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| Author | : Alok Gupta |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
| Author | : Alok Gupta |
| Publisher | : |
| Total Pages | : |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : 329 |
| Release | : 2008 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : 9781119202141 |
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.
| Author | : Charalambos G. Tsangarides |
| Publisher | : |
| Total Pages | : 28 |
| Release | : 2004 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
| Author | : Matt Sekerke |
| Publisher | : John Wiley & Sons |
| Total Pages | : 238 |
| Release | : 2015-08-19 |
| Genre | : Business & Economics |
| ISBN | : 1118747453 |
A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.
| Author | : P. K. Goel |
| Publisher | : North Holland |
| Total Pages | : 512 |
| Release | : 1986 |
| Genre | : Business & Economics |
| ISBN | : |
The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians from several countries. Part I of this book relates most directly to de Finetti's interests whilst Part II deals specifically with the implications of the assumption of finitely additive probability. Parts III & IV discuss applications of Bayesian methodology in econometrics and economic forecasting, and Part V examines assessment of prior parameters in specific parametric setting and foundational issues in probability assessment. The following section deals with state of the art for comparing probability functions and gives an assessment of prior distributions and utility functions. In Parts VII & VIII are a collection of papers on Bayesian methodology for general linear models and time series analysis (the most often used tools in economic modelling), and papers relevant to modelling and forecasting. The remaining two Parts examine, respectively, optimality considerations and the effectiveness of the Conditionality-Likelihood Principle as a vehicle to convince the non-Bayesians about the usefulness of the Bayesian paradigm.
| Author | : Riccardo Rebonato |
| Publisher | : John Wiley & Sons |
| Total Pages | : 269 |
| Release | : 2010-06-10 |
| Genre | : Business & Economics |
| ISBN | : 0470971487 |
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.
| Author | : Kai Yin |
| Publisher | : |
| Total Pages | : 145 |
| Release | : 2021 |
| Genre | : Mathematics |
| ISBN | : |
A Bayesian approach is used to calibrate financial volatility and disease transmission models. The Bayesian approach can incorporate heterogeneous information through a hierarchical structure and provides a natural mechanism for regularization in the form of prior distributions. It also provides a quantitative assessment of uncertainties for the model input parameters via a posterior probability distribution. A hierarchical Bayes model is used to fuse asset price data in the physical measure and derivative price data in the risk-neutral measure to reduce uncertainties in the volatility estimation. The Karhunen-Lo\`eve expansion is used for dimension reduction of the unknown volatility functionals in the context of stochastic and local volatility models. The forward derivative pricing models are non-linear; hence, the Bayesian inference is based on Markov Chain Monte Carlo (MCMC) samples from the posterior distribution. The need for multiple evaluations of the forward model and the high dimensionality of the posteriors result in many computation challenges in the MCMC sampling. A two-stage adaptive Metropolis algorithm is used where the bad proposals are screened in the first inexpensive stage, and the proposals are drawn adaptively using the past samples, which results in faster convergence and mixing of the chain. A retrospective study of the COVID-19 transmission dynamics in Indian states is conducted by using a modified population-based SEIR model that incorporates the mobility data, testing data, and public behavior factors. A fully Bayesian method is used to calibrate the proposed model with reported epidemic data on daily cases, deaths, and recoveries. The calibrated model is used to estimate undetected cases and study the effects of different initial non-pharmaceutical intervention strategies.
| Author | : Richard Michael Cyert |
| Publisher | : Springer |
| Total Pages | : 206 |
| Release | : 2011-10-12 |
| Genre | : Business & Economics |
| ISBN | : 9789401079228 |
We began this research with the objective of applying Bayesian methods of analysis to various aspects of economic theory. We were attracted to the Bayesian approach because it seemed the best analytic framework available for dealing with decision making under uncertainty, and the research presented in this book has only served to strengthen our belief in the appropriateness and usefulness of this methodology. More specif ically, we believe that the concept of organizational learning is funda mental to decision making under uncertainty in economics and that the Bayesian framework is the most appropriate for developing that concept. The central and unifying theme of this book is decision making under uncertainty in microeconomic theory. Our fundamental aim is to explore the ways in which firms and households make decisions and to develop models that have a strong empirical connection. Thus, we have attempted to contribute to economic theory by formalizing models of the actual pro cess of decision making under uncertainty. Bayesian methodology pro vides the appropriate vehicle for this formalization.
| Author | : Stephen Jeffery Brown |
| Publisher | : |
| Total Pages | : 422 |
| Release | : 1976 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
