A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

A time series contains the values of a dataset sampled at different points in time. Some examples in financial research include asset prices, volatility indices, inflation rates, revenues, and so on. This chapter briefly covers the basic methods used in time-series analysis. Issues include whether the time-series data have equally spaced intervals, whether there is noise or error, how quickly the series grows, and whether the series has missing values. The chapter begins by testing for autocorrelation and remedies for autocorrelation. It then presents some standard tests for stationarity and cointegration, briefly covering random walks and the unit-root test. The models covered, among others, include autoregressive distributed lag (ARDL), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH), and vector autoregressive (VAR) models. The chapter provides an application to mortgage rates and ends with lab work and a mini case study.

Nurses and other health care professionals play a vital role in providing equitable, collaborative health care in the community. A primary health care approach is underpinned by the social model of health care and examines how social, environmental, economic and political factors affect the health of individuals, families and communities. An Introduction to Community and Primary Health Care provides a comprehensive and practical explanation of the fundamentals of this approach, preparing learners for professional practice in Australia and Aotearoa New Zealand.

The fourth edition has been restructured into four parts covering theory, working with diverse communities, key skills for practice, and the professional roles that nurses and other health care practitioners can play in primary care and community health practice. Each chapter has been thoroughly revised to reflect the latest research and includes up-to-date case studies, reflection questions and critical thinking activities to strengthen students’ knowledge and analytical skills. A new postface reflects on the future directions of primary health care.

Written by an expert team of nurse authors with experience across a broad spectrum of professional roles, An Introduction to Community and Primary Health Care remains an indispensable resource for students of nursing and other health care professions.

A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

In regressions where the dependent variable takes limited values such as 0 and 1, or takes some category values, using the OLS estimation method will likely provide biased and inconsistent results. Because the dependent variable is either discontinuous or its range is bounded, one of the assumptions of the CLRM is violated (that the standard error is normally distributed conditional on the independent variables). This chapter focuses on limited dependent-variable models, for example, covering firm decision-making, capital structure decisions, investor decision-making, and so on. The chapter presents and discusses the linear probability model, maximum-likelihood estimator, probit model, logit model, ordered probit and logit models, multinomial logit model, conditional logit model, tobit model, Heckman selection model, and count data models. It covers the assumptions behind and applications of these models. As usual, the chapter provides an application of selected limited dependent-variable models, lab work, and a mini case study.

A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

Corporate finance research requires close consideration of the assumptions underlying the econometric models applied to test hypotheses. This is partly because, as the field has evolved, more complex relationships have been examined, some of which pose problems of endogeneity. Endogeneity is one problem that violates the assumptions of the CLRM. It is so central that this book devotes a whole chapter to discussing it. The chapter covers the sources of endogeneity bias and the most commonly used methods that can be applied to cross-sectional data to deal with the endogeneity problem in corporate finance. These methods cover two-stage least squares (so-called IV approach), treatment effects, matching techniques, and regression discontinuity design (RDD). An application is provided for an IV approach and an RDD approach. The chapter ends with an application of the most common methods to real data, lab work, and a mini case study.

We introduce the concepts of a map, a sigma-field generated by a map and the measurability of a map. This leads to the notion of random variable on a probability space, which is just a map being measurable with respect to the considered sigma-field. This guarantees that the distribution of a random variable is well-defined on a given probability space. Next, we review the main families of random variables, discrete (uniform, Bernoulli, binomial) and continuous (uniform, exponential, normal, log-normal), and recall their mass and density functions, as well as their cumulative distribution functions. In particular, we highlight that any random variable can be built by transforming a continuous uniform random variable in an appropriate manner, following the probability integral transform. Finally, we introduce random vectors (vector of random variables), joint and marginal distributions, and the independence property. We illustrate those concepts on toy examples as well as on our stock price model, computing the distribution of prices at various points in time. We explain how correlation can significantly impact the risk of a portfolio of stocks in simple discrete models.

This chapter extends the results found in the Cox–Ross–Rubinstein model (where the stock price is modeled as the exponential of a scaled random walk) to the Black–Scholes–Merton model (where the stock price is modeled as the exponential of a Brownian motion). We argue that, because the risk-neutral approach works when considering any time-step for the scaled random walk, it should also work in the limit where the time-step tends to zero; that is, when the stock price follows a geometric Brownian motion. Therefore, we apply the result of Chapter 9 and compute the price of the product as the risk-neutral expectation of the discounted payoff. The distribution of the stock price under the risk-neutral measure is recovered from the martingale property of the discounted stock price combined with Girsanov’s theorem. This yields a first expression for the fair price of derivative products in continuous time, without having to rely on stochastic calculus.

This chapter studies when the price found using the risk-neutral expectation approach coincides with the no-arbitrage price of the derivative, interpreted as the cost of launching a strategy replicating the product’s payoff. To this end, we define rigorously the concepts of payoff, arbitrage, self-financing strategy, and market model featuring multiple risky assets. A market model is arbitrage-free if it is impossible to find an arbitrage opportunity by trading in the primary assets at the prevailing prices. It is complete if every payoff can be replicated. In general, sophisticated models need to be arbitrage-free but are incomplete. How can we avoid arbitrage if we are unable to replicate the payoff by trading in the primary assets? And how can we determine whether a model is arbitrage-free or complete, given that one cannot reasonably review all the possible strategies to check whether they generate an arbitrage opportunity or not? The answer is provided by the fundamental theorems of asset pricing based on whether the model admits no, one, or multiple risk-neutral measures. This amazing result emphasizes how powerful mathematical modeling can be in finance.

Data management concerns collecting, processing, analyzing, organizing, storing, and maintaining the data you collect for a research design. The focus in this chapter is on learning how to use Stata and apply data-management techniques to a provided dataset. No previous knowledge is required for the applications. The chapter goes through the basic operations for data management, including missing-value analysis and outlier analysis. It then covers descriptive statistics (univariate analysis) and bivariate analysis. Finally, it ends by discussing how to merge and append datasets. This chapter is important to proceed with the applications, lab work, and mini case studies in the following chapters, since it is a means to become familiar with the software. Stata codes are provided in the main text. For those who are interested in using Python or R instead, the corresponding code is provided on the online resources page (www.cambridge.org/mavruk).

Good nursing practice is based on evidence, and undertaking a community health needs assessment is a means of providing evidence to guide community nursing practice. A community health needs assessment is a process that examines the health status and social needs of a particular population. It may be conducted at a whole-of-community level, a sub-community level or even a subsystem level. Nursing practice frequently involves gathering data and assessing individuals or families to determine appropriate nursing interventions. This concept is transferable to an identified community when the community itself is viewed as the client.

A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

Sex and gender have a significant relationship to health and health outcomes for women, men, and sexually and gender-diverse people. Sex relates to biological attributes, whether born female or male, while gender identity relates to how someone feels and experiences their gender, which may or may not be different to their physiology or sex at birth. Biological characteristics expose women and men to different health risks and health conditions. Gender also exposes people to different health risks, and gender inequity impacts on their potential to achieve health and well-being.