Reexamining the time-varying volatility spillover effects: A Markov switching causality approach

This paper intends to examine the volatility spillover effect between selective developed markets including U.S., U.K., Germany, Japan and Hong Kong over the sample period from 1996 to 2011. We introduce a Markov switching causality method to model the potential instability of volatility spillover relationships over market tranquil or turmoil periods. This method is more flexible as no prior information on the changing points or size of sample window is needed. From the empirical results, we find the evidence of the existence of spillover effects among most markets, and the bilateral volatility spillover effects are more prominent over turmoil or crisis episodes, especially during Asia crisis and subprime mortgage crisis periods. Moreover, the distinct role of each market is also investigated.     

Forecasting volatility with time-varying leverage and volatility of volatility effects

Predicting volatility is of primary importance for business applications in risk management, asset allocation, and the pricing of derivative instruments. This paper proposes a measurement model that considers the possibly time-varying interaction of realized volatility and asset returns according to a bivariate model to capture its major characteristics: (i) the long-term memory of the volatility process, (ii) the heavy-tailedness of the distribution of returns, and (iii) the negative dependence of volatility and daily market returns. We assess the relevance of the effects of “the volatility of volatility” and time-varying “leverage” to the out-of-sample forecasting performance of the model, and evaluate the density of forecasts of market volatility. Empirical results show that our specification can outperform the benchmark HAR–GARCH model in terms of both point and density forecasts.     

Centres and Peripheries in Finland: Granger Causality Tests Using Panel Data

Abstract

Despite their importance from a policy point of view, empirical studies on the effects of growth centres in their regions are rare. This paper analyses mutual relationships between growth processes in centres and their surrounding hinterlands in 19 Finnish regions. Annual population data from the period 1970–2004 are used. A novel testing procedure based on an extension of the Granger causality definition in a panel data context is applied. Heterogeneity between regions is allowed. Both the homogeneous non-causality hypothesis and the homogeneous causality hypothesis are rejected. Causal processes prove to be heterogeneous. Causality from centres to peripheries is found for nine regions and causality from peripheries to centres for twelve regions. Rapidly growing and large centres, in particular, have negative effects on their hinterlands.

Centres et banlieues en Finlande: tests de causalité de Granger faisant usage de données de panel

RÉSUMÉ?En dépit de leur importance sur le plan de la politique, les études empiriques sur les effets des centres d'expansion dans leurs régions sont rares. La présente communication analyse, dans dix-neuf régions de la Finlande, les rapports réciproques entre procédés d'expansion dans les centres et leur arrière-pays environnant. Pour ceci, on utilise des données sur la population annuelle remontant à la période 1970–2004. On applique une nouvelle technique de test basée sur une extension de la définition de Granger dans le contexte de données de panel. L'hétérogénéité entre les régions est admise. Tant l'hypothèse de la non causalité homogène que celle de la causalité homogène sont rejetées. Les techniques causales s'avèrent être hétérogènes. On relève une causalité des centres aux banlieues dans neuf régions, et des banlieues aux centres ville dans douze régions. Notamment, les centres ville de grande taille et en pleine expansion ont un effet négatif sur leur arrière-pays.

Centros y periferias en Finlandia: Ensayos de causalidad de Granger utilizando datos de panel

RÉSUMÉN?A pesar de su importancia desde un punto de vista de políticas, los estudios empíricos sobre los efectos de los centros de crecimiento en sus regiones son escasos. Este trabajo analiza las relaciones mutuas entre los procesos de crecimiento en los centros y sus interiores vecinos, en diecinueve regiones finlandesas. Se utilizan datos anuales de población entre el período de 1970–2004. Se aplica un nuevo procedimiento de ensayo basado en una extensión de la definición de causalidad de Granger dentro de un contexto de datos de panel. Se tiene en cuenta la heterogeneidad entre regiones. Se rechazan tanto la hipótesis de no causalidad homogénea como la hipótesis de causalidad homogénea. Los procesos causales demuestran ser heterogéneos. Causalidad de centros a periferias se encuentra en nueve regiones, y causalidad de periferias a centros en doce regiones. Los centros grandes y de rápido crecimiento, en particular, tienen efectos negativos sobre sus interiores.

     

Realistic Statistical Modelling of Financial Data

The aim of this paper is to present some of the stylized features of financial data which have received a lot of attention both from practitioners and those with more theoretical backgrounds. Some of the models resulting from these efforts are reviewed and discussed. To facilitate the discussion two data sets are used: one of these contains all US trades in IBM stocks in 1995 at NYSE.