Matteo Bonato

Bio: 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.

Does Geopolitical Risks Predict Stock Returns and Volatility of Leading Defense Companies? Evidence from a Nonparametric Approach

Abstract: We use 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...

Forecasting Realized (Co)Variances with a Block Structure Wishart Autoregressive Model

Abstract: The increased availability of high-frequency data provides new tools for forecasting of variances and covariances between assets However, recent realized (co)variance models may suffer from a 'curse of dimensionality' problem similar to that of multivariate GARCH specifications As a result, they need strong parameter restrictions, in order to avoid non-interpretability of model coefficients, as in the matrix and log exponential representations Among the proposed models, the Wishart autoregressive model introduced by Gourieroux et al (2005) analyzes the realized covariance matrices without any restriction on the parameters while maintaining coefficient interpretability Indeed, the model, under mild stationarity conditions, provides positive definite forecasts for the realized covariance matrices Unfortunately, it is still not feasible for large asset cross-section dimensions In this paper we propose a restricted parametrization of the Wishart Autoregressive model which is feasible even with a large cross-section of assets In particular, we assume that the asset variances-covariances have no or limited spillover and that their dynamic is sector-specific In addition, we propose a Wishart-based generalization of the HAR model of Corsi (2004) We present an empirical application based on variance forecasting and risk evaluation of a portfolio of two US treasury bills and two exchange rates We compare our restricted specifications with the traditional WAR parameterizations Our results show that the restrictions may be supported by the data and that the risk evaluations of the models are extremely close This confirms that our model can be safely used in a large cross-sectional dimension given that it provides results similar to fully parametrized specifications

Skewness and Kurtosis in Real Data Samples

Abstract: Parametric statistics are based on the assumption of normality. Recent findings suggest that Type I error and power can be adversely affected when data are non-normal. This paper aims to assess the distributional shape of real data by examining the values of the third and fourth central moments as a measurement of skewness and kurtosis in small samples. The analysis concerned 693 distributions with a sample size ranging from 10 to 30. Measures of cognitive ability and of other psychological variables were included. The results showed that skewness ranged between −2.49 and 2.33. The values of kurtosis ranged between −1.92 and 7.41. Considering skewness and kurtosis together the results indicated that only 5.5% of distributions were close to expected values under normality. Although extreme contamination does not seem to be very frequent, the findings are consistent with previous research suggesting that normality is not the rule with real data.

Modelling and Forecasting Multivariate Realized Volatility

Abstract: This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.

Modelling and forecasting multivariate realized volatility

Abstract: SUMMARY This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical application of the model, which shows that it outperforms other approaches in the extant literature, both in terms of statistical precision as well as in terms of providing a superior mean-variance trade-off in a classical investment decision setting. Copyright © 2010 John Wiley & Sons, Ltd.