Contagion in Futures FOREX Markets for the Post- Global Financial Crisis: A Multivariate FIGARCHcDCC Approach

This paper seeks to investigate the time-varying conditional correlations to the futures FOREX market returns. We employ a dynamic conditional correlation (DCC) Generalized ARCH (GARCH) model to find potential contagion effects among the markets. The under investigation period is 2014-2019. We focus on four major futures FOREX markets namely JPY/USD, KRW/USD, EUR/USD and INR/USD. The empirical results show an increase in conditional correlation or contagion for all the pairsof future FOREX markets. Based on the dynamic conditional correlations, KRW/USD seems to be the safest futures FOREX market. The results are of interest to policymakers who provide regulations for the futures FOREX markets. JEL Classification Codes: C58, C61, G11, G1

Forex and equity markets spillover effects among USA, Brazil, Italy, Germany and Canada in the aftermath of the global financial crisis

In this paper, we investigate the spillover effects of FOREX and equity markets for USA, Brazil, Italy, Germany, and Canada on the basis of daily data. We test for contagion co - movements for the period 2010 - 2018 post-global financial crisis, using the trivariate AR - diagonal BEKK model. The estimated dynamic conditional correlations show the strongest contagion effects for the pairs of markets: S&P500 - BOVESPA, S&P500 - FTSEMIB, S&P500 - DAX30, and S&P500 - S&PTSX. For institutions, multinational corporations and active investors, a portfolio consisting of financial assets from the above markets is extremely risky

Contagion in Crude Oil Futures Market and 3Y, 4Y and 5Y CDS Markets for the Post- Global Financial Crisis Period: A Multivariate GARCH-cDCC Approach

This paper seeks to investigate the time-varying conditional correlations to the crude oil futures contract returns and the private Credit Default Swap market returns of Germany and France. We employ a dynamic conditional correla- tion (DCC) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model to find potential conta- gion effects between the markets. The time under investigation is the 2011±2018 period. We focus on the CDSs of the biggest banks in Germany and France, namel\: Sociptp Gpnprale and Deutsche Bank AG, using 3-, 4- and 5- year maturity CDSs. Empirical results show an increase in conditional correlation or contagion for the following pairs of markets: Sociptp Gpnprale CDS 3Y-Crude oil futures; Sociptp Gpnprale CDS 4Y-Crude oil futures; and Sociptp Gpnprale CDS 5Y-Crude oil futures for two periods (10/2014±12/2014 and 04/2017±11/2017). The results are of interest to policymakers who provide regulations for the CDS market

Stock Market Predictions using LSTM Neural Networks

Ο σκοπός αυτής της πτυχιακής είναι να αναλυθεί ο βαθμός στον οποίο τα ιστορικά οικονομικά δεδομένα μια μετοχής επαρκούν για να πραγματοποιηθούν ουσιώδεις προβλέψεις της μελλοντικής της τιμής με τη χρήση Μηχανικής Μάθησης. Η διαίσθηση πίσω από την εργασία μας είναι ότι, καθώς η τιμή μιας μετοχής κυμαίνεται, θεωρείται πως ακολουθεί κάποια μοτίβα τα οποία ελπίζουμε να αντιληφθούμε χρησιμοποιώντας Βαθιά Μάθηση και να τα αξιοποιήσουμε για μελλοντικές προβλέψεις. Αρχικά, θα παραθέσουμε το απαραίτητο θεωρητικό υπόβαθρο σχετικό με τη Μηχανική Μάθηση, εστιάζοντας ιδιαίτερα στα νευρωνικά δίκτυα που θα χρησιμοποιηθούν στη συνέχεια. Έπειτα, θα εξετάσουμε τις ήδη υπάρχουσες έρευνες σχετικά με τις προβλέψεις χρηματιστηριακών αγορών που χρησιμοποιούν παρόμοιες τεχνικές και θα προτείνουμε ένα μοντέλο παλινδρόμησης χρησιμοποιώντας Μακροχρόνια βραχυχρόνια μνήμη (LSTM), μία αρχιτεκτονική Επαναλαμβανόμενων νευρωνικών δικτύων (RNN) που είναι πλέον κατάλληλη για τέτοιου είδους προβλήματα. Τέλος, χρησιμοποιώντας δεδομένα από το Χρηματιστήριο Αθηνών, θα επιχειρήσουμε να πραγματοποιήσουμε προβλέψεις για τις μελλοντικές πορείες των τιμών των μετοχών και να εξάγουμε συμπεράσματα από αυτές.The purpose of this thesis is to analyze the extent to which the historical financial data of a stock suffice to make meaningful predictions about its future price with the use of Machine Learning. The intuition behind our task is that, as the price of a stock fluctuates, it is assumed to follow certain patterns which we hope to capture using Deep Learning and utilize for future predictions. Firstly, we will expand on the necessary theoretical background information regarding Machine Learning, focusing particularly on the neural networks that will later be used. Following that, we will examine how existing research regarding stock market forecasting using similar techniques fared in the past and we will proceed to propose a regression model using Long short-term memory (LSTM), a Recurrent Neural Network architecture most suitable for this kind of tasks. Finally, using data from Athens Stock Exchange, we will attempt to make predictions about the future trajectories of the stocks’ prices and draw conclusions from them

