Time Series Modelling?

Time Series Modelling?

Quantitative time series modelling is a mathematical approach to modelling and analyzing time-dependent data. It is commonly used in finance, economics, and other fields to analyze and forecast data that varies over time, such as stock prices, exchange rates, and economic indicators.

One of the critical applications of quantitative time series modelling is in the field of finance, where it is used to predict the future value of financial assets such as stocks, bonds, and currencies. By incorporating a wide range of data and considering various factors, quantitative time series models can provide more accurate predictions than can be achieved using more traditional, qualitative approaches.

Types of Time Series Models

There are several different quantitative time series models, each of which uses a different set of assumptions and equations to generate output. Some of the most commonly used models include autoregressive (AR) models, moving average (MA) models, and autoregressive integrated moving average (ARIMA) models.

1) Autoregressive (AR) :

Autoregressive models are time series models that assume that a time-dependent variable's current value is a linear function of its past values. For example, an AR model of stock prices might assume that the current price of a stock is a linear function of its previous prices

2) Moving Average (MA) :

Moving average models, on the other hand, are time series model that assumes that the current value of a time-dependent variable is a linear function of the residual errors from a moving average of that variable. For example, an MA model of stock prices might assume that the current price of a stock is a linear function of the residual errors from the moving average of that stock's previous prices.

3) Autoregressive integrated moving average (ARIMA) :

Autoregressive integrated moving average models are time series model that combines both autoregressive and moving average components. These models are often used when the time series being modelled is not stationary, meaning that its statistical properties do not remain constant over time.

4) Generalized Autoregressive Conditional Heteroskedasticity (GARCH)

Another commonly used quantitative time series model is the generalized autoregressive conditional heteroskedasticity model. This type of model is used to model the volatility of a time series, which is the degree to which its values vary over time. By modelling volatility, GARCH models can assess the risk of financial assets and make more informed investment decisions.

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