The field of financial econometrics using advanced tools and techniques has emerged over the last decade. The intention of this course is to help practitioners cut through the vast literature on financial time series models, focusing on the most important and useful empirical concepts. This course is expected to develop a sound background in quantitative analysis of financial time series. It offers a guide to analyse and model time series properties of financial data using Python and is intended for researchers and practitioners in the finance industry. Our aim is to provide a road map from academic prospective to the research issues that are important for researchers and practitioners.
The MDP aims to discuss a broader aspects of time series modeling on financial data. It covers applied econometric tools relating to financial time series using Python, with an emphasis on model building and analysis. The course aims to develop insights of financial modeling to analyse real world financial and business time series.
1. Fundamentals of Financial Time series a. Visualization of Time series data b. Analysis of trend and seasonality in financial data c. Autocorrelation functions and testing of stationarity of financial data d. Moving averages and time series smoothers 2. Univariate Time series modeling a. Introduction to ARIMA b. Building ARIMA model and forecasting market returns c. Modeling using ARIMAX 3. Introduction to Multivariate Time series models a. Granger Causality test b. Vector Autoregressive Model (VAR) 1. Impulse Response Function 2. Variance Decomposition 4. Introduction to Fuzzy Time series models
Researchers, Financial Analysts, Industry professionals
|Starts On||Jun 23, 2022|
|Faculty||Prof. Ajaya Kumar Panda|
|Duration||Professional Fee*(Per participant)||GST(18%)||Total Fees(Per Participant)||Programme Code|
|18 Hrs.||9,000.00||1,620.00||10,620.00||1 23 1 04|