Econometrics models


ARIMA (Autoregressive Integrated Moving Average) models can be used to forecast stock prices, but it is important to keep in mind that stock prices can be difficult to predict and are subject to a high degree of uncertainty. ARIMA models are a type of time series model that can be used to capture the patterns in a time series data, and to make predictions based on those patterns.

However, it is important to note that the stock prices are influenced by a wide range of factors, many of which are difficult to quantify and model accurately. Some of these factors include economic trends, company-specific news and events, political developments, and global market conditions. These factors can lead to a sudden and unexpected changes in stock prices that may not be captured by a model such as ARIMA.

Therefore, while ARIMA models can be a useful tool for forecasting stock prices, they should be used in combination with other methods and should be interpreted with caution. It is also important to use other types of analysis and consider other relevant factors when making investment decisions.


ETS (Error, Trend, Seasonality) model is another widely used time series forecasting model that can be applied to forecast stock prices. The ETS model is particularly useful when there is a seasonal pattern in the data, as it can capture both the trend and the seasonality.

WAVELET analysis

Wavelet analysis is a mathematical technique that is often used in signal processing, time series analysis, and data compression. The main advantage of wavelet analysis is that it can decompose a signal or time series into different frequency components, which can be useful for identifying patterns and trends at different time scales.

In the context of forecasting stock prices, wavelet analysis can help in several ways. Here are a few examples:

1. Decomposing the time series: By decomposing the stock price time series into different frequency components using wavelet analysis, you can identify patterns and trends at different time scales. This can help you to better understand the underlying dynamics of the stock price and potentially identify important factors that are driving the price.

2. Identifying trends and cycles: By analyzing the wavelet coefficients, you can identify trends and cycles in the stock price that may not be apparent in the original time series. This can help you to better understand the long-term and short-term movements of the stock price and potentially improve your forecasting accuracy.

3. Filtering noise: Wavelet analysis can be used to filter out noise from the stock price time series, which can improve the accuracy of your forecasting models.

Overall, wavelet analysis can be a useful tool for forecasting the stock price, especially if you are interested in analyzing the time series at different time scales and identifying underlying trends and patterns. However, it's important to note that there is no guarantee that any forecasting method will be accurate, as the future behavior of the stock price is inherently uncertain and influenced by a wide range of economic, political, and other factors.

GARCH models

GARCH, or Generalized Autoregressive Conditional Heteroscedasticity, is a statistical model commonly used to forecast volatility in financial markets. GARCH models assume that the variance of the asset's returns changes over time and is influenced by past shocks to the market.

To apply GARCH models to forecast stock prices, historical data of stock prices and returns are analyzed to estimate the parameters of the model. These parameters are then used to forecast future volatility and calculate the conditional variance of the asset's returns.

However, it is important to note that forecasting stock prices using GARCH models is not a guarantee of accuracy, as these models are based on past data and do not account for unforeseeable events or changes in the market. Nonetheless, GARCH models can provide valuable insights and assist traders and investors in making informed decisions about stock market investments.