Covariance stability test for exploring the impact of subprime financial crisis on the FOREX

The sub-prime crisis started from November 2006 to February 2008 is a global crisis that affected almost all economy activities in the world. In this study, we used the covariance stability test for exploring its impact towards foreign exchange rate among 15 currencies. Box’s M control chart and its root causes analysis are employed to understand the behaviour and interrelationship of FOREX’s structure among America and Europe continents. From the analysis, it shows that the structures of covariance from Jan, 2006 to Dec, 2008 are not stable.To be detail, if there is any shift on USD during April-June 2007, the nearest currencies that will received the impact are Argentine Peso, Chilean Peso and Rusia Ruble

Empirical Research on the Asymmetric Multifractal Properties in Financial Market Data

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 장우진.After the recent financial crisis, the importance of financial market analysis for financial risk management has been emphasized. Financial markets have diverse characteristics that are difficult to explain from the traditional models. Therefore, the effort on describing such characteristics is required. Specifically, many researches are actively conducted on the features of multifractal and asymmetric correlation in financial markets. Multifractal features can be characterized by various fractal features with self-similarity that does not change with scaleit is difficult to represent in a single fractal dimension. This feature can explain the complexity of stock market. The asymmetric correlation, depending on the market trend, represents the asymmetric structure of the financial market. In this context, this dissertation focuses on the asymmetric correlation of multifractal characteristics in the financial market data where the asymmetric market efficiency is measured using asymmetric multifractal property. At first, Price-based Asymmetric Multifractal Detrended Fluctuation Analysis (Price-based A-MFDFA) model is proposed to measure multifractal characteristics which asymmetrically follow the trend of market price. Given that previous models measure the multifractal characteristics based on the entire market, the price-based A-MFDFA model has its advantage by considering the asymmetrical characteristics according to different market conditions. Furthermore, the methods to investigate the cause of multifractal features and the asymmetry are also suggested based on the proposed model. The empirical results in the U.S. financial market data confirms the presence of asymmetric multifractal characteristic and the autocorrelation of the variance in uptrend market and fat-tailed distribution in downtrend market as the cause of multifractality. The results of time-varying asymmetric multifractality show that the difference between the degree of uptrend and downtrend multifractality increases during the financial crisis period. Secondly, a simulation method is applied to prove the ability of capturing the asymmetric multifractal features of the Price-based A-MFDFA model by examining the factors affecting the asymmetric multifractality. In order to mimic the stock market data, an artificial time series with asymmetric features are constructed using the Monte-Carlo simulation. Then, the asymmetric multifractality is observed for each time series using the proposed model. The results show that the proposed model can detect the artificial asymmetric characteristics. In addition, the effects of autocorrelation of time series, autocorrelation of volatility, the skewness and fat-tailed of distribution on the asymmetric long-range dependence and multifractal features are studied. Lastly, a framework for testing the existence of asymmetric long-range dependence and multifractality is proposed. The source of market inefficiency, which has not been identified in previous models, is examined through the uptrend and downtrend multifractal features. The result of thirty four countries suggests that, in the financial crisis period, the difference in the long-range dependence measure and degree of multifractality between uptrend and downtrend increases, whereas the uptrend degree of multifractality has a strong negative correlation with the stock price in financial crisis period. In addition, the relationship between asymmetric long-range dependence and rate of return is tested. In conclusion, the contribution of this dissertation is to further refine the ability of multifractal analysis on asymmetric characteristics in accordance with market conditions as well as the overall market. While past analysis of the overall market focuses on only the downtrend, it is possible to analyze both uptrend and downtrend market through the segmented asymmetric multifractal characteristics. Hence, the proposed model can provide much useful information to various market participants in the perspective of financial risk management.Chapter 1 Introduction 1 1.1 Resarch motivation and purpose 1 1.2 Theoretical background 5 1.3 Organiation of the research 9 Chapter 2 Asymmetric multi-fractality in the U.S. stock indices using the price-based model of A-MFDFA 10 2.1 Introduction 10 2.2 Price-based A-MFDFA method 13 2.3 Data description 16 2.4 Empirical results of asymmetric scaling behavior 18 2.4.1 Asymmetric fluctuation functions and their dynamics 18 2.4.2 Estimating the generalized Hurst exponent 22 2.4.3 Source of multi-fractality 24 2.4.4 Source of asymmetry 28 2.4.5 Time-varying multi-fractal asymmetry 29 2.5 Conclusion 33 Chapter 3 Study of asymmetric multifractal characteristics through various time series simulations 34 3.1 Introduction 34 3.2 Various probability distribution and time series model 36 3.2.1 Normal distribution 36 3.2.2 Skewed distribution 37 3.2.3 Students t-distribution 37 3.2.4 Autoregressive model 38 3.2.5 Autoregressive conditional heteroscedasticity model 38 3.2.6 Gereralized autoregressive conditional heteroscedasticity model 39 3.3 Method to generate time series using Monte-Carlo simulation 41 3.3.1 Homogeneous time series generating 41 3.3.2 Heterogeneous time series with previous datas sign 41 3.3.3 Heterogeneous time series with precious datas trend 41 3.4 Simulation results 43 3.4.1 Homogeneous time series simulation results 43 3.4.2 Heterogeneous time series with previous datas sign simulation results 50 3.4.3 Heterogeneous time series with precious datas trend simulation results 60 3.5 Conclusion 70 Chapter 4 Evaluating the asymmetric long-range dependence and multifractality of financial markets 72 4.1 Introduction 72 4.2 Methodology 76 4.2.1 Price-based A-MFDFA 76 4.2.2 Evaluating the existence of asymmetric long-range dependence and multifractality 78 4.3 Data description 81 4.4 Results and Discussion 84 4.4.1 Monte Carlo Simulation 84 4.4.2 The results for testing the existence of asymmetric long-range dependence and multifractality in each period 89 4.4.3 Time-varying asymmetric Hurst exponent and multifractality 95 4.5 Conclusion 99 Chapter 5 Concluding Remarks 102 5.1 Summary and contributions 102 5.2 Limitations and future work 106 References 108 Appendix 116 Abstract (in Korean) 149Docto

