discuss how these techniques can be combined with other methods to obtain a more complete view of an investment’s risk and potential return.