Author: Laura L.R. RifoPublisher:
ISBN:
Category :
Languages : en
Pages : 16
Book Description
Full Bayesian Analysis for Price Calculation in Jump-diffusion Models
Author: Laura L.R. RifoA Bayesian Analysis of Return Dynamics with Lévy Jumps
Author: Haitao LiPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
We have developed Bayesian Markov chain Monte Carlo (MCMC) methods for inferences of continuous-time models with stochastic volatility and infinite-activity Leacute;vy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Leacute;vy jumps, and (ii) the affine jump-diffusion (AJD) models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the AJD models fail to capture the ldquo;infinitely manyrdquo; small Leacute;vy jumps, which are too big for Brownian motion to model and too small for compound Poisson process to capture. Empirical studies show that infinite-activity Leacute;vy jumps are essential for modeling the Samp;P 500 index returns.
Bayesian Analysis of Demand Under Block Rate Pricing
Author: Koji MiyawakiPublisher: Springer Nature
ISBN: 9811518572
Category : Business & Economics
Languages : en
Pages : 120
Book Description
This book focuses on the structural analysis of demand under block rate pricing, a type of nonlinear pricing used mainly in public utility services. In this price system, consumers are presented with several unit prices, which makes a naive analysis biased. However, the response to the price schedule is often of interest in economics and plays an important role in policymaking. To address this issue, the book adopts a structural approach, referred to as the discrete/continuous choice approach in the literature, to develop corresponding statistical models for analysis. The resulting models are extensions of the Tobit model, a well-known statistical model in econometrics, and their hierarchical structure fits well in Bayesian methodology. Thus, the book takes the Bayesian approach and develops the Markov chain Monte Carlo method to conduct statistical inferences. The methodology derived is then applied to real-world datasets, microdata collected in Tokyo and the neighboring Chiba Prefecture, as a useful empirical analysis for prediction as well as policymaking.
Stochastic Models
Author: José González-BarriosPublisher: American Mathematical Soc.
ISBN: 0821834665
Category : Mathematics
Languages : en
Pages : 282
Book Description
The volume includes lecture notes and research papers by participants of the Seventh Symposium on Probability and Stochastic Processes held in Mexico City. The lecture notes introduce recent advances in stochastic calculus with respect to fractional Brownian motion, principles of large deviations and of minimum entropy concerning equilibrium prices in random economic systems, and give a complete and thorough survey of credit risk theory. The research papers cover areas such as financial markets, Gaussian processes, stochastic differential equations, stochastic integration, quantum dynamical semigroups, self-intersection local times, etc. Readers should have a basic background in probability theory, stochastic integration, and stochastic differential equations. The book is suitable for graduate students and research mathematicians interested in probability, stochastic processes, and risk theory.
Bayesian Analysis of Linear Models
Author: BroemelingPublisher: Routledge
ISBN: 1351464477
Category : Mathematics
Languages : en
Pages : 480
Book Description
With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.
Identifying Price Jumps from Daily Data with Bayesian Vs. Non-Parametric Methods
Author: Milan FičuraPublisher:
ISBN:
Category :
Languages : en
Pages : 30
Book Description
Non-parametric approach to financial time series jump estimation, using the L-Estimator, is compared with the parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle Filters to estimate the out-sample evolution of its latent state variables, such as the jump occurrences. The comparison is performed on simulated time series with different kinds of dynamics, including Poisson jumps, self-exciting Hawkes jumps with long-term clustering, as well as co-jumps. In addition to that, a comparison is performed on the real world daily time series of 4 major currency exchange rates. The results from the simulation study show that for the purposes of in-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at the times at which the jumps occur, does the parametric approach achieve a similar accuracy as the non-parametric one in the case of the simulations with Poisson jumps that are relatively large, and it outperforms the non-parametric approach in the case of Hawkes jumps when the jumps are large. On the other hand, the L-Estimator provides better results than the parametric approach in all of the cases when the simulated jumps are small (1% or less), regardless of the jump process dynamics. The application of the methods to foreign exchange rate time series further shows that the estimates of the parametric method may be biased in the case when large outlier jumps occur in the time series as well as when the stochastic volatility grows too high (as happened during the crisis). In both of these cases, the non-parametric L-Estimator based approach seems to provide more robust jump estimates, less influenced by the mentioned issues.
HJM Interest Rate Models with Fractional Brownian Motions
Author: Alberto OhashiPublisher:
ISBN:
Category : Brownian motion processes
Languages : en
Pages : 32
Book Description
A Note on the Averaging Approach for the Random Linear Transport Equation
Author: Fábio A. DoriniPublisher:
ISBN:
Category : Averaging method (Differential equations)
Languages : en
Pages : 18
Book Description
Analysis of Financial Time Series
Author: Ruey S. TsayPublisher: John Wiley & Sons
ISBN: 1118017099
Category : Mathematics
Languages : en
Pages : 724
Book Description
This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
Author: Fábio A. Dorini