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Matteo Bonato

Researcher at University of Johannesburg

Publications -  34
Citations -  663

Matteo Bonato is an academic researcher from University of Johannesburg. The author has contributed to research in topics: Realized variance & Volatility (finance). The author has an hindex of 12, co-authored 32 publications receiving 424 citations. Previous affiliations of Matteo Bonato include UBS & Credit Suisse.

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Geopolitical risks and stock market dynamics of the BRICS

TL;DR: In this paper, the effect of geopolitical uncertainty on return and volatility dynamics in the BRICS stock markets via nonparametric causality-in-quantiles tests was examined, finding that news regarding geopolitical tensions do not affect return dynamics in these markets in a uniform way.
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Does Geopolitical Risks Predict Stock Returns and Volatility of Leading Defense Companies? Evidence from a Nonparametric Approach

TL;DR: The authors used the k-th-order nonparametric causality test at monthly frequency over the period of 1985:1 to 2016:06 to analyze whether geopolitical risks can predict movements in stock returns and volatil...
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The effect of investor sentiment on gold market return dynamics: Evidence from a nonparametric causality-in-quantiles approach

TL;DR: In this paper, the effect of investor sentiment on the intraday return dynamics in the gold market was explored and it was found that the effect was more prevalent on intradays volatility in the Gold market, rather than daily returns.
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Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?

TL;DR: In this paper, Hansen et al. used US-traded futures price data at a 1-min frequency over the 2002-2017 period to study the changes in the dynamics of price correlations and spillover effects in the commodity market and showed that while the diversification benefits of investing in this market have decreased, volatility transmission risk and hedging costs have increased.
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Forecasting Realized (Co)Variances with a Block Structure Wishart Autoregressive Model

TL;DR: In this article, a restricted parametrization of the Wishart autoregressive model is proposed for large asset cross-section dimensions, which can be safely used in a large cross-sectional dimension given that it provides results similar to fully parametrized specifications.