Time varying price discovery

Avino, Davide; Lazar, Emese; Varotto, Simone

doi:10.1016/j.econlet.2014.09.030

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…Other studies have tested price discovery using time-varying and clustering volatility through generalized autoregressive conditional heteroskedasticity (GARCH) models. Research by Avino et al [38] studied time varying price discovery to estimate the information share (IS) using daily data of bond yields, CDS mid-quotes, equity prices, and option-implied volatilities for Marks and Spencer Plc. These authors used BEKK-GARCH specification for the VECM to estimate the daily time varying IS.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach

Ahmed

2021

Mathematics

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This paper examines the world wheat market leadership using price discovery occurring in wheat futures markets of the United States (U.S.) and Europe. An error correction model (ECM) generalized autoregressive conditional heteroskedasticity (GARCH), and semi-parametric dynamic copula methods are used for this purpose. The results indicate a positive link between U.S. and Europe price discovery which is stronger, fluctuating less after August 2010 because of a drought occurring in the Black Sea region, and then lessens, fluctuating more after 2015 with the changing wheat trade map. Furthermore, after 2015, wheat market leadership moved from the U.S. to the European market, meaning price discovery is primarily located by the Marché à Terme International de France (MATIF) futures market.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach

Ahmed

2021

Mathematics

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“…In summary, the empirical evidence reviewed above suggests that carefully modeling time variation in market information shares can provide a richer understanding of price discovery process and market dynamics. Nevertheless, in all the studies the cause of the time variation is different underlying distribution due to the unexplained GARCH effects (e.g., Avino, Lazar, & Varotto, 2015) or relegated to other exogenous factors.…”

Section: Evidence Of Time-varying Information Sharementioning

confidence: 99%

Quantile information share

Lien

Wang

2018

Journal of Futures Markets

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This paper presents a new method to estimate Hasbrouck‐type market information share in price discovery. The prevailing market information share is calculated on the basis of conditional mean. We propose a conditional quantile regression approach to obtain a new market information share measure, quantile information share, which varies across the combinations of different price quantiles. The method is illustrated with two data sets, one on the spot and futures markets in pricing S&P 500 equity index, and the other on price discovery for a cross‐listed stock.

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“…Therefore, future studies may include other emerging markets (such as BRICS) and conduct further analysis to compare and understand the phenomenon of price discovery. Third, this paper has not included the time-variation in price discovery analysis (Avino et al, 2015), which could be a further dimension for research. Fourth, this paper has only identified the direction of price discovery, but not determinants of price discovery.…”

Section: Discussionmentioning

confidence: 99%

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Kumar

Inani

2017

IJFMD

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Using neural network approach, this study revisits the price discovery relationship between spot and futures prices of S&P CNX Nifty Index of India. This study uses minute-by-minute data of 167 trading days (from January 2015 to August 2015). Empirical results reveal that there exists a bi-directional lead-lag relationship between spot and futures markets, but the causality from the spot market to the futures market is more dominant as compared to that of running from the futures market to the spot market. Root mean squared error of the results indicates that the incorporation of spot returns in modelling futures returns improves the results by 62.37%. Whereas, the inclusion of futures returns in modelling spot returns improves the model by only 44.60%. Price discovery dynamics has significant implications for market participants, regulators, and exchanges. These findings could be helpful for portfolio management, market structure design, and the implementation of arbitrage and hedging strategies.

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Time varying price discovery

Cited by 19 publications

References 20 publications

Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach

Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach

Quantile information share

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

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