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The purpose of this paper is to present the Greek value at risk (VaR) legislation framework and to highlight some of its major deficiencies, using not only theoretical scenarios but also empirical evidence. Moreover, this paper does not only highlight the VaR legislation’s framework deficiencies but also suggests legal interventions for its revision and a new-alternative, flexible and simple-to-be-applied filtered estimation method which improves the VaR evaluations.

The Greek legislation framework suggests that for the daily VaR to be estimated, a minimum data set of the previous year (250 observations) at the 99 per cent confidence level should be considered. This approach may lead to inaccurate VaR estimations, for example, when after a long-term growth period, there is a sudden recession period, because the data input is not representative to the current financial environment. Taking into serious consideration that high volatility periods are linked to a financial crisis, it is assumed that volatility could be an indicator for the financial environment representation. The conventional historical VaR back-tested results suggest that the specific methodology should be revised, especially during the high volatility period. For the newly suggested filtered VaR, the data sample is divided into several regimes depending on the volatility range. The filtered VaR estimation process applies the conventional historical methodology but uses different historical data input depending on the current volatility. This new approach improves the VaR estimation by reducing the VaR daily violations.

The findings regarding the current legislation framework suggest that the financial analysts in Greece have a motivation to adopt a relative VaR approach for risk asset class portfolios (e.g. Greek domestic equity mutual funds), which enables them to bear increased risk without presenting it to the investors. For lower risk portfolios, the absolute VaR may be useful for increased risk bearing strategies. The stricter VaR approaches are preferred to be adopted because stricter VaR estimations are linked to a reduced number of violations. The filtered volatility approach improves the VaR estimations (fewer violations are relative to the conventional approach).

This methodology is designed to be applied for the VaR estimation, but it could be partly applied in other fields of the financial analysis study.

The suggested methodology could present efficient VaR estimation without using sophisticated procedures or expensive VaR systems. Therefore, it could be easily applied by the risk analysts. Moreover, the overview of the Greek legislation’s framework could be useful not only for the Greek regulators but also for the authorities in countries with a similar regulation.

The newly proposed methodology is so accurate and simple to apply that it could have far-reaching impact on practitioners. Finally, this is the first paper that examines the Greek VaR legislation framework in detail.

This study examines the impact of the Russo-Ukrainian War on the commodity markets of oil, wheat, and natural gas, in which these two countries play/have a dominant role. The author examines if during these extreme events an anti-leverage effect emerges and indicates the abnormal conditions of the markets.

The author uses daily returns from the future prices of brent oil, wheat, and natural gas for the period 31.3.2020–31.3.2022 and applies the exponential and the threshold asymmetry GARCH models.

The empirical findings confirm the existence of anti-leverage effect in the wheat and natural gas commodity markets and point to the existence of the leverage effect in the Brent market.

The existence of the anti-leverage effect means that stock prices and volatility present a positive relationship which is in contrast to the normal/conventional stylized facts which suggest the opposite. These findings could be useful for scholars and practitioners because they enable them to examine market abnormalities using the anti-leverage effect as a criterion and explore its relationship with extreme conditions and strategies that lead to skyrocketing increases in prices.

This study investigates the anti-leverage effect in commodity markets under extreme conditions, which has received little attention in the literature. Moreover, this paper links the existence of the anti-leverage effect with abnormal growth in asset prices, which shows a new direction for the behavior of assets when extreme conditions emerge.

We examine the aggregate price trend of the Greek housing market from a behavioral perspective.

We construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.

The results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.

To investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

This study examines the Gamestop (GME) short squeeze in early 2021. Using intraday data for the period 4/1/2021–5/2/2021, the author provides empirical evidence that the GME stock price exhibited abnormal behavior.

The author uses the popular Runs test to show that the GME returns were not randomly distributed, which is an indication of a violation of the Efficient Market Hypothesis (EMH). The main objective of the paper is to provide new quantitative evidence that stock returns are abnormal when short squeeze conditions emerge. The author employs the asymmetry Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models (the Exponential GARCH (EGARCH) and the Threshold GARCH (TGARCH)) and provides evidence that an exceptional time series feature emerged during the examined period: the antileverage effect.

The results show that the GME returns were not randomly distributed during the examined period and the asymmetry GARCH models indicate that, in contrast to what the time series normally show, volatility increased when the GME prices increased.

This paper presents a new/alternative approach for the study of EMH and abnormal returns in financial markets. Further studies on market performance during similar short squeeze conditions should be carried out in order to obtain empirical evidence for the antileverage effect abnormality.

This paper could be useful for scholars who examine the EMH in financial markets because it suggests an additional method for testing abnormalities. It also presents a useful tool that allows practitioners to monitor for indications of abnormality in the stock market during a short squeeze, since the emergence of the antileverage abnormality could function as such an indication. Additionally, the outcome of this analysis could be useful for regulators because coordination among investors is easier than ever in the Internet era and such events may happen again in the future; even under normal (not short squeeze) conditions and lead to market instability.