Commodity price volatility, stock market performance and economic growth: evidence from BRICS countries

Abstracts in English, Afrikaans and ZuluThe study investigated the nexus between commodity price volatility, stock market performance, and economic growth in the emerging economies of Brazil, Russia, India, China, and South Africa (the BRICS) predicated on two hypotheses. First, the study hypothesised that in modern integrated financial systems, commodity price volatility predisposes stock market performance to be non-linearly related to economic growth. The second hypothesis was that financial crises are an inescapable feature of modern financial systems. The study used daily data on stock indices and selected commodity prices as well as monthly data on national output proxies and stock indices. The study analysed data for non-linearities, fractality, and entropy behaviour using the spectral causality approach, univariate GARCH, EGARCH, FIGARCH, DCC-GARCH, and Markov Regime Switching (MRS) – GARCH. The four main findings were: first, spectral causality tests signalled dynamic non-linearities in the relationship between the three commodity futures prices and the BRICS stock indices. Second, the predominantly non-linear relationship between commodity prices and stock prices was reflected in the nexus between the national output proxies and the indices of the five main commodity classes. Third, spectral causality analysis revealed that the causal structures between commodity prices and national output proxies were non-linear and dynamic. Fourth, the Nyblom parameter stability tests revealed evidence of structural breaks in the data that was analysed. The DCC-GARCH model uncovered strong evidence of contagion, spillovers, and interdependence. The study added to the body of knowledge in three ways. First, micro and macro levels of commodity price changes were linked with corresponding stock market performance indicator changes. Second, unlike earlier studies on the commodity price – stock market performance – economic growth nexus, the study employed spectral causality analysis, single - regime GARCH analysis, Dynamic Conditional Correlation (DCC) – GARCH and a two-step Markov – Regime – Switching – GARCH as a unified analytical approach. Third, spectral causality graphs depicting relationships between stock indices and national output proxies revealed benign business cycle effects, thus, contributing to broadening the scope of business cycle theoryBusiness ManagementPhD. (Management Studies

The role of information efficiency in exchange rate forecasts: evidence from survey data

