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The goal of the presented work consists in the construction of the new three-levels scheme of authomatical classification. This scheme is based on the newly introduced notion of volatility of separate clusters as well as of whole classification. The property is exactly defined and efficiently calculated. It describes the stability, exactness, validity of subsets of the given initial set in essence, their possibility (or impossibility) to be selected as clusters. The suggested algorithm finds the clusters with arbitrary levels of volatility, including the conventional case of zero volatility. The clusters in USA, Russia and Sweden stock market (for crisis period of 2008-2010) and deputies clusters based on voting results in the 3rd State Duma between September 2001 and January 2002 (the period including the creation of the party "United Russia" 01.12.2001) were constructed by the suggested algorithm. Analyzing clusters constructed basing on the voting results for every of the considered months, it has turned out that the clustering volatility was equal to zero in September and October, drastically increased in November and slightly decreased in December and January. But several indices (i.e. concordance of parties' positions) did not show sensible jumps near this political "bifurcation point". The other considered various model examples demonstrated the results well-coordinated with geometrical intuition.

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Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1.

Purpose. There are sufficient evidences in the literature that welfare of food producers and consumers is easily compromised due to unfavorable food price volatility dynamics. Therefore, this study investigates the volatility dynamics in food price index returns (FPIRETURNS), imported food price index returns (CIFCPIRETURNS), price of dollars at bureau de change (BDCRETURNS) and inter-bank rate (EXRETURNS). Design/Methodology/Approach. In view of the increasing quest to account for volatility behavior such as non-linear and time-varying risk premium in food price series using an appropriate tool, this paper adopts exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. This is because it allows error terms to be conditional heteroscedastic, and the dynamics process generating the underlying heteroscedasticity to be asymmetric. That is, the model introduces a parameter that can reveal how conditional variance respond to both positive and negative shocks of equal magnitude (asymmetric effect). Findings and Implications. The study finds leverage effect and high persistence in some of the selected models. Also, exchange rate volatility affects volatility of FPIRETURNS, but it is more pronounced on the volatility of CIFCPIRETURNS. Limitations. Inadequate data especially for CIFCPIRETURNS is a huge limitation in this study. Originality. However, this study has sufficient empirical evidences that instability in forex market flows into the Nigerian food market with pronounced leverage effect and persistence in food price volatility. The recommendation is, government should implement stabilization policy in the forex market as a precursor to ensuring stability in domestic food market.

AbstractThis paper explores the possibility that financial depth may have an asymmetric impact on macroeconomic volatility by affecting its "good" and "bad" components in different ways. While "good" volatility refers to positive shocks to gross domestic product, consumption and investment growth, "bad" volatility denotes negative fluctuations in these macroeconomic indicators. Dynamic panel regressions in a sample of 97 countries over the period 1960–2010 provide evidence of asymmetry on three main grounds. First, financial depth reduces good volatility but does not have much impact on bad volatility except that it reduces some bad volatility of consumption. Second, though financial depth reduces both good and bad volatility of consumption, the reduction in the good component is much greater. Third, the impact of financial depth on macroeconomic volatility varies across sectors. Particularly in low‐income economies, financial depth enables better consumption decisions but poorer investment choices. These results have important policy implications.

PurposeTo investigate whether monetary volatility in the US exerts any asymmetric impact on output volatility over the period 1974‐2002.Design/methodology/approachFor the empirical purposes, the analysis makes use of the multi‐variable GARCH (MVGARCH), which allows not only the presence of volatility clustering but also the presence of asymmetries in that volatility clustering.FindingsThe empirical findings suggest that money supply volatility exerts a significant asymmetric influence on output volatility, i.e. the variance of output changes more due to positive changes than negative changes of money supply volatility.Originality/valueThe paper investigates, for the first time, the presence of any asymmetric impact of the volatility of money on the volatility of output in the case of the US.