This research differs from other studies that examine the GME case because it presents a new way to quantitatively present the abnormal performance of the stock markets for reasons that could be linked with the emergence of short squeeze conditions.

The purpose of this paper is to re-examine in detail the banking window dressing (WD) theory. Using data from the Greek banking industry during the euro period (2001-2013), the results suggest that there are signs of upward assets and deposits WD. Moreover, it tries to explain, using new-alternative approaches, the incentives for the WD and how the deposits WD occur.

To examine why bank managers upwardly WD the deposits and which strategy increases the regular payments cost, the author uses a Granger causality procedure and confirms the theory that the aim of increased assets report causes the deposits WD. Moreover, using a GARCH(1,1) and a methodological approach that is usually used in calendar anomalies, but is more flexible than the one usually applied, enables us not only to confirm that there is a WD but also under which mechanisms it occurs.

Bank managers prefer to pay increased regular payments due to the upward deposits WD to upwardly WD the bank assets, for the following reasons: they receive compensation depending on the assets’ volume, they gain prestige and most managers try to present increased bank assets and deposits to include them in the “too big to fail ” (TBTF) regime. The author also finds that managers increase the new offered bank premiums in the quarters’ end to attract more deposits, and in this way, the deposits WD occurs.

The findings of this study are limited to the Greek case, but the methodological approach may be applied to other cases to examine the WD theory and it could present a new-flexible way for WD research.

Examining the institutional framework, the theoretical background and the results, the author may suggest that reforms, at least in the way that the regular payment to the Greek deposits insurer are paid, should be applied. In Greece, 80 per cent of the regular DIS payments are deposited in the same financial institution. This reduction of the ratio significantly increase the deposits WD cost.

The contribution of this paper is to re-examine the WD theory and to suggest new and more flexible methodological approaches. Moreover, this is the first study that examines the WD for the Greek case.

This paper aims to examine the month and the trading month effects under changing financial trends. The Greek stock market was chosen to implement the authors' assumptions because during the period 2002-2012, there were clear and long-term periods of financial growth and recession. Thus, the authors examine whether the financial trends influence not only the Greek stock market’s returns, but also its anomalies.

Daily financial data from the Athens Exchange General Index for the period 2002-2012 are used. The sample is separated into two sub-periods: the financial growth sub-period (2002-2007), and the financial recession sub-period (2008-2012). Several linear and non-linear models were applied to find which is the most appropriate, and the results suggested that the T-GARCH model better fits the sample.

The empirical results show that changing economic and financial conditions influence the calendar effects. The trading month effect, especially, completely changes in each fortnight following the financial trend. Regarding the January effect, which is the most popular month effect, the results confirm its existence during the growth period, but during the recession period, we find that it fades. Therefore, by examining the aforementioned calendar effects in different periods, different conclusions may be reached, perhaps because the financial trends’ influence is ignored.

The empirical results confirm the authors' assumption that a possible explanation for the controversial empirical findings regarding the calendar anomalies may be the different financial trends. However, these are some primary results that are confirmed only for the Greek case. Further empirical research for deeper stock markets and/or a group of countries may be useful to reach conclusions regarding the financial trends’ influence on the calendar anomalies patterns.

The findings are helpful to anyone who invests and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, proving useful for investors who take these anomalies into account when they plan their investment strategy.

This paper contributes to the literature by presenting an alternative methodological approach regarding the calendar anomalies study and a new explanation for the calendar effects existence/fade through time by examining the calendar anomalies patterns under a changing economic environment and financial trends.

This paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.

The connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.

This analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.

The connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Citizen's service centers (CSCs) are front-end delivery public points which aim to reduce bureaucratic procedures and improve citizens' services. The present study, based on the citizens' perceptions, aims to explore the impact of the CSCs' service quality on citizens' satisfaction and also to identify statistically significant differences with regard to service quality dimensions and citizen satisfaction among different groups of citizens.

A questionnaire survey was carried out based on the five dimensions of the SERVPERF model. Random sampling was used to acquire a representative and reliable sample of 1,226 respondents. Descriptive statistics, multiple linear regression analysis, T-test and ANOVA were applied to analyze the data.

According to the citizens' perceptions, the levels of the service quality dimensions and citizen satisfaction are medium to high. The service quality dimensions have a statistically significant impact on citizens' satisfaction. Moreover, statistically significant differences are observed among groups of citizens in terms of the service quality dimensions and citizen satisfaction.

The present study is stimulated by the existing gap identified in the literature in the field of the public sector and more specifically in the CSCs. Building on the public sector literature, the study examines the relationship between service quality dimensions and citizens' satisfaction from the services provided by the Greek CSCs, which are innovative public service organizations operating all over Greece. It also highlights key implications for public organizations and government policy decision-makers based on citizens' demographic characteristics.