Does Investor Sentiment Affect Clean Energy Stock? Evidence from TVP-VAR-Based Connectedness Approach

Tiantian Liu,Shigeyuki Hamori

doi:10.3390/en14123442

Abstract

We investigated the connectedness of the returns and volatility of clean energy stock, technology stock, crude oil, natural gas, and investor sentiment based on the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach. The empirical results indicate that the average total connectedness is higher in the volatility system than in the return system. The investor sentiment has a weak impact on clean energy stock. Our results show that the dynamic total connectedness across assets in the system varies with time. Furthermore, the dynamic total connectedness increases significantly during financial turmoil. Dynamic total volatility connectedness is more sensitive to financial turmoil. By comparing the connectedness estimated by the TVP-VAR model with the rolling-window VAR model, we find the dynamic total return connectedness of the TVP-VAR model is similar to the estimated results of a 200 day rolling-window VAR model.

Highlights

Energy Stock? Evidence fromWith the deepening of the sustainable development concept, the clean energy industry has proliferated in recent years
We applied the time-varying parameter vector autoregressive (TVP-vector autoregression (VAR))-based connectedness approach proposed by Antonakakis and Gabauer [20], who extended the connectedness approach of Diebold and Yilmaz [19], by combining it with the TVP-VAR method of Koop and Korobilis [24]
This section reports the results of the connectedness across S&P GCE, Henry Hub natural gas, WTI crude oil, technology stock (Tech), and investor sentiment (SI) from estimating the TVP-VAR-based connectedness approach proposed by Antonakakis and Gabauer [21]

Summary

Introduction

With the deepening of the sustainable development concept, the clean energy industry has proliferated in recent years. Given the rapid expansion of the clean energy industry, the performance of the clean energy industry in financial markets has attracted increased attention from policymakers and investors. Most investors are interested in considering clean energy stock in their asset allocation to obtain investment returns and moderate risks. It is essential to understand the interaction between clean energy stock and other financial assets. The empirical literature is increasingly analyzing the interaction between clean energy stock and other assets [1,2,3]. Reboredo and Ugolini [4]

Objectives

Methods

Results

Conclusion