Granger causality tests based on reduced variable information

Abstract

Granger causality is a classical and important technique for measuring predictability from one group of time series to another by incorporating information of the variables described by a full vector autoregressive (VAR) process. However, in some applications economic forecasts need to be made based on information provided merely by a portion of variates (e.g., removal of a listed stock due to halting, suspension or delisting). This requires a new formulation of forecast based on an embedded subprocess of VAR, whose theoretical properties are often difficult to obtain. To avoid the issue of identifying the VAR subprocess, we propose a computation‐based approach so that sophisticated predictions can be made by utilizing a reduced variable information set estimated from sampled data. Such estimated information set allows us to develop a suitable statistical hypothesis testing procedure for characterizing all designated Granger causal relationships, as well as a useful graphical tool for presenting the causal structure over the prediction horizon. Finally, simulated data and a real example from the stock markets are used to illustrate the proposed method.

Exploring the Resilience of Islamic Stock in Indonesia and Asian Markets

“…Data on the stock market index were obtained from the Yahoo finance database (stock market index and trading volume, https://finance.yahoo.com/ quote/%5EJKII/ (accessed on 6 March 2024)) and the Thomson Reuters Eikon database (VaR, https://eikon.refinitiv.com/ (accessed on 20 December 2023)). Tseng et al (2024) examined the stationarity of time series data by analyzing the slope of the objective function, which relies on variance stationarity. Examining time series data allows us to analyze patterns of variance over time, revealing fluctuations and disparities during stable periods, crises, and pandemics.…”