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Risk analysis in theory and practice
The objective of this book is to present this analytical framework and to illustrate how it can be used in the investigation of economic decisions under risk. In a sense, the economics of risk is a difficult subject: it involves understanding human decisions in the absence of perfect information. How do we make decisions when we do not know some of events affecting us? The complexities of our uncertain world and of how humans obtain and process information make this difficult. In spite of these difficulties, much progress has been made. First, probability theory is the corner stone of risk assessment. This allows us to measure risk in a fashion that can be communicated among decision makers or researchers. Second, risk preferences are now better understood. This provides useful insights into the economic rationality of decision making under uncertainty. Third, over the last decades, good insights have been developed about the value of information. This helps better understand the role of information in human decision making and this book provides a systematic treatment of these issues in the context of both private and public decisions under uncertainty. * Balanced treatment of conceptual models and applied analysis * Considers both private and public decisions under uncertainty * Website presents application exercises in EXCEL
eBook
Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives
Building upon the ideas introduced in their previous book, Derivatives in Financial Markets with Stochastic Volatility, the authors study the pricing and hedging of financial derivatives under stochastic volatility in equity, interest-rate, and credit markets. They present and analyze multiscale stochastic volatility models and asymptotic approximations. These can be used in equity markets, for instance, to link the prices of path-dependent exotic instruments to market implied volatilities. The methods are also used for interest rate and credit derivatives. Other applications considered include variance-reduction techniques, portfolio optimization, forward-looking estimation of CAPM 'beta', and the Heston model and generalizations of it. 'Off-the-shelf' formulas and calibration tools are provided to ease the transition for practitioners who adopt this new method. The attention to detail and explicit presentation make this also an excellent text for a graduate course in financial and applied mathematics.
eBook
Introduction to Computable General Equilibrium Models
Computable general equilibrium (CGE) models are widely used by governmental organizations and academic institutions to analyze the economy-wide effects of events such as climate change, tax policies and immigration. This book provides a practical, how-to guide to CGE models suitable for use at the undergraduate college level. Its introductory level distinguishes it from other available books and articles on CGE models. The book provides intuitive and graphical explanations of the economic theory that underlies a CGE model and includes many examples and hands-on modeling exercises. It may be used in courses on economics principles, microeconomics, macroeconomics, public finance, environmental economics and international trade and finance, because it shows students the role of theory in a realistic model of an economy. The book is also suitable for courses on general equilibrium models and research methods and for professionals interested in learning how to use CGE models.
eBook
The spatial spillover effects of green finance on ecological environment—empirical research based on spatial econometric model
Correct understanding of the positive role and mechanism of green finance in promoting ecological environment is an important premise and guarantee for promoting green finance to better serve the improvement of ecological environment. Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2009 to 2017, this paper constructs a spatial Dubin model based on the distance weight matrix and empirically analyzes the impact of green finance on the ecological environment and its spatial spillover effects. The empirical results show that (1) the development of green finance promotes the improvement of the ecological environment in this region and (2) the influence of green finance on the ecological environment has a significant positive spatial spillover effect, that is, the development of green finance in this region will promote the improvement of the ecological environment in the surrounding areas.
Journal Article
Anticipating correlations
Financial markets respond to information virtually instantaneously. Each new piece of information influences the prices of assets and their correlations with each other, and as the system rapidly changes, so too do correlation forecasts. This fast-evolving environment presents econometricians with the challenge of forecasting dynamic correlations, which are essential inputs to risk measurement, portfolio allocation, derivative pricing, and many other critical financial activities. In Anticipating Correlations, Nobel Prize-winning economist Robert Engle introduces an important new method for estimating correlations for large systems of assets: Dynamic Conditional Correlation (DCC). Engle demonstrates the role of correlations in financial decision making, and addresses the economic underpinnings and theoretical properties of correlations and their relation to other measures of dependence. He compares DCC with other correlation estimators such as historical correlation, exponential smoothing, and multivariate GARCH, and he presents a range of important applications of DCC. Engle presents the asymmetric model and illustrates it using a multicountry equity and bond return model. He introduces the new FACTOR DCC model that blends factor models with the DCC to produce a model with the best features of both, and illustrates it using an array of U.S. large-cap equities. Engle shows how overinvestment in collateralized debt obligations, or CDOs, lies at the heart of the subprime mortgage crisis--and how the correlation models in this book could have foreseen the risks. A technical chapter of econometric results also is included.
eBook
The impact of rapid urbanization on residential energy consumption in China
Due to the rapid progress of urbanization in China, the percentage of residential energy consumption out of total energy consumption has increased. This paper uses statistical data from 30 Chinese provinces (autonomous regions and municipalities) from 2000 to 2020 to analyze the impact of urbanization on residential energy consumption and construct an econometric model to test the mechanism. The empirical tests show that the consumption of direct energy (energy that exists in nature in its original form and has not been transformed) is positively U-shaped about the urbanization rate. Furthermore, the impact of economic development on direct and indirect energy consumption is significantly positive. In contrast, the effects of population agglomeration on immediate energy consumption are adverse, and the indirect energy consumption is positive.
Journal Article
Does finance affect environmental degradation: evidence from One Belt and One Road Initiative region?
This paper explores the effects of finance on environmental degradation and investigates environmental Kuznets curve (EKC) of each country among 52 that participate in the One Belt and One Road Initiative (OBORI) using the latest long panel data span (1980–2016). We utilized panel long run econometric models (fully modified ordinary least square and dynamic ordinary least square) to explore the long-run estimates in full panel and country level. Moreover, the Dumitrescu and Hurlin (2012) causality test is applied to examine the short-run causalities among our considered variables. The empirical findings validate the EKC hypothesis; the long-run estimates point out that finance significantly enhances the environmental degradation (negatively in few cases). The short-run heterogeneous causality confirms the bi-directional causality between finance and environmental degradation. The empirical outcomes suggest that policymakers should consider the environmental degradation issue caused by financial development in the One Belt and One Road region.
Journal Article
Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models
Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper makes a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions.
Journal Article
China’s experience in developing green finance to reduce carbon emissions: from spatial econometric model evidence
The objective of this study is to attempt to assess the effect of green finance in reducing carbon emissions in China, analyze the transformative role of policy impact in the development of green finance markets, and investigate the impact mechanisms of how green finance affects carbon dioxide emissions. Our time frame from 2007 to 2018 is selected for the empirical study by integrating the availability of data due to the scarcity of relevant statistics in the early days of green finance. Location of this study is in China where 30 provinces are included, excluding Tibet due to severe data shortage. As for methodology, we construct a green finance evaluation index system containing five indicators by entropy weight method, choose dynamic spatial Durbin model (DSDM) for empirical research, and perform mechanism analysis of restructuring industry and greening technology as intermediary channel. Our findings demonstrate that green finance in China does significantly reduce carbon emissions, and its spatial spillover effect and long-term effect are also verified. Furthermore, green finance tends to reduce CO
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emissions through restructuring industry and greening technology. Correspondingly, policy implications are recommended. First, improving green financial market and strengthening information disclosure of green financial market are crucial to facilitate green finance development. Local governments formulate carbon emission reduction strategies focusing on space by joint conference or coordination mechanism like river head system. Lastly, a mechanism should be developed to strengthen the transformation of industrial structure and to promote greening technology.
Journal Article