Konstantinos Gkillas

Hellenic Mediterranean University · School of Management and Economics

We analyze various types of models for Value at Risk (VaR) forecasts for daily copper returns. The period of the analysis is from January 4, 2000 to January 14, 2021 including 5290 daily closing prices. The models considered are GARCH-type models, the Generalized Autoregressive Score model, the Dynamic Quantile Regression model, and the Conditional...

The COVID-19 pandemic necessitated the production of mathematical models that were able to explain and thoroughly study various aspects and features of the pandemic. In this work, we provide a well-stated mathematical model to explain and simulate the evolution of the COVID-19 pandemic. To test our models performance and validity , we utilize actua...

In the present paper, we do provide more specific results on stochastic integration and stochastic differential equations on \(L^{p}\) -spaces. We also may obtain the existence and uniqueness of strong solutions to these stochastic differential equations. These results are actually extensions of equivalent results on Brownian stochastic differentia...

This paper’s content is devoted to the study of the monetary utility functions and their use in optimal portfolio choice and optimal risk allocation. In most of the relative papers, the domain of a monetary utility function is a dual space. This approach implies that closed and convex sets are weak-star compact. The main contribution of the present...

Which and how many covariates are relevant to educational hypergamy in the matching market? This paper addresses these questions by using a Lasso quantile regression framework applied on a unique survey containing information with respect to education or other characteristics associated with economic well-being. Our findings provide evidence for th...

In this paper, we study the generalized Johnson distributions’ class and its applications in finance and risk theory. The recent literature on Johnson distributions displays a better gooodness of fitting for data coming from financial markets, such as portfolio returns. However, a gereral question in risk theory and finance is the following: Which...

This paper examines the effect of R&D investment on firms’ level cash holdings in the UK market after the financial market crisis, using the trade-off model and the pecking order model as a theoretical backdrop. For this purpose, we employ a sample of UK listed non-financial no-utility firms throughout 2010–2018. Our findings indicate that R&D spen...

Though there are diverse factors that influence crude oil prices, we examine the degree of influence of factors, such as demand, supply, and geopolitical and economic agents’ expectations. The purpose of this study is to determine the necessary factors that influence crude oil prices, to find the level of factors required to produce the specific le...

We study the importance of economic uncertainty so as to predict realized jumps (hereafter jumps) in the pound-dollar exchange rate. The empirical analysis covers the time period from February 1900 to May 2018 on a monthly basis, incorporating several market states, including various booms and crashes. First, we apply a standard linear Granger caus...

We study the impact of Greek government‐debt crisis events on inter‐relations of European financial markets during the European sovereign debt crisis. To this end, we examine the effects of three categories of Greek government‐debt crisis events in the realized correlations and correlation jumps of government bonds, CDS, and stock indices of seven...

This paper proposes a novel asymmetric jump model for modeling interactions in discontinuous movements in asset prices. Given the jump behavior and high volatility levels in cryptocurrency markets, we apply our model to cryptocurrencies to study the impact of various types of jumps occurring in one cryptocurrency’s price process on the discontinuit...

The role of non-financial sector market fluctuations such as the role of oil price uncertainty either on financial stability or on policy-related economic uncertainty has not been investigated extensively. This study examines the connectedness of financial stress and economic policy uncertainty with a non-financial market of Brent oil and its price...

News about referendums and the ongoing evolution of a global contagious increase uncertainty about the development of economic fundamentals reflected by increased volatility in the financial markets. In this paper, employing volatility impulse response functions and assessing the volatility spillovers we examine intra-market volatility transmission...

This paper contributes to the debate on the role of geographic proximity as a driver of information diffusion and price discovery from a novel angle by examining the tail dependence structure of nine African stock markets that are linked by a plethora of free trade areas and economic unions. While we observe generally stronger dependency for negati...

We analyze the predictive power of time-varying risk aversion for the realized volatility of crude oil returns based on high-frequency data. Using random forests, and their extensions to quantile random forests and extreme random forests, we show that risk aversion improves out-of-sample accuracy of realized volatility forecasts. The predictive pow...

