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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"--

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- 1. Subjective and Social Well-Being -- 1.1. Introduction -- 1.1.1. Subjective Well-Being -- 1.1.2. Objective Measures -- 1.1.3. Multidimensional Indicators -- 1.1.4. Surveys -- 1.1.5. Social Networking Sites and Data at Scale -- 1.1.6. What You'll Find (and What You'll Not) in This Book -- 1.1.7. Wellbeing, Well Being or Well-Being? -- 1.2. Gross Domestic Product -- 1.3. Well-Being as a Multidimensional Notion -- 1.3.1. The Capability Approach -- 1.3.1.1. Empirical Limitations of the Capability Approach -- 1.3.2. Multidimensional Well-Being Indicators -- 1.3.2.1. HDI: Human Development Index -- 1.3.2.2. BLI: Better Life Index -- 1.3.2.3. HPI: Happy Planet Index -- 1.3.2.4. BES: Benessere Equo Sostenibile (Fair Sustainable Well-Being) -- 1.3.2.5. CIW: Canadian Index of Well-Being -- 1.3.2.6. Other Initiatives for Measuring Well-Being -- 1.3.2.7. GNH: Gross National Happiness -- 1.3.2.8. Pros and Cons of Multidimensional Indicators -- 1.4. Self-Reported Well-Being -- 1.4.1. Gallup Surveys -- 1.4.1.1. Gallup World Poll -- 1.4.1.2. Gallup-Sharecare and Global Well-Being Index -- 1.4.1.3. Well-Being Research Based on Gallup Data -- 1.4.2. European Social Survey -- 1.4.3. World Values Survey -- 1.4.4. European Quality of Life Survey -- 1.4.5. How to Collect (and Interpret) Self-Reported Evaluations -- 1.5. Social Networking Sites and Well-Being -- 1.5.1. Sentiment Analysis -- 1.5.2. Evaluating Subjective Well-Being on the Web -- 1.5.3. Pros and Cons of Large-Scale Data from SNS -- 1.5.4. International and Intercultural Comparisons -- 1.6. Subjective or Social Well-Being? -- 1.7. Glossary -- 2. Text and Sentiment Analysis -- 2.1. Text Analysis -- 2.1.1. Main Principles of Text Analysis -- 2.2. Different Types of Estimation and Targets.

In this paper, we review recent advances on change point estimation for the volatility component of stochastic differential equations under different discrete sampling schemes. We consider both ergodic and non‐ergodic cases, and present a Monte Carlo study on the change point estimator to compare the three methods under different setups.

1. Social media electoral forecasts : an overview -- 2. From noise to signal in sentiment and opinion analysis -- 3. Nowcasting and forecasting the campaign : evidence from France, the United States, and Italy -- 4. Leaders, promises and negative campaigning : digging into an electoral campaign through social media -- 5. Social media and electoral forecasts : sources of bias and meta-analysis -- 6. Conclusion : "To predict or not to predict?" : future avenues of social media research within and beyond electoral forecasts.

"The importance of social media as a way to monitor an electoral campaign is well established. Day-by-day, hour-by-hour evaluation of the evolution of online ideas and opinion allows observers and scholars to monitor trends and momentum in public opinion well before traditional polls. However, there are difficulties in recording and analyzing often brief, unverified comments while the unequal age, gender, social and racial representation among social media users can produce inaccurate forecasts of final polls. Reviewing the different techniques employed using social media to nowcast and forecast elections, this book assesses its achievements and limitations while presenting a new technique of 'sentiment analysis' to improve upon them. The authors carry out a meta-analysis of the existing literature to show the conditions under which social media-based electoral forecasts prove most accurate while new case studies from France, the United States and Italy demonstrate how much more accurate 'sentiment analysis' can prove."--

Researchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties these theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. Properties of estimators based on this theory are much easier to understand and can be satisfied without the unattractive properties of existing theories, such as assumptions hidden in data analyses rather than stated up front, asymptotics, unfamiliar estimators, and complex variance calculations. Our theory of inference makes it possible for researchers to treat matching as a simple form of preprocessing to reduce model dependence, after which all the familiar inferential techniques and uncertainty calculations can be applied. This theory also allows binary, multicategory, and continuous treatment variables from the outset and straightforward extensions for imperfect treatment assignment and different versions of treatments.

We discuss a method for improving causal inferences called "Coarsened Exact Matching" (CEM), and the new "Monotonic Imbalance Bounding" (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software forR, Stata, andSPSSthat implement all our suggestions.

The growing usage of social media by a wider audience of citizens sharply increases the possibility of investigating the web as a device to explore and track political preferences. In the present paper we apply a method recently proposed by other social scientists to three different scenarios, by analyzing on one side the online popularity of Italian political leaders throughout 2011, and on the other the voting intention of French Internet users in both the 2012 presidential ballot and the subsequent legislative election. While Internet users are not necessarily representative of the whole population of a country's citizens, our analysis shows a remarkable ability for social media to forecast electoral results, as well as a noteworthy correlation between social media and the results of traditional mass surveys. We also illustrate that the predictive ability of social media analysis strengthens as the number of citizens expressing their opinion online increases, provided that the citizens act consistently on these opinions.