Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

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.

http://diposit.ub.edu/dspace/bitstream/2445/153945/1/603461.pdf

Skewness and Kurtosis in Real Data Samples

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

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

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.

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Modelling and forecasting multivariate realized volatility

This paper examines the changes in price and return dynamics that affected the commodity market around the 2007-2008 boom&bust. Relying on data at intra-day frequency and adapting the recently proposed realized Beta GARCH model of Hansen et. al (J. Appl. Econ.(2014)), it is shown that starting from 2006 realized correlations between agricultural commodities belonging to the same group and between agriculturals and oil have significantly increased. The rise in correlations between grains - or soft commodities - and oil is especially notable. The dynamics of the volatility spillover across commodities are also investigated. It is found that spillover effects became more evident prior to the commodity price crash. However, this increase in volatility transmission tented to anticipate the increase in correlations. To conclude, it is shown that the optimal ratio of a short position on oil required to hedge a 1$ long position in an agricultural commodity, which corresponds to the the model realized beta, also increased significantly after 2006. This finding has important implication in terms of portfolio hedging and risk management.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2670084_code603588.pdf?abstractid=2571217&mirid=2

Realized Correlations, Betas and Volatility Spillover in the Commodity Market: What Has Changed?

We examine the forecasting power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for Real Estate Investment Trusts (REITs) realized market variance of the United States (US) via the heterogeneous autoregressive realized volatility (HAR-RV) model. Our results show that the EMVID index improves the forecast accuracy of realized variance of REITs at short-, medium-, and long-run horizons in a statistically significant manner, with the result being robust to the inclusion of additional controls (leverage, realized jumps, skewness, and kurtosis) capturing extreme market movements, and also carries over to ten sub-sectors of the US REITs market. Our results have important portfolio implications for investors during the current period of unprecedented levels of uncertainty resulting from the outbreak of COVID-19.

Uncertainty due to Infectious Diseases and Forecastability of the Realized Variance of US REITs: A Note

In this paper a new multivariate volatility model is proposed. It combines the appealing properties of the stable Paretian distribution to model the heavy tails with the GARCH model to capture the volatility clustering. We assume that multivariate asset-returns of financial stocks follow a sub-Gaussian distribution, which is a particular multivariate stable distribution. In this way the characteristic function of the fitted returns has a tractable expression and the density function can be recovered by numerical methods. A multivariate GARCH structure is then adopted to model the covariance matrix of the Gaussian vectors underlying the sub-Gaussian system. The model is applied to a bivariate series of daily U.S. stock returns. Value-at-Risk for long and short positions is computed and compared with the one obtained using the multivariate normal and the multivariate Student's t distribution. Finally, exploiting the recent developments in the vast dimensional time-varying covariances modeling, possible feasible extensions to higher dimensions are suggested and an illustrative example using the Dow Jones index components is presented.

Modeling Fat Tails in Stock Returns: A Multivariate Stable-GARCH Approach

Commodity markets have experienced a surge in participation by financial investors over the past decade. This notable uptick highlighted the potential effect of financialization as one of the causes of increased comovement of commodities returns. Building on the theory of comovement proposed by Barberis et al. (2005), this paper investigates drivers of the observed increase in comovement prices of commodity futures in the United States and provides new empirical evidences. This empirical analysis allows us to distinguish between two views of return comovement: the traditional view, in which return comovement is explained by comovements in news about fundamentals value, and the alternative view, in which excess comovement is attributed to traders’ sentiment. We examine differences in the dependence structure between index and off-index commodities, and three major commodities indexes. Non-energy commodities present in the index exhibit an increase in comovement whereas those commodities off the index do not. These results are supported by the analysis of high-frequency returns dynamics by means of the so called realised beta. We therefore provide new evidence supporting the friction or sentiment based view of commodity comovement. We interpret this to be additional evidence of financialization as the driver of the observed increase in commodity return comovement.

Matteo Bonato

Papers

Realized Correlations, Betas and Volatility Spillover in the Commodity Market: What Has Changed?

Uncertainty due to Infectious Diseases and Forecastability of the Realized Variance of US REITs: A Note

Modeling Fat Tails in Stock Returns: A Multivariate Stable-GARCH Approach

Comovement and the Financialization of Commodities