We study the predictive value of transaction activity in the bitcoin network for the realized volatility of bitcoin returns constructed by high-frequency data. As an alternative modeling approach to the popular linear heterogeneous autoregressive model, we provide out-of-sample forecasts for realized volatility of bitcoin returns employing machine...

Due to the COVID-19 pandemic originating in China in December 2019, apart from the grave concerns on the exponentially increasing casualties, the affected countries are called to deal with severe repercussions in all aspects of everyday life, from economic recession to national and international movement restrictions. Several regions managed to han...

The present research investigates the impact of trading volume on stock return volatility using data from the Greek banking system. For our analysis, the empirical study uses daily measures of volatility constructed from intraday data for the period 5 January 2001–30 December 2020. This period includes several market phases, such as the latest fina...

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predi...

We use intraday data to construct measures of the realized volatility of bitcoin returns. We then construct measures that focus exclusively on relatively large realizations of returns to assess the tail shape of the return distribution, and use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether these measures help...

We study the simultaneity impact of the European Central Bank news on the daily realized volatility transmission mechanism (spillovers) among various US spot and futures markets. To this end, we apply a bias-corrected vector autoregressive model via Wild bootstrap simulation. We use minute-by-minute intraday data to construct daily realized volatil...

We examine the forecasting power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for gold market returns volatility via the heterogeneous autoregressive realized variance (HAR-RV) model. Our results show that the EMVID index increases realized variance (RV) at the highest level of statistical significance...

We study the role of OPEC meeting dates and production announcements for predicting jumps in the oil market. The analysis covers the daily period from 2nd December 1997 to 26th May 2017, with the start and end dates corresponding to our availability of intraday oil-price data. We start our analysis by applying the standard linear Granger non-causal...

We analyze the role of the US–China trade war in forecasting out-of-sample daily realized volatility of Bitcoin returns. We study intraday data spanning from 1st July 2017 to 30th June 2019. We use the heterogeneous autoregressive realized volatility model (HAR-RV) as the benchmark model to capture stylized facts such as heterogeneity and long-memo...

We study the day−of−the−week effect in relation to bid-ask spreads determinants by employing a comprehensive dataset of international equity markets from 2000 to 2015. To this end, we apply a battery of tests regarding return patterns and a panel cointegration approach. Given that there is growing evidence that stock markets behave differently on d...

Option pricing depends heavily on the volatility measure used. We examine two potential routes to improve the outcome of option pricing: extracting the variance from futures prices instead of the underlying asset prices, and calculating the variance in different frequencies with intraday data instead of daily closing prices. We perform a valuation...

This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at d...

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this...

In this paper, we study the contribution of the geographical (both the regional and international) aspect of news releases, related to the Greek sovereign debt crisis. We investigate the impact of the Greek debt crisis via economic news and surprises on Euro exchange rate volatility and volatility-jumps within a Tobit regression framework. In parti...

Over the past decade, the UK has witnessed significant booms in the real estate market, and housing prices have experienced increases. Since 1997, the housing price has almost tripled, which is far beyond the long-term trend. To identify the existence of housing bubbles is a crucial issue for any country to prevent possible damage to economies and...

During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users’ online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of e...

We extend existing studies by considering the higher-order moments relationships among crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to June 10, 2018, we analyze spillovers in jumps and realized second, third, and fourth moments among crude oil, gold, and Bitcoin markets via Granger causality and generalized...

We study the role of rare disaster risks in discontinuities (jumps) in the US equity market. To this end, we use data from Dow Jones Industrial Average and International Crisis Behavior database (as a proxy for rare disaster risks) over the period January 1918 – December 2013. We apply a quantile dependence approach in order to detect directional p...

We study the impact of economic news releases of the United States on the tail risk of Mexican financial markets. We also control for the impact of (domestic) economic news releases of Mexico. We consider daily data for: (i) the equity market, (ii) the foreign exchange market, as well as (iii) sovereign bonds, (iv) financial institutional bonds and...

We study the financial determinants of cash holdings and discuss the importance of firm size in the post-crisis period. We employ panel data regression analysis on a sample of 6629 non-financial and non-utility listed companies in the United Kingdom from 2010 to 2018. We focus on the comparative analysis of large, medium, and small size firms in te...

