Narrow Results

This chapter proposes a mixture copula framework for integration of different types of bank risks, which is able to capture comprehensively the nonlinearity, tail dependence, tail asymmetry and structure asymmetry of bank risk dependence. We analyze why mixture copula is well-suited for bank risk integration, discuss how to construct a proper mixture copula and present detailed steps for using mixture copula. In the empirical analysis, the proposed framework is employed to model the dependence structure between credit risk, market risk and operational risk of Chinese banks. The comparisons with seven other major approaches provide strong evidence of the effectiveness of the constructed mixture copulas and help to uncover several important pitfalls and misunderstandings in risk dependence modeling.

Usually, companies with poor financial conditions at the time of an epidemic issue bonds, and it is particularly important to reasonably measure the various risks associated with the issue of bonds. This paper uses the Conditional Cost at Risk (CCaR) risk measurement technology based on the Conditional Value at Risk (CVaR) idea to measure the cost risk of epidemic prevention and control bonds issued by enterprises in the western region during the epidemic. We design a Conditional Payment at Risk (CPaR) risk measurement model to measure the liquidity risk brought by the issuance of epidemic prevention and control bonds to enterprises. We also analyze the effectiveness of epidemic prevention and control bonds in the western region based on different bonds and come up with an accurate evaluation. By comparing the calculation results, it can be analyzed that the overall level of financing utility of anti-epidemic bonds issued by non-government financed enterprises in the western region is good, both in terms of the market risk of bonds and the cash liquidity risk faced by enterprises.

Using an international dataset of 5,861 firm-year observations between 2009 and 2016 obtained from the Carbon Disclosure Project (CDP), we analyze the effect of firms’ Greenhouse Gas (GHG) emissions on stock price performance. To this end, we first discuss former research which finds an equity discount entailed by high levels of GHG emissions. We then focus on additional metrics of stock price performance, namely stock price return and stock price risk. Interestingly, we do not find any significant impact of GHG emissions on these metrics. A possible explanation is that investors are not yet able to quantify the GHG emission risk due to insufficient disclosure.

The following sections are included:

• INTRODUCTION

• FOUR KEY ELEMENTS OF A DECISION

• DECISIONS BASED ON EXTREME VALUES
  • Maximin Criterion
  • Minimax Regret Criterion

• EXPECTED MONETARY VALUE AND UTILITY ANALYSIS
  • The Expected Monetary Value Criterion
  • Utility Analysis

• BAYES STRATEGIES
  • Bayesian Approach in Forecasting Interest Rates

• DECISION TREES AND EXPECTED MONETARY VALUES
  • A Decision Tree for Testing a Drilling Site
  • A Decision Tree for Capital Budgeting
  • Utilizing Sample Information to Improve the Determination of Pricing Policy⁴

• MEAN AND VARIANCE TRADE-OFF ANALYSIS
  • The Mean–Variance Rule and the Dominance Principle
  • The Capital Market Line
  • The Capital Asset Pricing Model

• THE MEAN AND VARIANCE METHOD FOR CAPITAL BUDGETING DECISIONS
  • Statistical Distribution of Cash Flow

• Summary

• Appendix 21A Using the Spreadsheet in Decision-Tree Analysis

• Appendix 21B Graphical Derivation of the Capital Market Line

• Appendix 21C Present Value and Net Present Value
  • Present Value
  • Net Present Value

• Appendix 21D Derivation of Standard Deviation for NPV

• Questions and Problems

• Project V Project for Selected Topics in Statistical Analysis

An overview of risk measurement techniques for typical energy utilities is given. Most common calculated risk measures are explained among the often simple calculation methods used in practice. For a more sophisticated risk analysis, the various model classes proposed in the literature are reviewed. A three-factor model is explained in mathematical detail and its application to the practical modeling of energy prices is shown. This includes spot and futures prices in different time resolutions as well as the calibration of such models.