La previsione \ue8 un atteggiamento naturale di uomini e donne: si fanno previsioni perch\ue9 per prendere determinate decisioni oggi, bisogna sapere come sar\ue0 il mondo domani, e quindi come il futuro stato del mondo influenzer\ue0 il risultato delle scelte. In finanza, il termine "previsione" si riferisce alle aspettative degli individui sulla tendenza futura delle variabili studiate, sulla base di informazioni o intuizioni, a partire dal presupposto che gli individui abbiano una buona conoscenza del sistema in cui operano e dell'economia politica. Questa tesi, sviluppata in tre capitoli, ha come tema principale l'irrazionalit\ue0 degli investitori generata dall'inefficienza delle informazioni. L'analisi \ue8 stata effettuata sulle previsioni in valuta euro-dollaro presentate da varie istituzioni come banche, divisioni di ricerca di banche e centri di ricerca. I dati disponibili provengono dalla piattaforma Bloomberg. Nel primo capitolo \ue8 stato sviluppato un modello che stima gli errori di previsione come parametri per la valutazione della capacit\ue0 predittiva di ciascun predittore, mostrando se la differenza di valore assoluto tra il tasso spot e la previsione o revisione emessa aumenta, diminuisce o rimane invariata nel tempo per valutare la capacit\ue0 degli istituti finanziari di incorporare nuove informazioni in modo completo e tempestivo nel percorso temporale che porta alla data terminale per la quale \ue8 stata emessa la previsione. I risultati in accordo con la letteratura esistente hanno mostrato che le revisioni peggiorano le previsioni precedentemente emesse anzich\ue9 migliorarle, in tutti gli orizzonti considerati. Ci\uf2 significa che i predittori non riescono a imparare dai propri errori e quindi non riescono a incorporare nuove informazioni in modo efficiente. Inoltre, \ue8 stato dimostrato che i predittori avrebbero ottenuto un errore di previsione in un valore assoluto inferiore, utilizzando il modello di camminata casuale o emettendo una previsione pari al tasso spot noto al momento della previsione. Inoltre, al fine di rafforzare i risultati raggiunti nel primo capitolo, le previsioni dei predittori disponibili sono state analizzate attraverso il test di Hurst. L'applicazione di un coefficiente di memoria a lungo termine permette di capire in che misura le previsioni passate influenzano le previsioni future. Maggiore \ue8 il valore del coefficiente in un intervallo compreso tra 0 e 1, maggiore sar\ue0 la memoria lunga della serie storica. I risultati hanno mostrato valori quasi sempre maggiori di 0,5, punto in cui si pu\uf2 affermare l'efficienza delle variabili analizzate. Infine, nel terzo capitolo di questo documento, \ue8 stato applicato il test statistico di Toda e Yamamoto (1995) con l'obiettivo di mostrare che i predittori, sebbene chiaramente inefficienti, come dimostrato dall'analisi del camminare casuale (primo capitolo) e dai test di Hurst (secondo capitolo) sono collegati da un meccanismo di causa ed effetto, che mostra l'esistenza di un'inefficienza di massa. L'idea \ue8 stata quella di analizzare se l'inefficienza dei predittori a lungo termine sia generata o meno da un meccanismo di causa ed effetto, che spinge le banche pi\uf9 importanti ad agire come leader del mercato, influenzando le scelte e le previsioni di tutti gli altri istituti finanziari

Complexity in Economic and Social Systems

There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure

Robust high dimensional m-test using regularized geometric median covariance

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Assessing for the volatility of the Saudi, Dubai and Kuwait stock markets: time series analysis (2005-2016)

The Kuwait, UAE and Saudi stock markets, alongside five specific firms from the latter, provided the indicator data for analysing market volatility. Autoregressive integrated moving average (ARIMA), Bayesian and Akaike analysis, Ljung-Box Q test, Partial autocorrelation function (PAC) and autocorrelation were the unit root tests applied, alongside matrix error, in this quantitative research. The study aims were to: assess weak-natured efficiency; contrast stock markets’ efficiency; explore market efficiency changes as time progresses. Statistics regarding matrices errors enabled variables influencing market efficiency to be established. Agreement on market efficiency has not been reached by researchers, with one shortcoming of EMH being lack of acknowledgement that an explored time frame may be characterised by varying efficiency levels. Consequently, the EMH and financial conduct have been subject to historiography, yet the connection between EMH and the financial behaviour model has not been the focus of studies using historical data. Accordingly, this research shortcoming tackled through this study, with the company-linked variables influencing stock market efficiency being identified through a literature review. Further, this study prospects identified, with the ongoing development of market efficiency and acceptance of inefficiency and efficiency’s simultaneous presence being the outcome. Finally, liberalisation, financial crises and reform in the Middle East and North Africa (MENA) region is a focus lacking in the extant research, with this study offering a further contribution in this regard. The study reveals that, with five companies and all countries characterised by market inefficiencies, which also changed as time progressed. Foremost efficiency characterised DSM, with SSM second, based on contrasting the obtained data’s random walk. The overall index had less efficiency than the specific firms. Concerning variables, SSM mark efficiency was not enhanced via crises or liberalisation, although it was by reform. Further, the research explains the results’ implications