Financial Contagion and Volatility Spillover: an exploration into Bitcoin Future and FOREX Future Markets

This paper examines the time-varying conditional correlations between Bitcoin future market and five FOREX future markets. A sixvariate dynamic conditional correlation (DCC) GARCH model is applied in order to capture potential contagion effects between the markets for the period 2017-2019. Empirical results reveal contagion during the under investigation period regarding the one sixvariate model, showing potential volatility transmission channels among the future markets. Findings have crucial implications for policymakers who provide regulations for the above derivative markets.&nbsp

Θέματα εφαρμοσμένων χρηματοοικονομικών

This dissertation consists of four self-contained chapters in the form of papers. The first chapter investigates the volatility spillover effects and the contagion to sovereign CDS spread returns for Germany, France, China and Japan against USA. To the best of our knowledge, this is the first empirical research in the literature, which investigates potential spillovers and contagion effects among sovereign CDS markets. We use daily data from October 2011 to February 2018. Employing a fourvariate cDCC-AR-FIGARCH model, we find evidence of spillover effects for all the pairs of markets. Furthermore, we find empirical evidence of contagion for the pairs of markets: Germany – France, Germany – Japan and France – Japan. Regarding China’s CDS market we obtain little empirical support for contagion with the rest of the countries. The results are of interest to policymakers, who provide regulations for the CDS markets, as well as to market-makers. The second chapter investigates the spillover effects and the contagion to major equity and FOREX markets of G20. The financial markets under scrutiny are those of USA, Brazil, Italy, Germany and Canada. The frequency of the data is daily. We set the sample period from April 2010 to April 2018, namely after the GFC. Other related empirical work include Kanas (2000), who investigated the existence of spillovers between national equity and FOREX markets, by employing a trivariate AR-diagonal BEKK model for S&P 500, national equity markets and the respective FOREX markets. Our empirical results find evidence of spillovers and contagion effects for the pairs of markets: S&P500-BOVESPA, S&P500-FTSEMIB, S&P500-DAX30 and S&P500-S&PTSX. Moreover, the pairs of markets S&P 500-CAD/USD, S&P 5000BRL/USD and BOVESPA-BRL/USD present no contagion. The resultsare of interest to individual investors, who want to diversify their portfolios through international financial market investments. The third chapter investigates the spillovers and the financial contagion of four major FOREX markets. The FOREX markets are those of EUR/USD, JPY/USD, CHW/USD and GBP/USD. Lee (2010) investigates ten FOREX markets in Asia and Latin America to USD, among others and finds evidence of spillover effects from JPY/USD on Asian currency markets. A fourvariate dynamic Conditional Correlation Generalized ARCH (DCC-GARCH) model is employed for the period April 2011 to February 2018. The empirical results suggest contagion for all the pairs of markets. Additionally, we find that EUR/USD and GBP/USD present the strongest contagion effects, while CHW/USD show the lowest contagion levels with the rest of the markets.The fourth chapter analyses the spillover and the contagion effects of MSCI (global index), NIKKEI 400 (Japan), CSI 300 (China) and S&P 500 (USA). We consider a portofolio analysis in order to produce the standardized residuals using in a trivariate cDCC-GARCH framework. Other research work include Miyakoshi (2003), who suggests the existence of spillover effects between USA and Asian national equity markets. We extend the above analysis by taking into consideration the individual effects of MSCI on three of the most important national equity markets. We use daily data for the period 2008-2018. The main empirical results are the following: (1) portfolio analysis results suggest that MSCI has a significant positive influence on all equity market returns, (2) we find empirical evidence of spillovers on all pairs and (2) we find contagion for the pairs of markets: NIKKEI 400-CSI 300, NIKKEI 400-S&P 500 and S&P 500-CSI 300 that indicate risky positive correlations from an investor’s perspective