scholarly journals Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Syed Jawad Hussain Shahzad ◽  
Elie Bouri ◽  
Ladislav Kristoufek ◽  
Tareq Saeed
Keyword(s):  
Stock Market ◽  
Network Structure ◽  
Forecast Error ◽  
Common Factor ◽  
Error Variance ◽  
Stock Market Index ◽  
Vector Autoregressive ◽  
The Us ◽  
Market State ◽  
Forecast Error Variance

AbstractThe aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

scholarly journals Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

2009 ◽  
Vol 41 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Andrew M. McKenzie ◽  
Harold L. Goodwin ◽  
Rita I. Carreira
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Autoregressive Models ◽  
Model Specification ◽  
Decomposition Approach ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Variance Decompositions

Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.

scholarly journals Asymmetric and nonlinear inter-relations of US stock indices

2018 ◽  
Vol 14 (1) ◽  
pp. 78-129 ◽  
Author(s):  
Dimitrios Vortelinos ◽  
Konstantinos Gkillas (Gillas) ◽  
Costas Syriopoulos ◽  
Argyro Svingou
Keyword(s):  
Debt Crisis ◽  
Forecast Error ◽  
Error Variance ◽  
Asset Markets ◽  
Correction Model ◽  
Content Type ◽  
Stock Indices ◽  
The Us ◽  
Forecast Error Variance ◽  
Causality Model

Purpose The purpose of this paper is to examine the inter-relations among the US stock indices. Design/methodology/approach Data of nine US stock indices spanning a period of sixteen years (2000-2015) are employed for this purpose. Asymmetries are examined via an error correction model. Non-linear inter-relations are researched via Breitung’s nonlinear cointegration, a M-G nonlinear causality model, shocks to the forecast error variance, a shock spillover index and an asymmetric VAR-GARCH (VAR-ABEKK) approach. Findings The inter-relations are significant. The results are robust across all types of inter-relations. They are highest in the Lehman Brothers sub-period. Higher stability after the EU debt crisis, enhances independence and growth for the US stock indices. Originality/value To the best of the knowledge, this is the first study to examine the inter-relations of US stock indices. Most studies on inter-relations concentrate on the portfolio analysis to reveal diversification benefits among various asset markets internationally. Hence this study contributes to this literature on the inter-relations of a specific asset market (stock), and in a specific nation (USA). The evident inter-relations support the notion of diversification benefits in the US stock markets.

scholarly journals Intraday Volatility Spillovers among European Financial Markets during COVID-19

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Faheem Aslam ◽  
Paulo Ferreira ◽  
Khurrum Shahzad Mughal ◽  
Beenish Bashir
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Forecast Error ◽  
Error Variance ◽  
Stock Market Volatility ◽  
Volatility Spillovers ◽  
Forecast Error Variance ◽  
Intraday Volatility ◽  
Market Interdependence ◽  
Rolling Windows

During crises, stock market volatility generally rises sharply, and as consequence, spillovers are identified across markets. This study estimates the volatility spillover among twelve European stock markets representing all four regions of Europe. The data consists of 10,990 intraday observations from 2 December 2019 to 29 May 2020. Using the methodology of Diebold and Yilmaz, we use static and rolling windows to characterize five-minute volatility spillovers. Our results show that 77.80% of intraday volatility forecast error variance in twelve European markets comes from spillovers. Furthermore, the highest gross directional volatility spillovers are found in Sweden and the Netherlands, while the minimum spillovers to other stock markets are observed in the stock markets of Poland and Ireland. However, German and Dutch markets transmit the highest net directional volatility spillovers. Splitting the whole sample in pre- and post-pandemic declaration (11 March 2020) we find more stable spillovers in the latter. The findings reveal important information about European stock market interdependence during COVID-19, which will be beneficial to both policy-makers and practitioners.

scholarly journals Tfp, News, and “Sentiments”: the International Transmission of Business Cycles

2018 ◽  
Vol 18 (1) ◽  
pp. 302-341 ◽  
Author(s):  
Andrei A Levchenko ◽  
Nitya Pandalai-Nayar
Keyword(s):  
United States ◽  
Business Cycle ◽  
Forecast Error ◽  
Error Variance ◽  
The United States ◽  
International Transmission ◽  
The Us ◽  
Forecast Error Variance ◽  
Short Run ◽  
Positive Shock

Abstract We propose a novel identification scheme for a nontechnology business cycle shock, which we label “sentiment”. This is a shock orthogonal to identified surprise and news TFP shocks that maximize the short-run forecast error variance of an expectational variable, alternatively a GDP forecast or a consumer confidence index. We then estimate the international transmission of three identified shocks—surprise TFP, news of future TFP, and sentiment—from the United States to Canada. The US sentiment shock produces a business cycle in the United States, with output, hours, and consumption rising following a positive shock, and accounts for the bulk of the US short-run business cycle fluctuations. The sentiment shock also has a significant impact on Canadian macroaggregates. In the short run, it is more important than either the surprise or the news TFP shocks in generating business cycle comovement between the United States and Canada, accounting for over 40% of the forecast error variance of Canadian GDP and over one-third of Canadian hours, imports, and exports. The news shock is responsible for some comovement at 5–10 years, and surprise TFP innovations do not generate synchronization. We provide a simple theoretical framework to illustrate how the US sentiment shocks can transmit to Canada.