Essays on Conditional Quantile Estimation and Equity Market Downside Risk

Fully aware of the importance of effective risk management, we develop the HYBRID-quantile model aimed at enhancing the accuracy of conditional quantile predictions. In the first essay, we validate that the model has a strong performance when applied to various GARCH-type processes. We use conditional asymmetry measures derived from the conditional quantile predictions to design portfolio allocation strategies. We identify two portfolios that could improve upon the risk-return trade-off of the benchmarks. In the second essay, we study the downside risk in the Chinese equity market. A wide range of investors, both domestic and foreign, have paid more attention to the Chinese stock market because of the growing significance of the Chinese economy. Downside risk has been a focal point, particularly considering the large price movements and the regulatory changes that took place over time. We use the 1% and 5% conditional quantiles of equity index returns to study the pattern of downside risk, and discover several break dates linked to major financial crises and trading reforms. Furthermore, our findings indicate that breaks in the B shares and the H shares downside risk tend to appear earlier than those corresponding to the A shares tails. Lastly, the revised Qualified Foreign Institutional Investor (QFII) program in 2006 and government share purchasing actions in 2015 have shown to be effective at alleviating downside risks in the Shanghai A shares. In the third essay, a joint work with Eric Ghysels and Steve Raymond, we examine granularity in the U.S. stock market. The U.S. equities market price process is largely driven by large institutional investors. We use quarterly 13-F holdings reported by institutional investors and focus on the Herfindahl-Hirschman Index (HHI) as the measure of granularity. We provide a comprehensive study of how granularity affects: (1) the cross-section of returns, (2) conditional variances across stocks and (3) downside risk. We find that constructing a low-HHI minus high-HHI portfolio produces an annualized return of 5.6%, and a 6.2% liquidity risk-adjusted return. We document the adverse impact that investor ownership concentration has on both conditional volatility, and critically, a robust set of downside risk measures at both the portfolio and the firm level.Doctor of Philosoph

The impact of intraday periodicity and news announcements on high-frequency stock volatility

High-frequency intraday financial data are commonly used in stock market volatility estimation and forecasting because they produce accurate results. However, little work to date has focused on the stylised facts of high-frequency returns, such as their tail properties, autocorrelations and leverage effects. One of the most discussed features of high-frequency returns is intraday periodicity, yet it is not well known how this feature operates in returns from data with different sampling schemes and frequencies. In addition, macroeconomic news announcements have been shown to have a large impact on first-moment and second-moment responses in financial markets. However, few existing models consider the effect of news on volatility estimation and forecasting, and those that do tend to treat it as a dummy variable, limiting its analytical power.  This thesis addresses these issues by reporting a study of the stylised facts of returns from S&P 500 stocks and the SPY index, and standardised returns from the latter, using various volatility measures in different financial regimes (i.e. before, during and after the 2008 financial crisis). It presents a comparison of the intraday patterns, jump frequencies, jump components and volatility forecasting of stock returns from calendar-time and business-time sampling schemes, as well as how these features are affected by intraday periodicity. It assesses the direct impact of macroeconomic news announcements on volatility estimation and forecasting for stock returns by incorporating significant news announcements as an index to identify the jumps caused by news in heterogeneous autoregressive (HAR) class models.  The results suggest that absolute intraday returns for high-frequency data exhibit autocorrelations and that aggregated returns display heavy tails. Standardising the returns of the SPY index using eleven different volatility measures produces distributions that are closer to a normal distribution. We find that various volatility measures are significantly correlated with trading volume, and hence that HAR-class models that include trading volume yield better volatility forecasting results than existing models. However, this effect may be limited to data from the relatively non-volatile pre-crisis and post-crisis periods. High-frequency returns based on business-time sampling have smaller jump frequencies, jump components and intraday periodicity patterns, than calendar-time data, which may be useful for volatility analysis. Intraday periodicity has a notable impact on jumps for both sampling schemes, however, and adjusting for intraday periodicity produces fewer jumps for all returns and smaller jump components for the majority. We also find that the forecasting results for less volatile data, such as healthcare stocks and data from the post-crisis period, improved after filtering for intraday periodicity. Finally, macroeconomic news announcements can affect jump components, and considering news outlets in HAR models can improve the forecasting results. The thesis thus contributes to our understanding of the factors affecting stock market volatility by providing evidence in support of including trading volume, efficient intraday periodicity estimators and news surprise in volatility estimation and forecasting models

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice