Work detail

Modeling Dynamics of Correlations between Stock Markets with High-frequency Data

Author: Mgr. Vyacheslav Lypko
Year: 2012 - summer
Leaders: doc. PhDr. Jozef Baruník Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 76
Awards and prizes:
Link:
Abstract: In this thesis we focus on modelling correlation between selected stock markets using
high-frequency data. We use time-series of returns of following indices: FTSE, DAX
PX and S&P, and Gold and Oil commodity futures. In the first part of our empirical
work we compute daily realized correlations between returns of subject instruments and
discuss the dynamics of it. We then compute unconditional correlations based on
average daily realized correlations and using feedforward neural network (FFNN) to
assess how well the FFNN approximates realized correlations. We also forecast daily
realized correlations of FTSE:DAX and S&P:Oil pairs using heterogeneous
autoregressive model (HAR), autoregressive model of order p (AR(p)) and nonlinear
autoregressive neural network (NARNET) and compare performance of these models.

Partners

Deloitte

Sponsors

CRIF
McKinsey
Patria Finance