Due to the COVID-19 pandemic originating in China in December 2019, apart from the grave concerns on the exponentially increasing casualties, the affected countries are called to deal with severe repercussions in all aspects of everyday life; from economic recession to national and international movement restrictions. Several regions managed to han...

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency dat...

We apply the heterogeneous autoregressive realized volatility (HAR-RV) model to examine the importance of investor happiness in predicting the daily realized volatility of gold returns. We estimate daily realized volatility by employing intraday data providing both in-sample and out-of-sample predictions. Our in-sample results reveal that realized...

We use the the heterogeneous autoregressive realized volatility (HAR-RV) model to analyze both in sample and out-of-sample whether a measure of investor happiness predicts the daily realized volatility of oil-price returns, where we use high-frequency intraday data to measure realized volatility. Full-sample estimates reveal that realized volatilit...

During the difficult times that the world is facing due to the COVID-19 pandemic that has already had severe consequences in all aspects of our lives, it is imperative to explore novel approaches of monitoring and forecasting the regional outbreaks as they happen or even before they do. In this paper, the first approach of exploring the role of Goo...

This paper examines the dependence structure and dynamics between gold and oil prices. Specifically, we study the hedge and safe haven ability of gold for oil prices using daily gold prices and West Texas Intermediate Institute (WTI) crude oil spot prices. To this end, we employ time-varying Markov switching copula models. The period of the analysi...

In this paper, we first estimate the monthly realised correlation, based on daily data, between stock returns of the United States (US) and Bitcoin returns. Then, we relate the realised correlation over the period October 2011 to May 2019 with a news‐based measure of the growth of trade uncertainty of the US. Our results show that the realised corr...

We propose novel nonparametric estimators for stochastic volatility and the volatility of volatility. In doing so, we relax the assumption of a constant volatility of volatility and therefore, we allow the volatility of volatility to vary over time. Our methods are exceedingly simple and far simpler than the existing ones. Using intraday prices for...

We study 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 parametrization can produce more accurate forecasts. Tourism demand is forecast monthl...

We analyze the role of global and regional measures of financial stress in forecasting realized volatility of the oil market based on 5-min intraday data covering the period of 4th January, 2000 until 26th May, 2017. In this regard, we use various variants of the Heterogeneous Autoregressive (HAR) model of realized volatility (HAR-RV). Our main fin...

In this paper, we analyse the role of oil price shocks, derived from expectations of consumers, economists, financial market, and policymakers, in predicting volatility jumps in the S&P500 over the monthly period of 1988:01–2015:02, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality tests...

We examine the impact of economic news releases on returns, volatility and jumps of the stock and foreign exchange markets of South Africa. We also assess the impact of macroeconomic determinants. The dataset range is fifteen years covering the period from January, 2000 to December, 2014. Results are robust to different sub-periods before and after...

This 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 parameterisations of the kNN model. The selection of the most appropriate kNN parameterisation can produce more accurate forecasts. Tourism demand is...

We examine the impact of the Indian cricket team's performance in one-day international cricket matches on return, realized volatility and jumps of the Indian stock market, based on intraday data covering the period of 30th October, 2006 to 31st March, 2017. Using a nonparametric causality-in-quantiles test, we were able to detect evidence of predi...

We investigate potential mean and volatility spillovers among sovereign bond yield spreads for five peripheral countries of the euro area. We focus on Greece, Ireland, Italy, Portugal and Spain during the European sovereign debt crisis. We propose a bootstrap bias-corrected bivariate Vector Autoregressive Moving Average (VARMA), GARCH-in-Mean, asym...

We examine the size of contagion (i.e., integration and co-movement) of weighted portfolios on a global level determining whether the amplification of transmission channels among either emerging or developed financial markets can be affected by different values of a country's characteristics (macro-economic variables) on a regional and global level...

We study the in- and out-of-sample predictive value of time-varying risk aversion for realized volatility of gold returns via extended heterogeneous autoregressive realized volatility (HAR-RV) models. Our findings suggest that time-varying risk aversion possesses predictive value for gold volatility both in- and out-of-sample. Time-varying risk ave...

