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This chapter presents the flaws of Value-at-Risk methodology—a statistical technique used to measure the level of financial risk within a firm or investment portfolio over a specific time frame. Value-at-Risk is used by risk managers to measure and control the level of risk which the firm undertakes. The risk managers' job is to ensure that risks are not taken beyond the level at which the firm can absorb the losses of a probable worst outcome. However, this practice has a flaw: the standard rolling window of observation is typically too long to stay relevant. Still, many risk managers think the opposite. They believe that averaging over multiple years is bound to improve precision.

This chapter presents the extensive enterprise risk management processes at work. After discussing why proactive risk management is important for value generation, the chapter links the traditional risk adjusted model framework to value generation from good risk management practices. An analytical tool for evaluating hedging decisions is presented. Then the chapter explains how companies can manage their risks. The enterprise risk management processes are explicated in detail including risk identification, risk assessment, risk treatment (with tools) and risk monitoring based on the value generation model. Risk management is linked to capital structure decisions and approaches to manage capital requirements are presented with the techniques to assess corporate financial stability. (109 words)

The backdrop of rapid growth of worldwide energy consumption and increasing concerns about global energy sustainability and environment protection, as well as an increasing uncertainty of commodity prices, require energy companies to use derivatives to hedge against risks related to energy trading. Over time, this situation has led to a more important role for energy risk management as part of a company’s core business operation. This chapter discusses the primary financial instruments used in the energy sector and risk management for energy companies. It reviews the application of several important quantitative methodologies, including Value at Risk and its variant risk metrics, to measure market risk. The chapter also examines credit risk measures and credit risk migration. Lastly, it discusses liquidity risk, operational risk, and legal risk. Overall, the chapter focuses more on the risk the commodity producer/deliverer faces and less on the end user.

The 2011 Japanese quake affected many companies and illustrates the growing problem with deep-tier supply chain disruptions in which the suppliers of suppliers are affected. Chapter 3 examines GM’s handling of the Japan crisis and the management of what GM calls “white-space”—the gap in parts supply left between pre-disruption inventories in the supply chain and the post-recovery refilling of the chain. The white-space conceptual framework allows companies to estimate a value-at-risk from various types of disruptions and to prioritize prevention and preparation initiatives.

Risk is endogenous. The brief opening and then the closing of the Millennium Bridge in London in 2000 illustrates how market prices play a dual role: they are simultaneously a reflection of market participants’ actions as well as an imperative for their actions.

This chapter surveys applications of Benford's law within economics, such as its use in investigating the validity of macroeconomic data series. It argues that, given the strong interest in strategic behavior in economics, it makes sense to use Benford's law to investigate possible anomalies that suggest manipulation or other interference, especially when incentives increase for such tampering. The chapter then considers how a first-digit analysis informs Value at Risk (VAR) data from the U.S. financial sector over the past ten years. It finds that Benford's law fits precrisis data very well but is rejected for postcrisis data. Opportunities and incentives for such misreporting are then discussed.

This chapter presents ways in which Value-at-Risk (VaR) can become useful in calculating financial risk. VaR may yield efficient results in measuring risk by applying range measures, implied volatilities, and information from other assets into dynamic formula or estimators, causing some investment advisors to take enough comfort to use volatility-adjusted indices as benchmarks for their own performance. Also, this method encourages the network externalities that favor a few leading currencies or credit ratings markets to focus on a few volatility-adjusted benchmarks. Given enough appeal, brokers will offer financial derivatives mimicking their performance to reduce transaction costs. In doing so, assisting retail investors would help level the playing field.

This chapter discusses the large output losses and the rises in inflation rates that accompanied the two oil shocks of 1970 in most industrialized countries, and shows the more recent absence of analogous effects, even though the rise in oil prices was of a similar magnitude. Using a Value at Risk (VAR) to identify exogenous oil price shocks, the chapter shows that the latter can only account for a relatively small part of the stagflationary episodes of 1970, suggesting that shocks other than oil but coinciding in time with the latter should also be held responsible for the dismal macroeconomic performance of that period. Three alternative explanations for the dampening effects of oil price shocks, that is, a smaller share of oil in production and consumption, more flexible labor markets, and an enhanced credibility of monetary policy are also given and being discussed in this chapter.