Dimitrios I. Vortelinos

Asymmetric and nonlinear inter-relations of US stock indices

Purpose - The purpose of this paper is to examine the inter-relations among the US stock indices. Design/methodology/approach - Data of nine US stock indices spanning a period of sixteen years (2000-2015) are employed for this purpose. Asymmetries are examined via an error correction model. Non-linear inter-relations are researched via Breitung’s nonlinear cointegration, a M-G nonlinear causality model, shocks to the forecast error variance, a shock spillover index and an asymmetric VAR-GARCH (VAR-ABEKK) approach. Findings - The inter-relations are signi?cant. The results are robust across all types of inter-relations. They are highest in the Lehman Brothers sub-period. Higher stability after the EU debt crisis, enhances independence and growth for the US stock indices. Originality/value - To the best of the knowledge, this is the first study to examine the inter-relations of US stock indices. Most studies on inter-relations concentrate on the portfolio analysis to reveal diversification benefits among various asset markets internationally. Hence this study contributes to this literature on the inter-relations of a specific asset market (stock), and in a specific nation (USA). The evident inter-relations support the notion of diversification benefits in the US stock markets.

Does corruption facilitate growth? A cross-national study in a non-linear framework

The article examines the corruption–growth relationship in a non-linear framework using panel fixed effects (FE) and system generalized methods of moments (SGMM) model for over 110 countries for the period 1984–2009. The results reveal that the least corrupt countries enjoy higher growth rates, whereas highly corrupt countries experience low growth. Furthermore, corruption has a positive and significant effect on economic growth up to a certain level and thereafter it reduces growth. The results are robust under various methodology and an alternative measure of corruption. JEL Classification: D73, O47, O50

The Impact of Greek Economic News on European Financial Markets. Evidence from the European Sovereign Debt Crisis

This chapter examines the impact of Greek economic news on European government bond, CDS, and stock markets. The impact of three categories of news is examined via the respective number of dummy variables, number of news per month, and news surprises of 2-year, 5-year, and 10-year government bonds and CDS on return, volatility, volatility jump, correlation, and correlation jump of government bonds, CDS, and stock indices of seven European countries, within a tobit regression framework. This chapter also discusses the implications of Greek economic news to policy actions. Recommendations driven by results are made to policy makers as well.

Forecasting Tourism Demand in Europe

The present study investigates the performance of the k nearest neighbor (kNN) forecasts in the context of European tourism demand. The forecasting performance of neural networks is examined across different parameterizations of the kNN model. The selection of the most appropriate kNN parameterization can produce more accurate forecasts. Tourism demand is forecasted monthly for twenty European countries. Tourism demand is measured via seven variables for the reason of consistency in results. kNNs better forecast tourism demand in shorter horizons; in specific, one month ahead. The parameterisation of the kNN model affects forecasting performance. More sophisticated parameterisations perform better the either an ARIMA model or a naive kNN parameterization. The inclusion of international stock indices significantly inrease forecasting accuracy. The more explanatory variables employed, the higher forecasting accuracy is retrieved. However, there is not a specific group of stock markets affecting more the kNN model’s forecasting accuracy. The forecasting accuracy of kNNs differs between three (Western, Eastern and Southern) European regions.

Properties of Realized Correlation

In this chapter, I examine the properties of four realized correlation estimators and model their jumps. The correlations are between the three main FTSE indices of the Athens Stock Exchange. Using intraday data I first construct four state-of-the-art realized correlation estimators which I then use in testing for normality, long memory, asymmetries and jumps and also in modelling for jumps. Jumps are detected when the realized correlation is higher than 0.99 and lower than 0.01 in absolute values. Then the realized correlation is modelled with the simple heterogeneous autoregressive (HAR) model and the HAR model with jumps (HAR-J). This is the first time, to the best of my knowledge, that the realized correlation between the three indices for the Greek equity market is examined.

Modeling Volatility & Jumps in the Athens Stock Exchange

In this paper I test for and model volatility jumps for the General Index (GD) of the Athens Stock Exchange (ASE), expanding the previous literature on the ASE in various ways. Using intraday data I first construct various state-of-the-art realized volatility estimators which I then use in testing and modeling for volatility jumps in the General Index of the ASE. The jump detection scheme allows, beyond testing for jumps, the extraction of both the jump and continuous components of volatility which are then used in modeling realized volatility with the class of Heterogeneous Autoregressive (HAR) models. This is the rst time, to the best of my knowledge, that volatility jumps are examined and modeled for the GD of the ASE, using a variety of realized volatility estimators.