The time-varying and volatile macroeconomic effects of immigration

2021 ◽  
pp. 1-21
Author(s):  
Huachen Li
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Time Varying ◽  
Vector Autoregressions ◽  
Hours Worked ◽  
Supply Shock ◽  
The Us ◽  
Forecast Error Variance ◽  
The Business Cycle ◽  
The Impact

Abstract This paper studies the impact of immigration on the US macroeconomy. I identify structural vector autoregressions (SVARs) with time-varying parameters (TVPs) and stochastic volatility (SV) using a novel set of restrictions. The TVP-SV-SVARs are estimated on a quarterly sample including average labor productivity (ALP), hours worked, immigration, consumption, and term spread from 1953 to 2017. An immigration supply shock increases domestic ALP and hours worked over the business cycle horizons. Movements in immigration are explained by its own shock and to a lesser extent by the productivity and news shocks. IRFs driven by these shocks vary over the sample, especially around changes in immigration policy such as the Immigration Act of 1990. In contrast, the forecast error variance decompositions exhibit little change over the sample. Immigration plays an important role in the US macroeconomy.

Estimating Orthogonal Impulse Responses via Vector Autoregressive Models

1991 ◽  
Vol 7 (4) ◽  
pp. 487-496 ◽  
Author(s):  
Helmut Lütkepohl ◽  
D.S. Poskitt
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Error Variance ◽  
Asymptotic Distributions ◽  
Impulse Response Functions ◽  
Impulse Responses ◽  
Vector Autoregressive ◽  
Forecast Error Variance ◽  
Variance Decompositions ◽  
The Relationship

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is derived under the assumption that finite order vector autoregressive (VAR) models are fitted to time series generated by possibly infinite order processes. The resulting asymptotic distributions of forecast error variance decompositions are also given.

scholarly journals Assessing the progress of exports diversification in Saudi Arabia: growth-share matrix approach

2020 ◽  
Vol 18 (3) ◽  
pp. 118-128
Author(s):  
Mohammad Imdadul Haque
Keyword(s):  
Growth Rate ◽  
Saudi Arabia ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Vector Autoregressive ◽  
Long Run ◽  
Market Shares ◽  
Forecast Error Variance ◽  
Revenue Volatility

High dependence on a particular category of exports results in fluctuations in income as the price of the export item fluctuates. In Saudi Arabia, a single category of mineral exports forms over 78% of the total exports, exposing the country to revenue volatility. The study aims to assess the magnitude of diversification of the export basket for the country. It uses data from 1984 to 2018 to study the importance of non-mineral exports in total exports. It applies Granger causality, variance decomposition, and impulse response function in the vector autoregressive framework. The study also uses the growth-share matrix to evaluate individual items of non-mineral exports. The results show a long-run relationship with a 1% increase in non-mineral exports, leading to a 0.30% increase in total exports. Non-mineral exports Granger-cause total exports. In the long run, non-mineral exports have a share of 64% of the forecast error variance in total exports. Moreover, a 1% shock in non-mineral exports creates a huge initial impact on total exports. Also, the growth rate of non-mineral products is higher than mineral products. The results indicate the importance of non-mineral exports for a predominantly oil-exporting country. Finally, the study attempts to classify its non-mineral export categories based on growth rates and market shares. Targeted emphasis on export category with a strong growth rate and low market share can be an effective strategy for further export diversification.

scholarly journals The determinants of electricity consumption for ASEAN countries

2017 ◽  
Vol 13 (4-1) ◽  
pp. 331-339
Author(s):  
Mohd Tahir Ismail ◽  
Nadhilah Mahmud ◽  
Rosmanjawati Abdul Rahman
Keyword(s):  
Forecast Error ◽  
Carbon Dioxide Emission ◽  
Electricity Consumption ◽  
Error Variance ◽  
Sample Period ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Vec Model ◽  
Forecast Error Variance ◽  
Asean Countries

The present study investigates the causal relationship between ASEAN seven member countries electricity consumption (EC) and some determinants such as gross domestic product (GDP), exports (EXP) and carbon dioxide emission (CO2) using vector autoregressive (VAR) framework via vector error correction (VEC) model for the period from 1980-2015. The findings show that the effect of the chosen determinants is different among the seven countries. Within the sample period, by utilizing Granger causality test, out of the seven countries, only four revealed either unidirectional or bidirectional causality running from EC to the three determinants, GDP, EXP and CO2. Whereas, thru forecast error variance decomposition (FEVD), forecasting beyond the sample period uncovered a shock to EC will also spread to GDP, EXP and CO2. The present study suggests that ASEAN should take note in designing their electricity policy, since electricity affect and be affected by other factors. In addition, ASEAN also should find solutions on how to control CO2 emission through EC.

THE ROLE OF US-BASED FDI FLOWS FOR GLOBAL OUTPUT DYNAMICS

2017 ◽  
Vol 23 (3) ◽  
pp. 943-973 ◽  
Author(s):  
Florian Huber ◽  
Manfred M. Fischer ◽  
Philipp Piribauer
Keyword(s):  
Foreign Direct Investment ◽  
Latin American ◽  
Direct Investment ◽  
Forecast Error ◽  
Emerging Market ◽  
Error Variance ◽  
Vector Autoregressive ◽  
Long Term Effect ◽  
Forecast Error Variance ◽  
Global Vector

This paper uses a global vector autoregressive (GVAR) model to analyze the relationship between foreign direct investment (FDI) inflows and output dynamics in a multicountry context. The GVAR model enables us to make two important contributions: First, to model international linkages among a large number of countries, which is a key asset given the diversity of countries involved, and second, to model foreign direct investment and output dynamics jointly. The country-specific small-dimensional vector autoregressive submodels are estimated utilizing a Bayesian version of the model coupled with stochastic search variable selection priors to account for model uncertainty. Using a sample of 15 emerging and advanced economies over the period 1998:Q1–2012:Q4, we find that US outbound FDI exerts a positive long-term effect on output. Asian and Latin American economies tend to react faster and also stronger than Western European countries. Forecast error variance decompositions indicate that FDI plays a prominent role in explaining gross domestic product fluctuations, especially in emerging market economies.

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