Download or read book Optimal Monetary Policy Using Reinforcement Learning written by Natascha Hinterlang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Monetary Policy when Agents are Learning written by Krisztina Molnár and published by . This book was released on 2007 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Monetary Policy Under Bounded Rationality written by Jonathan Benchimol and published by International Monetary Fund. This book was released on 2019-08-02 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: The form of bounded rationality characterizing the representative agent is key in the choice of the optimal monetary policy regime. While inflation targeting prevails for myopia that distorts agents' inflation expectations, price level targeting emerges as the optimal policy under myopia regarding the output gap, revenue, or interest rate. To the extent that bygones are not bygones under price level targeting, rational inflation expectations is a minimal condition for optimality in a behavioral world. Instrument rules implementation of this optimal policy is shown to be infeasible, questioning the ability of simple rules à la Taylor (1993) to assist the conduct of monetary policy. Bounded rationality is not necessarily associated with welfare losses.

Download or read book Optimal Monetary Policy with Overlapping Generations of Policymakers written by Maral Shamloo and published by International Monetary Fund. This book was released on 2010-02-01 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper I study the effect of imperfect central bank commitment on inflationary outcomes. I present a model in which the monetary authority is a committee that consists of members who serve overlapping, finite terms. Older and younger generations of Monetary Policy Committee (MPC) members decide on policy by engaging in a bargaining process. I show that this setup gives rise to a continuous measure of the degree of monetary authority's commitment. The model suggests that the lower the churning rate or the longer the tenure time, the closer social welfare will be to that under optimal commitment policy.

Download or read book Optimal Monetary Policy Rules written by Anna Bogomolova and published by . This book was released on 2008 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Download or read book Deep Reinforcement Learning Emerging Trends in Macroeconomics and Future Prospects written by Tohid Atashbar and published by International Monetary Fund. This book was released on 2022-12-16 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.

Download or read book Learning and Optimal Monetary Policy written by Richard Dennis and published by . This book was released on 2007 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: To conduct policy efficiently, central banks must use available data to infer, or learn, the relevant structural relationships in the economy. However, because a central bank's policy affects economic outcomes, the chosen policy may help or hinder its efforts to learn. This paper examines whether real-time learning allows a central bank to learn the economy's underlying structure and studies the impact that learning has on the performance of optimal policies under a variety of learning environments. Our main results are as follows. First, when monetary policy is formulated as an optimal discretionary targeting rule, we find that the rational expectations equilibrium and the optimal policy are real-time learnable. This result is robust to a range of assumptions concerning private sector learning behavior. Second, when policy is set with discretion, learning can lead to outcomes that are better than if the model parameters are known. Finally, if the private sector is learning, then unannounced changes to the policy regime, particularly changes to the inflation target, can raise policy loss considerably.

Download or read book Foundations of Reinforcement Learning with Applications in Finance written by Ashwin Rao and published by CRC Press. This book was released on 2022-12-16 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book

Download or read book Optimal Monetary Policy with R written by Roberto M. Billi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Two sided Learning and Optimal Monetary Policy in an Open Economy Model written by Timothy C. Y. Kam and published by . This book was released on 2004 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Two sided learning and optimal monetary policy in an open economy model written by Timothy Kam and published by . This book was released on 2004 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book AI and Macroeconomic Modeling Deep Reinforcement Learning in an RBC Model written by Tohid Atashbar and published by International Monetary Fund. This book was released on 2023-02-24 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, one of which is deterministic without the technological shock and the other is stochastic. The objective of the deterministic environment is to compare the learning agent's behavior to a deterministic steady-state scenario. We demonstrate that in both deterministic and stochastic scenarios, the agent's choices are close to their optimal value. We also present cases of unstable learning behaviours. This AI-macro model may be enhanced in future research by adding additional variables or sectors to the model or by incorporating different DRL algorithms.

Download or read book Optimal Monetary Policy in a Micro founded Model with Parameter Uncertainty written by Takeshi Kimura and published by . This book was released on 2003 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Learning Persistence and Optimal Monetary Policy written by Vítor Gaspar and published by . This book was released on 2006 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Learning Pesistence and Optimal Monetary Policy written by Vítor Gaspar and published by . This book was released on 2006 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Monetary Policy with Uncertainty written by Roger Craine and published by . This book was released on 1977 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: