Insects are the most abundant and diverse group of animals on Earth. They are critical to ecosystem function in terrestrial and aquatic systems, yet they are one of the most understudied groups of organisms. Only a small proportion of the more than five million insect species have been assessed by the IUCN Red List. For most of these species, there is not enough evidence to know what is happening to their populations. In fact, for most insect species globally, there is very little data available on where they live, how they live and what environmental conditions they need to persist in the long term. A number of threats affect insect biology and life cycles generally, including climate change, habitat clearing, invasive species, use of broad-spectrum pesticides, and pollution of soil and waterways. These threats should be addressed immediately to prevent further declines in insect populations. To understand insects better, greater investment in research and documentation of the world’s insect diversity is urgently needed.

In this chapter, we study risks associated with movements of interest rates in financial markets. We begin with a brief discussion of the term structure of interest rates. We then discuss commonly used interest rate sensitive securities. This is followed by the study of different measures of sensitivity to interest rates, including duration and convexity. We consider mitigating interest rate risk through hedging and immunization. Finally, we take a more in-depth look at the drivers of interest rate term structure dynamics.

In this chapter, we present the frequency-severity model, which is implicit in common risk calculations used in practice. In this model, the total loss from a risk, or set of risks, is treated as a random sum of random, identically distributed individual losses. If the frequency and severity random variables are independent, then the mean and variance of the aggregate loss can easily be calculated from the moments of the frequency and severity distributions. However, numerical methods are usually required for other metrics, such as quantiles or expected shortfall. We show how to implement these methods and discuss the limitations of this type of model, arising from the independence assumptions.

In this chapter, we consider qualitative and quantitative aspects of risk related to the development, implementation, and uses of quantitative models in enterprise risk management (ERM). First, we discuss the different ways that model risk arises, including defective models, inappropriate applications, and inadequate or inappropriate interpretation of the results. We consider the lifecycle of a model – from development, through regular updating and revision, to the decommissioning stage. We review quantitative approaches to measuring model and parameter uncertainty, based on a Bayesian framework. Finally, we discuss some aspects of model governance, and some potential methods for mitigating model risk.

In this chapter, we review the different methods available to a firm that wants to transfer risk. First, we consider the traditional route of insurance, or reinsurance. We describe the different types of insurance contracts, and analyse their advantages and disadvantages. We then consider captive insurance companies, which are insurance companies that are owned by the organization that is transferring risk. Next, we discuss securitization of risk, where risk is packaged into investments that are sold off in the capital markets. One of the most interesting examples of securitized insurance risk is the catastrophe bond, or cat bond. We also look at examples of securitization of demographic risk, through pandemic bonds and longevity derivatives.

A taxonomy is a classification system. In this chapter, we present a risk taxonomy, by which we mean that we shall categorize and describe all the major risks that may be faced by a firm or institution. We will describe risks that arise from outside the organization (external risks) and those that come from within the organization (internal risks). External risks are further categorized into economic, political, and environmental categories, while internal risks include operational and strategic risks. Reputational risk may be internally or externally generated. We describe some examples of how risks have arisen in several high-profile cases, showing the intersectionality of the different risk categories – that is, how the different risk types can all be driven by a single risk event.

In this chapter, we discuss the ways that credit risk arises, and how it can be modelled and mitigated. First, we consider the various types of contractual forms for loans and other obligations. We then discuss credit derivatives, which are contracts with payoffs that are contingent on credit events. We consider credit risk models based on the three fundamental components: probability of default, proportionate loss given default, and exposure at default. We consider models of default for individual firms, including the role of credit rating agencies, structural models, which are based on the underlying processes causing default, and reduced form models which are more based on the empirical information, with less emphasis on the underlying story. This is followed by a description of portfolio credit risk models, where the joint credit risk of multiple entities is the modelling objective.

In this chapter, we discuss some of the common psychological or behavioural factors that influence risk analysis and risk management. We give examples of cases where behavioural biases created a risk management failure, and some ways in which the negative impact of biases can be mitigated. Biases are categorized, loosely, as relating to (i) self-deception, (ii) information processing (both forms of cognitive bias), and (iii) social bias, relating to the pressures created by social norms and expectations. We give examples of a range of common behavioural biases in risk management, and we briefly describe some strategies for overcoming the distortions created by behavioural factors in decision-making. Next, we present the foundational concepts of Cumulative Prospect Theory, which provides a mathematical framework for decision making that reflects some universal cognitive biases.

In this chapter, we review some of the risk management implications of the regulation of banks and insurance companies. Banks are largely regulated through local implementation of the Basel II and Basel III Accords. Insurance regulation is more varied, but the development of the Solvency II framework in the European Union has influenced regulation more widely, and so we focus on Solvency II as an example of a modern insurance regulatory system.

In this chapter, we distinguish funding liquidity from market liquidity, and idiosyncratic liquidity from systemic liquidity. We discuss the nature of highly liquid assets, and methods by which a firm might acquire liquid assets to cover short-term cash flow problems, either in normal operations, or in more extreme crises. As liquidity risk is a problem of cash flow management, we explain how cash flow scenario tests can be used to identify and mitigate risks. We describe liquidity adjusted risk measures used in banking. Finally, we describe how firms might create emergency plans for managing extreme and unexpected liquidity shocks.