Search results for "forecast error variance decomposition"

showing 3 items of 3 documents

Asymmetric semi-volatility spillover effects in EMU stock markets

2018

Abstract The aim of this paper is to quantify the strength and the direction of semi-volatility spillovers between five EMU stock markets over the 2000–2016 period. We use upside and downside semi-volatilities as proxies for downside risk and upside opportunities. In this way, we aim to complement the literature, which has focused mainly on the contemporaneous correlation between positive and negative returns, with the evidence of asymmetry also in semi-volatility transmission. For this purpose, we apply the Diebold and Yilmaz (2012) methodology, based on a generalized forecast error variance decomposition, to downside and upside realized semi-volatility series. While the analysis of Diebol…

Normalization (statistics)Multivariate statisticsEconomics and Econometrics050208 financeForecast error variance decomposition05 social sciencessemi-volatility asymmetry forecast error variance decompositionVolatility spilloverDownside riskSemi-volatilitySettore SECS-P/05 - EconometriaAsymmetryFull sampleSpilloverSpillover effect0502 economics and businessVHAREconometricsVariance decomposition of forecast errorsEconomicsSemi-volatility Asymmetry Forecast error variance decomposition Spillover VHAR050207 economicsStock (geology)FinanceInternational Review of Financial Analysis
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How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

2019

Abstract This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is pref…

Normalization (statistics)Economics and EconometricsSocial connectedness020209 energySettore SECS-P/05 - Econometria02 engineering and technologyNormalization schemeconnectednessSpillover effect0502 economics and business0202 electrical engineering electronic engineering information engineeringEconometrics050207 economicsMathematicsspillover normalization connectednessVector autoregression models05 social sciencesFinancial marketCovarianceCausalitySpilloverGeneral EnergynormalizationGeneralized forecast error variance decompositionCommodity price fluctuations Driving forces Nonparametric additive regression modelsVariance decomposition of forecast errorsBond marketStock marketSimulationNormalization schemes
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A note on normalization schemes: The case of generalized forecast error variance decompositions

2016

The aim of this paper is to propose new normalization schemes for the values obtained from the generalized forecast error variance decomposition, in order to obtain more reliable net spillover measures. We provide a review of various matrix normalization schemes used in different application domains. The intention is to contribute to the financial econometrics literature aimed at building a bridge between different approaches able to detect spillover effects, such as spatial regressions and network analyses. Considering DGPs characterized by different degrees of correlation and persistence, we show that the popular row normalization scheme proposed by Diebold and Yilmaz (2012), as well as t…

normalization schemes forecast error variance decomposition spillover networks spatial econometrics VARspatial econometricsspillovernetworksSettore SECS-P/05 - Econometrianormalization schemes forecast error variance decomposition spillover networksforecast error variance decompositionVARnormalization schemes
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