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Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model
This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.
Growth Determinants Revisited
This paper revisits the cross-country growth empirics debate using a novel Limited Information Bayesian Model Averaging framework to address model uncertainty in the context of a dynamic growth model in panel data with endogenous regressors. Our empirical findings suggest that once model uncertainty is accounted for there is strong evidence that initial income, investment, life expectancy, and population growth are robustly correlated with economic growth. We also find evidence that debt, openness, and inflation are robust growth determinants. Overall, the set of our robust growth determinants differs from those identified by other studies that incorporate model uncertainty, but ignore dynamics and/or endogeneity. This underscores the importance of accounting for model uncertainty and endogeneity in the investigation of growth determinants.
A Primer on Managing Sovereign Debt-Portfolio Risks
This paper provides an overview of sovereign debt portfolio risks and discusses various liability management operations (LMOs) and instruments used by public debt managers to mitigate these risks. Debt management strategies analyzed in the context of helping reach debt portfolio targets and attain desired portfolio structures. Also, the paper outlines how LMOs could be integrated into a debt management strategy and serve as policy tools to reduce potential debt portfolio vulnerabilities. Further, the paper presents operational issues faced by debt managers, including the need to develop a risk management framework, interactions of debt management with fiscal policy, monetary policy, and financial stability, as well as efficient government bond markets.
Growth Slowdowns and the Middle-Income Trap
The “middle-income trap” is the phenomenon of hitherto rapidly growing economies stagnating at middle-income levels and failing to graduate into the ranks of high-income countries. In this study we examine the middle-income trap as a special case of growth slowdowns, which are identified as large sudden and sustained deviations from the growth path predicted by a basic conditional convergence framework. We then examine their determinants by means of probit regressions, looking into the role of institutions, demography, infrastructure, the macroeconomic environment, output structure and trade structure. Two variants of Bayesian Model Averaging are used as robustness checks. The results—including some that indeed speak to the special status of middle-income countries—are then used to derive policy implications, with a particular focus on Asian economies.
Gaussian Markov Random Fields
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
A Guide to IMF Stress Testing
The IMF has had extensive involvement in the stress testing of financial systems in its member countries. This book presents the methods and models that have been developed by IMF staff over the years and that can be applied to the gamut of financial systems. An added resource for readers is the companion CD-Rom, which makes available the toolkit with some of the models presented in the book (also located at elibrary.imf.org/page/stress-test-toolkit).
A Primer on Managing Sovereign Debt-Portfolio Risks
This paper provides an overview of sovereign debt portfolio risks and discusses various liability management operations (LMOs) and instruments used by public debt managers to mitigate these risks. Debt management strategies analyzed in the context of helping reach debt portfolio targets and attain desired portfolio structures. Also, the paper outlines how LMOs could be integrated into a debt management strategy and serve as policy tools to reduce potential debt portfolio vulnerabilities. Further, the paper presents operational issues faced by debt managers, including the need to develop a risk management framework, interactions of debt management with fiscal policy, monetary policy, and financial stability, as well as efficient government bond markets.
FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financi...
Understanding Intermolecular Interactions in the Solid State
An overview of the latest techniques for studying intermolecular interactions in crystalline matter.
