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We extend earlier work on the NoVaS transformation approach introduced by Politis (2003a, 2003b). The proposed approach is model-free and especially relevant when making forecasts in the context of model uncertainty and structural breaks. We introduce a new implied distribution in the context of NoVaS, a number of additional methods for implementing NoVaS, and we examine the relative forecasting performance of NoVaS for making volatility predictions using real and simulated time series. We pay particular attention to data-generating processes with varying coefficients and structural breaks. Our results clearly indicate that the NoVaS approach outperforms GARCH model forecasts in all cases we examined, except (as expected) when the data-generating process is itself a GARCH model.

We thank the Simon Center for Regional Forecasting at the John Cook School of Business at Saint Louis University – especially Jack Strauss, Director of the Simon Center and Ellen Harshman, Dean of the Cook School – for its generosity and hospitality in hosting a conference during the summer of 2006 where many of the chapters appearing in this volume were presented. The conference provided a forum for discussing many important issues relating to forecasting in the presence of structural breaks and model uncertainty, and participants viewed the conference as helping to significantly improve the quality of the research appearing in the chapters of this volume.³ This volume is part of Elsevier's new series, Frontiers of Economics and Globalization, and we also thank Hamid Beladi for his support as an Editor of the series.

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.

The volatile tax system is a huge disincentive for taking business, as frequent changes in the tax system create extremely difficult problems. It is a major barrier to entrepreneurship, it fails to address them economic inequalities, is too complex, changing very often, and is made in a way that facilitates tax evasion.

The factors that discourage investors from investing in our country are as follows (as research has shown in the past five years in our country by researchers and scientists): high taxation, complex institutional framework, bureaucracy, corruption, political liquidity and limited access to finance. These pathogens hamper the ability of the economy to produce competitive goods and quality services on the market.

The purpose of this paper is to investigate the main pathogens of the tax system which are a brake on the development of Greek entrepreneurship and how they can be tackled so that our country produces competitive goods and quality services.