Advanced ERM Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk
This presentation is based on a part of an academic course on Advanced Enterprise Risk Management (Advanced ERM) titled ‘Risk measures and Measuring, managing and mitigating market, credit and operational risk’ and covers topics such as: mathematical underpins, market risk, credit risk, operational risk and managing market, credit and operational risk. It also includes appendices covering: VaR coherence and VaR vs TVaR, GARCH models, Maximum Likelihood Estimation (MLE), principal components analysis (PCA) and hazard rates and fitting operational risk loss distributions
Slides
| 1 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 2 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 3 | Model evolution characteristics |
| 4 | Whatever we do requires |
| 5 | Terminology |
| 6 | Defining value and applicable axioms |
| 7 | What if no suitable market in asset or liability? |
| 8 | Worth noting implication on loss definition |
| 9 | Assumed future behaviour of assets and liabilities |
| 10 | More generally |
| 11 | Most this course can realistically do |
| 12 | Case study / scenario |
| 13 | Selection of risk measures |
| 14 | Advantages and disadvantages of VaR |
| 15 | TVaR has certain technical advantages over VaR |
| 16 | Mathematical definitions of VaR and TVaR |
| 17 | VaR estimation |
| 18 | VaR closely linked to capital requirements |
| 19 | VaR and expected/unexpected loss |
| 20 | Other risk measures, e.g. |
| 21 | Marginal risk, e.g. marginal VaR (MVaR) |
| 22 | The Gaussian case |
| 23 | Incremental risk, e.g. incremental VaR (IVaR) |
| 24 | Risk budgeting |
| 25 | Statistical techniques for estimating risk measures |
| 26 | Parametric approach |
| 27 | Issues |
| 28 | Incomplete or out-of-date data |
| 29 | Non-linear exposures |
| 30 | Call option price |
| 31 | Heteroscedasticity |
| 32 | Non-parametric approach: order statistics |
| 33 | Standard errors in non-parametric approach |
| 34 | Empirical studies of VaR estimates |
| 35 | Back testing |
| 36 | Basel back testing rules for banks |
| 37 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 38 | Market risk |
| 39 | Typical approaches (1) |
| 40 | Typical approaches (2) |
| 41 | New directions for market risk: IRC |
| 42 | New directions for market risk: Stress VaR |
| 43 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 44 | Credit risk |
| 45 | Market developments |
| 46 | Implications of new credit markets |
| 47 | Credit portfolio risk models |
| 48 | Ratings-based models |
| 49 | Ratings-based models: components |
| 50 | Latent variables |
| 51 | Example |
| 52 | Pros and cons of ratings based models |
| 53 | Equity-based credit portfolio risk models |
| 54 | Equity and debt as options on asset value |
| 55 | Implementing an equity-based credit risk model |
| 56 | Implementing the model statistically |
| 57 | Default trigger |
| 58 | Strengths and weaknesses of equity-based models |
| 59 | Mixture models (1) |
| 60 | Mixture models (2) |
| 61 | Implementation issues for either type of model |
| 62 | Broader issues |
| 63 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 64 | Operational Risk |
| 65 | Agency problems, monitoring and control |
| 66 | More controls or more control? |
| 67 | An illustrative game theoretic model of fraud |
| 68 | Equilibria |
| 69 | Characteristics of intermittent fraud equilibrium |
| 70 | Managing operational risk in practice |
| 71 | Benchmarking processes and losses versus others |
| 72 | Sharing data |
| 73 | Capital for Operational Risk |
| 74 | Insurance as a substitute for capital |
| 75 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 76 | Managing market, credit and operational risk |
| 77 | Any action to reduce one risk can increase another |
| 78 | Quantifying risk transformation risks |
| 79 | Main 'market' hedging instruments |
| 80 | Dynamic hedging (1) |
| 81 | Dynamic hedging (2) |
| 82 | Risk/reward analysis and asset/exposure allocation |
| 83 | Session 2: Agenda covered |
| 84 | Session 2: Risk measures and Measuring, managing and mitigating market, credit and operational risk |
| 85 | Appendix A: VaR coherence and VaR vs TVaR |
| 86 | Axiomatic approach: coherence |
| 87 | Characterisation of coherent risk measures |
| 88 | VaR is coherent for Gaussian distributions |
| 89 | Arguments favouring TVaR based on coherence |
| 90 | More generic arguments favouring TVaR |
| 91 | Which takes into account loss in the event of default? |
| 92 | Different stakeholder perspectives (1) |
| 93 | Different stakeholder perspectives (2) |
| 94 | Treatment of illiquidity (1) |
| 95 | Treatment of illiquidity (2) |
| 96 | Treatment of illiquidity (3) |
| 97 | Stress testing methodologies |
| 98 | Appendix B: GARCH models |
| 99 | Properties of GARCH models |
| 100 | RiskMetrics approach |
| 101 | RiskMetrics specification |
| 102 | Appendix C: Maximum Likelihood Estimation |
| 103 | Log likelihood |
| 104 | Desirable properties of ML estimator |
| 105 | Likelihood ratio tests |
| 106 | Binomial back testing |
| 107 | Introducing a restriction |
| 108 | Appendix D: Principal components analysis |
| 109 | Principal components - explanation or noise? |
| 110 | Smaller eigenvalues/principal components |
| 111 | Appendix E: Hazard rates and fitting loss distributions |
| 112 | Hazard rates |
| 113 | Simple example of a hazard rate |
| 114 | Hazard rates that vary through time |
| 115 | Simulating independent failure times |
| 116 | Incorporating dependence in failure times |
| 117 | Fitting loss distributions |
| 118 | E.g. Gamma distribution |
| 119 | Important Information |
NAVIGATION LINKS
Contents | Next | ERM Lecture Series