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

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3442
Author(s):  
Tiantian Liu ◽  
Shigeyuki Hamori
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Investor Sentiment ◽  
Clean Energy ◽  
Var Model ◽  
Time Varying ◽  
Parameter Vector ◽  
Vector Autoregressive ◽  
Time Varying Parameter ◽  
Rolling Window

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.

scholarly journals Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions

2020 ◽  
Vol 13 (4) ◽  
pp. 84 ◽  
Author(s):  
Nikolaos Antonakakis ◽  
Ioannis Chatziantoniou ◽  
David Gabauer
Keyword(s):  
Covariance Structure ◽  
Window Size ◽  
Underlying Structure ◽  
Time Varying ◽  
Parameter Vector ◽  
Vector Autoregressions ◽  
Vector Autoregressive ◽  
Time Varying Parameter ◽  
Rolling Window ◽  
Dynamic Measures

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

scholarly journals A time-varying approach to analysing fiscal policy and asset prices in South Africa

2014 ◽  
Vol 6 (1) ◽  
pp. 46-63 ◽  
Author(s):  
Rangan Gupta ◽  
Charl Jooste ◽  
Kanyane Matlou
Keyword(s):  
Fiscal Policy ◽  
Stock Prices ◽  
Asset Prices ◽  
Marked Change ◽  
Var Model ◽  
Time Varying ◽  
Content Type ◽  
Vector Autoregressive ◽  
Time Varying Parameter ◽  
The Impact

Purpose – This paper aims to study the interplay of fiscal policy and asset prices in a time-varying fashion. Design/methodology/approach – Using South African data since 1966, the authors are able to study the dynamic shocks of both fiscal policy and asset prices on asset prices and fiscal policy based on a time-varying parameter vector autoregressive (TVP-VAR) model. This enables the authors to isolate specific periods in time to understand the size and sign of the shocks. Findings – The results seem to suggest that at least two regimes exist in which expansionary fiscal policy affected asset prices. From the 1970s until 1990, fiscal expansions were associated with declining house and slightly increased stock prices. The majority of the first decade of 2000 had asset prices increasing when fiscal policy expanded. On the other hand, increasing asset prices reduced deficits for the majority of the sample period, while the recent financial crises had a marked change on the way asset prices affect fiscal policy. Originality/value – This is the first attempt in the literature of fiscal policy and asset prices to use a TVP-VAR model to not only analyse the impact of fiscal policy on asset prices, but also the feedback from asset prices to fiscal policy over time.

scholarly journals The Time-Varying Effects of Monetary Policy on House Prices in China: An Application of TVP-VAR Model with Stochastic Volatility

2018 ◽  
Vol 13 (4) ◽  
pp. 149 ◽  
Author(s):  
Weina Cai ◽  
Sen Wang
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
House Prices ◽  
Var Model ◽  
Time Varying ◽  
Credit Channel ◽  
Monetary Policy Shocks ◽  
Time Varying Parameter ◽  
Policy Shocks ◽  
Interest Rate Channel

The boom of housing market in China in recent years has attracted great concerns from all over the world. How monetary policy affects house prices in China becomes an essential topic. This paper studies the time-varying effects of monetary policy on house prices in China during 2005.7-2017.10, by using a time-varying parameter VAR model. This paper obtains three interesting results. First, there are time-varying features of the responses of house prices to monetary policy shocks half-year and 1-year ahead, no matter through interest rate channel or through credit channel. Second, interest rate channel and credit channel have been enhanced since financial crisis in 2008. Third, the responses of nominal house prices to monetary policy in China are mainly driven by the responses of real house prices, instead of inflation. Finally, this paper gives proper suggestions for each finding respectively to central bank in China.

Can energy commodities affect energy blockchain-based cryptos?

2019 ◽  
Vol 36 (4) ◽  
pp. 682-699 ◽  
Author(s):  
Ikhlaas Gurrib
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Autoregressive Model ◽  
Structural Breaks ◽  
Price Index ◽  
Principal Component ◽  
Commodity Price ◽  
Var Model ◽  
Content Type ◽  
Heating Oil

Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

scholarly journals Transmission mechanisms in Japan’s quantitative easing policy (2001–2006)

2017 ◽  
Vol 6 (2) ◽  
pp. 35 ◽  
Author(s):  
Hiroyuki Ijiri
Keyword(s):  
Bank Lending ◽  
Var Model ◽  
Quantitative Easing ◽  
Time Varying ◽  
Transmission Channel ◽  
Bank Lending Channel ◽  
Monthly Data ◽  
Lending Channel ◽  
Transmission Mechanisms ◽  
Time Varying Parameter

This study investigates exchange rates and bank lending as the transmission channels for Japan’s Quantitative Easing Policy (QEP) during 2001–2006. Using a Time Varying Parameter-VAR model and monthly data to analyze the dynamism of the QEP, this study is the first to show that the exchange rate channel was the effective QEP transmission channel after around 2005, while the bank lending channel was inactive.

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