ALLOWING FOR JUMP MEASUREMENTS IN VOLATILITY: A HIGH‐FREQUENCY FINANCIAL DATA ANALYSIS OF INDIVIDUAL STOCKS

Following recent advances in the non‐parametric realized volatility approach, we separately measure the discontinuous jump part of the quadratic variation process for individual stocks and incorporate it into heterogeneous autoregressive volatility models. We analyse the distributional properties of the jump measures vis‐à‐vis the corresponding realized volatility ones, and compare them to those of aggregate US market index series. We also demonstrate important gains in the forecasting accuracy of high‐frequency volatility models.     

Efficiency evaluation of Greek equity funds

This study assesses the relative performance of Greek equity funds employing a non-parametric method, namely Data Envelopment Analysis (DEA). Specifically, we evaluate the funds’ total productivity change using the DEA-based Malmquist Index. Our results reveal significant losses in funds’ productivity for the period of 2003–2009, which calls for the attention of domestic policy makers and market regulators. Significant implications for the investors’ fund selection process arise from our analysis since we are able to identify potential sources of operational inefficiencies. Employing a panel logit model we document a significant negative relationship between the probability of being efficient and funds’ size, a finding which may be related to the microstructure of the domestic stock market. Furthermore, we provide evidence against the notion of funds’ mean-variance efficiency.     

E-procurement in the Greek food and drink industry: Drivers and impediments

Most empirical research on e-procurement has focused on large economies and technology-related industries, paying little attention to smaller economies and traditional industries. This paper addresses this gap by presenting a study on the state and development of e-procurement in the Greek food and drink industry, based on four case studies with some of the largest organisations in the industry. This study indicates that the uptake of e-procurement has been slow and reveals some important impediments, such as the uncertainty of the technology and its benefits, the lack of infrastructure and skills and the traditional nature of the industry. These results led to a series of findings, propositions for further investigation. The drivers and impediments to e-procurement have been classified into four different levels: global, country, industry and firm. Each of these levels requires a different approach to dealing with it, having implications for practitioners and policymakers.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Did Baltic stock markets offer diversification benefits during the recent financial turmoil? Novel evidence from a nonparametric causality-in-quantiles test

Motivated by financial liberalization investors seek for new investment opportunities through international portfolio diversification. To this end we explore any asymmetric causal relationship between developed European stock markets (Germany, France and UK) and emerging Baltic markets namely; Estonia, Latvia and Lithuania. Our analysis focuses on the period before and after countries’ EU accession and pre- and post the global financial crisis. For this purpose, both the standard parametric test for causality and a novel nonparametric test for causality-in-quantiles are employed. The results of both the parametric and nonparametric Granger causality test support a causal relationship in mean that runs from all of the major markets to the Baltic markets across both samples. The results imply the existence of significant nonlinear return and volatility spillover from European markets to Baltic markets. Policy implications for international investors are also discussed.     

Social accountability and its auditing

This paper contains an analytical review of most of the approaches to social accounting/auditing. The following approaches are briefly described and their usefulness is critically appraised: (i) The perceived reality approach. (ii) The cost or outlay approach. (iii) The benefit-cost approach. (iv) The programme-management approach. (v) The inventory approach—social process auditing. (vi) The state-interference approach. (vii) The social-goal-programming approach.     

The Efficiency of the Realized Range Measure of Daily Volatility: Evidence from Greece

In this study we estimate and compare the realized range volatility, a novel efficient volatility estimator computed by summing high–low ranges for intra‐day intervals, to the recently popularized realized variance estimator obtained by summing squared intra‐day returns. Our results, derived from a Greek equity high‐frequency data set, show that realized range‐based measures improve upon the corresponding realized variance‐based ones in most cases, especially for the most actively traded stocks. The usefulness of high‐frequency data in measuring and forecasting financial volatility is apparent throughout the paper.