Bayesian Methods in Finance - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Methods in Finance write by Svetlozar T. Rachev. This book was released on 2008-02-13. Bayesian Methods in Finance available in PDF, EPUB and Kindle. Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.
Bayesian Methods in Finance
Bayesian Methods in Finance - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Methods in Finance write by Svetlozar T. Rachev. This book was released on 2008-02-08. Bayesian Methods in Finance available in PDF, EPUB and Kindle. Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.
Bayesian Methods in Finance
Bayesian Methods in Finance - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Methods in Finance write by . This book was released on 2008. Bayesian Methods in Finance available in PDF, EPUB and Kindle. Provides an overview of the theory and practice of Bayesian methods in finance. This book explains and illustrates the foundations of the Bayesian methodology and provides a unified examination of the use of the Bayesian theory and practice to analyze and evaluate asset management.
Bayesian Methods for Hackers
Bayesian Methods for Hackers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Methods for Hackers write by Cameron Davidson-Pilon. This book was released on 2015-09-30. Bayesian Methods for Hackers available in PDF, EPUB and Kindle. Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
Coherent Stress Testing
Coherent Stress Testing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Coherent Stress Testing write by Riccardo Rebonato. This book was released on 2010-06-10. Coherent Stress Testing available in PDF, EPUB and Kindle. In Coherent Stress Testing: A Bayesian Approach, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit. Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches. The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.
