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Empirical Finance
There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.
The Business Cycle in Post-war Japan
Business Cycle in Post-War Japan
An Empirical Investigation of Stock Markets
An Empirical Investigation of Stock Markets: The CCF Approach attempts to make an empirical contribution to the literature on the movements of stock prices in major economies, i.e. Germany, Japan, the UK and the USA. Specifically, the cross-correlation function (CCF) approach is used to analyze the stock market. This volume provides some empirical evidence regarding the economic linkages among a group of different countries. Chapter 2 and Chapter 3 analyze the international linkage of stock prices among Germany, Japan, the UK and the USA. Chapter 2 applies the standard approach, whereas Chapter 3 uses the CCF approach. Chapter 4 analyzes the relationship between stock prices and exchange rates. Chapter 5 analyzes the relationship among stock prices, exchange rates, and real economic activities. Chapter 6 summarizes the main results obtained in each chapter and comments on the possible directions of future research.
Empirical Techniques in Finance
Includes traditional elements of financial econometrics but is not yet another volume in econometrics. Discusses statistical and probability techniques commonly used in quantitative finance. The reader will be able to explore more complex structures without getting inundated with the underlying mathematics.
ESG Investment in the Global Economy
This book introduces environmental, social and governance (ESG) investment and clarifies its characteristics as financial securities. It is forecasted that companies’ ESG information will be reflected in their corporate value as much as their financial information is in the future. The special feature of this book is to reveal the characteristics and impact of ESG investment using various quantitative analyses (e.g. EGARCH, asymmetric DCC, copula, VaR, connectedness, dynamic spillover). This book focuses on the relationship between some ESG indexes and the other economic variables, particularly in light of the recent economic environment (e.g. Global Financial Crisis, COIVID-19 pandemic, crude oil price crash). Readers can grasp a larger picture of ESG investment through a survey of its history and current status, predictions of its future, and interpretation of various empirical analysis results.
The European Sovereign Debt Crisis and Its Impacts on Financial Markets
The global financial crisis saw many Eurozone countries bearing excessive public debt. This led the government bond yields of some peripheral countries to rise sharply, resulting in the outbreak of the European sovereign debt crisis. The debt crisis is characterized by its immediate spread from Greece, the country of origin, to its neighbouring countries and the connection between the Eurozone banking sector and the public sector debt. Addressing these interesting features, this book sheds light on the impacts of the crisis on various financial markets in Europe. This book is among the first to conduct a thorough empirical analysis of the European sovereign debt crisis. It analyses, using advanced econometric methodologies, why the crisis escalated so prominently, having significant impacts on a wide range of financial markets, and was not just limited to government bond markets. The book also allows one to understand the consequences and the overall impact of such a debt crisis, enabling investors and policymakers to formulate diversification strategies, and create suitable regulatory frameworks.
AI and Financial Markets
Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.
Credit Default Swap Markets in the Global Economy
This book provides a comprehensive overview for various segments of the global credit default swap (CDS) markets, touching upon how they were affected by the recent financial turmoil. The book uses empirical analysis on credit default swap markets, applying advanced econometric methodologies to the time series data. It covers not only well-studied sovereign credit default swap markets but also sector credit default swap indices (i.e., CDS index for the banking sector) and corporate credit default swap indices (i.e., Markit iTraxx Japan CDS index), which have not been fully examined by the previous literature. The book also investigates causality and co-movement among several credit default swap markets, or between CDS and other financial markets.
