Multivariate GARCH models and realized covariance prediction

Staropoli, Roberto (A.A. 2019/2020) Multivariate GARCH models and realized covariance prediction. Tesi di Laurea in Empirical finance, Luiss Guido Carli, relatore Paolo Santucci de Magistris, pp. 177. [Master's Degree Thesis]

[img] PDF (Full text)
Restricted to Registered users only

Download (7MB) | Request a copy

Abstract/Index

Statistical and portfolio optimization basic concepts. Risk and return. The creation of an efficient frontier. ARMA processes. Autoregressive (AR) processes. Estimation of an ARMA(p,q) process. Forecasting an ARMA process. GARCH volatility modelling. Univariate autoregressive conditional heteroskedastic (ARCH) model. Univariate generalized autoregressive conditional heteroskedastic (GARCH) model. Multivariate GARCH models. In-sample analysis. ARMA-GARCH estimation. Multivariate estimation. Out-the-sample analysis. ARMA-GARCH forecasting. Comparison between models.

References

Bibliografia: pp. 116-118.

Thesis Type: Master's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56)
Chair: Empirical finance
Thesis Supervisor: Santucci de Magistris, Paolo
Thesis Co-Supervisor: Grassi, Stefano
Academic Year: 2019/2020
Session: Autumn
Deposited by: Alessandro Perfetti
Date Deposited: 14 May 2021 13:54
Last Modified: 14 May 2021 13:54
URI: https://tesi.luiss.it/id/eprint/29490

Downloads

Downloads per month over past year

Repository Staff Only

View Item View Item