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"Presents inference and simulation of stochastic process in the field of model calibration for financial times series modeled with continuous time processes and numerical option pricing. Introduces the basis of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them and covers option pricing with one or more underlying assets based on these models. Analysis and implementation of models based on switching models or models with jumps are featured along with new models (Levy and telegraph process modeling) and topics such as; volatilty, covariation, p-variation and regime switching analysis, attention is focused on the calibration of these topics from a statistical viewpoint. The book features problems with solutions and examples. All the examples and R code are available as an additional R package, therefore all the examples can be reproduced"--Provided by publisher.

Options (Finance) --- Probabilities --- Stochastic processes --- Time-series analysis --- 332.6453 --- 305.91 --- AA / International- internationaal --- Analysis of time series --- Random processes --- Probability --- Statistical inference --- Call options --- Calls (Finance) --- Listed options --- Options exchange --- Options market --- Options trading --- Put and call transactions --- Put options --- Puts (Finance) --- Prices --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles --- Actuarial mathematics --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Combinations --- Mathematics --- Chance --- Least squares --- Risk --- Derivative securities --- Investments

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The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA  package, already available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page. Contains both theory and code with step-by-step examples and figures Uses YUIMA package to implement the latest techniques available in the literature of inference for stochastic processes Shows how to create the description of very abstract models in the same way they are described in theoretical papers but with an extremely easy interface Stefano M. Iacus, PhD, is full professor of statistics the Department of Economics, Management and Quantitative Methods at the University of Milan. He has been a member of the R Core Team (1999-2014) for the development of the R statistical environment and now member of the R Foundation. His research interests include inference for stochastic processes, simulation, computational statistics, causal inference, text mining, and sentiment analysis.  Nakahiro Yoshida, PhD, is a professor at the Graduate School of Mathematical Sciences, University of Tokyo. He is working in theoretical statistics, probability theory, computational statistics, and financial data analysis. He was awarded the Japan Statistical Society Award in 2009 and the Analysis Prize from the Mathematical Society of Japan in 2006.

Statistics. --- Mathematical statistics. --- Probabilities. --- Statistics and Computing/Statistics Programs. --- Probability and Statistics in Computer Science. --- Probability Theory and Stochastic Processes. --- Computer science. --- Distribution (Probability theory. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Informatics --- Science --- Statistical methods --- Stochastic processes. --- Stochastic processes --- R (Computer program language) --- Data processing. --- GNU-S (Computer program language) --- Domain-specific programming languages --- Random processes --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- R (Computer program language). --- Computer science --- Statistics and Computing. --- Probability Theory. --- Mathematics. --- Computer mathematics --- Electronic data processing

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This book is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book will be useful to practitioners and students with only a minimal mathematical background because of the many R programs, and to more mathematically-educated practitioners. Many of the methods presented in the book have not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called "sde" provides functions with easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations. The book is organized into four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other publications. The third one focuses on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments, and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader who is not an expert in the R language will find a concise introduction to this environment focused on the subject of the book. A documentation page is available at the end of the book for each R function presented in the book. Stefano M. Iacus is associate professor of Probability and Mathematical Statistics at the University of Milan, Department of Economics, Business and Statistics. He has a PhD in Statistics at Padua University, Italy and in Mathematics at Université du Maine, France. He is a member of the R Core team for the development of the R statistical environment, Data Base manager for the Current Index to Statistics, and IMS Group Manager for the Institute of Mathematical Statistics. He has been associate editor of the Journal of Statistical Software.

Stochastic processes --- Statistics. --- Signal, Image and Speech Processing. --- Computational Mathematics and Numerical Analysis. --- Quantitative Finance. --- Simulation and Modeling. --- Econometrics. --- Statistics and Computing/Statistics Programs. --- Computer simulation. --- Finance. --- Computer science --- Mathematical statistics. --- Statistique --- Simulation par ordinateur --- Finances --- Informatique --- Statistique mathématique --- Econométrie --- Mathematics. --- Mathématiques --- 517.96 --- 519.216 --- Finite differences. Functional and integral equations --- Stochastic processes in general. Prediction theory. Stopping times. Martingales --- Equations. --- Stochastic differential equations. --- Stochastic differential equations --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- 519.216 Stochastic processes in general. Prediction theory. Stopping times. Martingales --- 517.96 Finite differences. Functional and integral equations --- Ergodic theory. --- Ergodic transformations --- Mathematical analysis. --- Analysis (Mathematics). --- Economics, Mathematical. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Analysis. --- Differential equations --- Fokker-Planck equation --- Continuous groups --- Mathematical physics --- Measure theory --- Transformations (Mathematics) --- Distribution (Probability theory. --- Global analysis (Mathematics). --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Economics, Mathematical --- Statistics --- Funding --- Funds --- Economics --- Currency question --- Analysis, Global (Mathematics) --- Differential topology --- Functions of complex variables --- Geometry, Algebraic --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistical inference --- Statistics, Mathematical --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Econometrics --- 517.1 Mathematical analysis --- Mathematical analysis --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Statistical analysis --- Statistical data --- Statistical science --- Methodology --- R (Computer program language). --- GNU-S (Computer program language) --- Domain-specific programming languages --- Social sciences --- Statistics and Computing. --- Probability Theory. --- Mathematics in Business, Economics and Finance. --- Computer Modelling. --- Data processing.

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Due miliardi e mezzo di utenti internet, oltre un miliardo di account Facebook, 550 milioni di profili Twitter. Che parlano, discutono, si confrontano sui temi più svariati. Un flusso in continuo divenire di informazioni che dà sostanza ogni giorno al mondo dei Big Data. Ma come si analizza concretamente il “sentiment” della Rete? Quali sono i pregi e i limiti dei diversi metodi esistenti? E a quali domande possiamo dare una risposta? Dopo aver presentato le varie tecniche di analisi testuale applicate ai social media, questo libro discute di come l’informazione presente in Rete sia in grado di aiutarci a meglio comprendere il presente e a fare previsioni sul futuro riguardo a una molteplicità di fenomeni sociali, che spaziano dall’andamento dei mercati finanziari, alla diffusione di malattie, alle rivolte e ai sommovimenti popolari fino ai risultati dei talent show, prima di concentrarsi su due casi specifici: l’andamento della felicità degli italiani giorno per giorno, e i risultati delle campagne elettorali in Francia, Stati Uniti e Italia tra il 2012 e il 2013.

Social sciences (general) --- Statistical science --- Information systems --- Lexicology. Semantics --- semantiek --- sociale media --- Facebook --- big data --- sociale wetenschappen --- statistiek --- database management --- data acquisition