Financial Time series and Labs This a collection of financial time series labs designed to help learners explore and analyze financial data. Each lab is structured as a hands-on, practical exercise that allows learners to apply theoretical concepts to real-world financial problems. Topics covered in the labs may include time series modeling, forecasting, volatility analysis, risk management, and trading strategies. By completing the labs, learners will develop skills in data analysis, statistical modeling, and programming, and gain practical experience in working with financial data.
Lab_1: Deal with Exploratory Data Analysis in FTSA
The analysis includes the computation of simple and log returns, which are commonly used measures for assessing the performance of financial investments. The EDA aims to provide insights into the underlying patterns and trends in the data, and to identify potential outliers and anomalies. The results of the analysis can be used to inform investment decisions and to guide further research in FTSA.
Lab_2: Concepts relating to random walk, ARIMA and ARCH-based models. Explores concepts related to random walk, ARIMA, and ARCH-based models in Financial Time Series Analysis (FTSA). A random walk is a statistical model used to describe the movement of a variable over time, where each step is randomly determined. ARIMA models, on the other hand, are time series models that use a combination of autoregression (AR), moving average (MA), and differencing techniques to predict future values. Finally, ARCH-based models are used to model the volatility of financial returns, with the assumption that volatility is not constant over time. By incorporating these concepts into the EDA, the repository aims to provide a more comprehensive understanding of FTSA and its potential applications.