We use a quantile-regression heterogeneous autoregressive realized volatility (QR-HAR-RV) model to study whether geopolitical risks have predictive value in sample and out-of-sample for realized gold-returns volatility estimated from intradaily data. We consider overall geopolitical risks along with a decomposition into actual risks (i.e., acts) an...

We use intraday data to construct measures of realized volatility, realized kurtosis, and realized skewness of returns of six major exchange rates vis-à-vis the dollar. The currencies under consideration are: (i) Australian dollar, (ii) Canadian dollar, (iii) Swiss franc, (iv) euro, (v) British pound, and (vi) Japanese yen. The period of the analys...

We study the contemporaneous tail dependence structure between Economic Policy Uncertainty and US stock market returns using multivariate extreme value theory. Extreme correlations are found to display an asymmetric pattern with respect to the level of policy uncertainty regardless of the state of the market. The findings suggest that extreme outco...

We investigate the day-of-the-week effect in relation to bid ask spreads determinants by employing a comprehensive dataset of international equity markets from 2000 until 2015 incorporating different market phases, such as various booms and crashes. To this end, we apply a battery of tests regarding returns patterns and a panel cointegration techniqu...

In this paper, we investigate the non-parametric relation between political risk and Mexican financial markets. We focus on stock, foreign exchange, financial institutions bond, corporate bond and sovereign bond markets. We apply a quantile correlation approach between five categories of the most used political risk indicators and volatility discon...

We investigate the contemporaneous relation between return and transaction volume in distribution tails under the restrictions on transactions due to the capital controls implemented on the Athens Stock Exchange in July 2015. We use bivariate extreme value theory to model the tail dependence structure. We show that restrictions on transactions have...

We investigate the asymmetries in the African financial markets; both stock and exchanges markets, namely Botswana, Egypt, Kenya, Mauritius and South Africa. The dataset begins on January 1, 2001 and ends on January 20, 2018, for a total of 4479 trading days. We apply an asymmetric threshold approach with an error-correction model with four dummy v...

We study the tail behaviour of the returns of five major cryptocurrencies. By employing an extreme value analysis and estimating Value-at-Risk and Expected Shortfall as tail risk measures, we find that Bitcoin Cash is the riskiest, while Bitcoin and Litecoin are the least risky cryptocurrencies.

In this paper we analyse the role of a news-based index of geopolitical risks (GPRs), in predicting volatility jumps in the Dow Jones Industrial Average (DJIA) over the monthly period of 1899:01 to 2017:12, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality test failed to detect any evide...

This paper examines the importance of macroeconomic announcements, nonlinearity and combining, to realised volatility forecasting in equity-, energy- and commodities-mini-futures markets, by using intraday frequency data. We use three evaluation criteria to detect whether the predictions are more accurate on the out-of-sample announcement days or o...

This paper examines the importance of macroeconomic announcements, nonlinearity and combining, to realised volatility forecasting in equity-, energy-And commodities-mini-futures markets, by using intraday frequency data. We use three evaluation criteria to detect whether the predictions are more accurate on the out-of-sample announcement days or on...

We examine the impact of US economic news releases in the liquidity of eleven not so extensively researched emerging stock markets. We employ ten liquidity measures. The sample begins from June 2007 up to December 2016. Analysis is performed in a weekly frequency. China is the least liquid Asian market. Peru is the most liquid Latin American market...

Is Bitcoin the new digital Gold? To answer this question, we investigate the potential benefits of Bitcoin during extremely volatile periods. To this end, we focus on the extreme correlation of asset returns estimated by multivariate extreme value theory. Considering first a position in equity markets, we find -similarly to previous studies- that t...

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, cor...

This paper investigates the properties of realized volatility and correlation series in the Indian stock market by employing daily data converting to monthly frequency of five different stock indices from January 2, 2006 to November 30, 2014. Using non-parametric estimation technique the properties examined include normality, long-memory, asymmetri...

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 no...

We investigate the day-of-the-week effect in relation to bid ask spreads determinants by employing a comprehensive dataset of international equity markets from 2000 until 2015 incorporating different market phases, such as various booms and crashes. To this end, we apply a battery of tests regarding returns patterns and a panel cointegration techni...