Financial Econometrics: Analyzing the Behaviour of Financial Markets and Assets Using Econometric Techniques

Dr Rajendra Singh , Neelam Singh

Volume 4, Issue 2 2020

Page: 29-36

Abstract

The complex behaviour of financial markets and assets may be better understood by the application of econometric tools, which are the focus of financial econometrics. Situated at the crossroads of economics, statistics, and finance, this field models and evaluates the intricate linkages that control financial variables through the use of quantitative approaches. The main objective is to discover the hidden dynamics and patterns in financial data so that we may better understand asset pricing, market movements, and risk factors. The links between different financial variables are quantified and interpreted using econometric methods including regression analysis, time series analysis, and volatility modelling. Financial econometrics is driven by the goal of improving our understanding of market movements, developing successful investment strategies, and providing credible risk assessments via the use of rigorous statistical methods. By providing a methodical framework for comprehending and forecasting the actions of financial markets and assets, this area significantly influences financial decision-making.

Back Download



References

  • Tsay, R. S. (2015). Analysis of Financial Time Series. John Wiley & Sons.
  • Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2017). The Econometrics of Financial Markets. Princeton University Press.
  • Bollerslev, T. (2016). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Engle, R. F. (2018). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1008.
  • . Hamilton, J. D. (2019). Time Series Analysis. Princeton University Press.
  • Greene, W. H. (2019). Econometric Analysis. Pearson Education.
  • Alexander, C. (2018). Market Risk Analysis: Practical Financial Econometrics. John Wiley & Sons.
  • Brooks, C. (2015). Introductory Econometrics for Finance. Cambridge University Press.
  • Enders, W. (2015). Applied Econometric Time Series. John Wiley & Sons.
  • Veronis, N., Thomakos, D. D., & Dritsakis, N. (2017). Financial Time Series Forecasting using Improved Wavelet Neural Networks. International Journal of Business and Economics, 3(1), 1-10.
  • Diebold, F. X., & Mariano, R. S. (2016). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3), 253-263.
  • Ruey, S. T. (2016). Time Series Analysis and Its Applications: With R Examples. Springer.
  • Gujarati, D. N. (2019). Basic Econometrics. McGraw-Hill Education.
  • Hamilton, J. D. (2017). Time Series Analysis. Princeton University Press.
  • Lütkepohl, H. (2015). New Introduction to Multiple Time Series Analysis. Springer Science & Business Media.
  • Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer.
  • Davidson, R., & MacKinnon, J. G. (2017). Econometric Theory and Methods. Oxford University Press.
  • Granger, C. W. (2017). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
  • Hansen, L. P. (2020). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50(4), 1029-1054.
  • Jorion, P. (2016). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill Education.
  • McAleer, M. (2016). Automated inference and learning in modeling financial volatility. Econometric Theory, 19(3), 435-461.
  • Stock, J. H., & Watson, M. W. (2017). Introduction to Econometrics. Pearson Addison Wesley.

Looking for Paper Publication??

Come to us.