Derivatives Valuation And Risk Management Pdf
March 2017 CPS 226 - 1 Prudential Standard CPS 226 Margining and risk mitigation for non-centrally cleared derivatives Objectives and key requirements of this Prudential.
• • • In, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing. How To Download More Apps On Toshiba Smart Tv here. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist.
Rebonato in 2002 considers alternative definitions including: • After observing a set of prices for the underlying and hedging instruments, different but identically calibrated models might produce different prices for the same exotic product. • Losses will be incurred because of an 'incorrect' hedging strategy suggested by a model. Rebonato defines model risk as 'the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market'. Contents • • • • • • • • • • • • • • • • • • • • • • • • • Types [ ] Burke regards failure to use a model (instead over-relying on expert judgment) as a type of model risk. Derman describes various types of model risk that arise from using a model: Wrong model [ ] • Inapplicability of model. • Incorrect model specification.
Model implementation [ ] • Programming errors. • Technical errors. • Use of inaccurate numerical approximations.
Model usage [ ] • Implementation risk. • Data issues. • Calibration errors.
Sources [ ] Uncertainty on volatility [ ] Volatility is the most important input in risk management models and pricing models. Uncertainty on volatility leads to model risk. Derman believes that products whose value depends on a are most likely to suffer from model risk. He writes 'I would think it's safe to say that there is no area where model risk is more of an issue than in the modeling of the volatility smile.'
Avellaneda & Paras (1995) proposed a systematic way of studying and mitigating model risk resulting from volatility uncertainty. Time inconsistency [ ] Buraschi and Corielli formalise the concept of 'time inconsistency' with regards to models that allow for a perfect fit of the term structure of the interest rates. In these models the current is an input so that new observations on the can be used to update the model at regular frequencies. They explore the issue of time-consistent and self-financing strategies in this class of models.
Model risk affects all the three main steps of: specification, estimation and implementation. Correlation uncertainty [ ] Uncertainty on correlation parameters is another important source of model risk. Cont and Deguest propose a method for computing model risk exposures in multi-asset equity derivatives and show that options which depend on the worst or best performances in a basket (so called ) are more exposed to model uncertainty than index options. Gennheimer investigates the model risk present in pricing basket default derivatives. He prices these derivatives with various copulas and concludes that '. Unless one is very sure about the dependence structure governing the credit basket, any investors willing to trade basket default products should imperatively compute prices under alternative copula specifications and verify the estimation errors of their simulation to know at least the model risks they run'. Complexity [ ] Complexity of a model or a financial contract may be a source of model risk, leading to incorrect identification of its risk factors.
This factor was cited as a major source of model risk for mortgage backed securities portfolios during the 2007 crisis. An Arranged Marriage By Jo Beverley Pdf Printer here. Illiquidity and model risk [ ] Model risk does not only exist for complex financial contracts. Frey (2000) presents a study of how market illiquidity is a source of model risk. He writes 'Understanding the robustness of models used for hedging and risk-management purposes with respect to the assumption of perfectly liquid markets is therefore an important issue in the analysis of model risk in general.' ,, and can often be illiquid and difficult to value. Hedge funds that trade these securities can be exposed to model risk when calculating monthly NAV for its investors. Quantitative approaches [ ] Model averaging vs worst-case approach [ ] Rantala (2006) mentions that 'In the face of model risk, rather than to base decisions on a single selected 'best' model, the modeller can base his inference on an entire set of models by using model averaging.'
Another approach to model risk is the worst-case, or minmax approach, advocated in decision theory by Gilboa and Schmeidler. In this approach one considers a range of models and minimizes the loss encountered in the worst-case scenario. This approach to model risk has been developed by Cont (2006). Quantifying model risk exposure [ ] To measure the risk induced by a model, it has to be compared to an alternative model, or a set of alternative benchmark models.
The problem is how to choose these benchmark models. In the context of derivative pricing Cont (2006) proposes a quantitative approach to measurement of model risk exposures in derivatives portfolios: first, a set of benchmark models is specified and calibrated to market prices of liquid instruments, then the target portfolio is priced under all benchmark models. A measure of exposure to model risk is then given by the difference between the current portfolio valuation and the worst-case valuation under the benchmark models.
Such a measure may be used as a way of determining a reserve for model risk for derivatives portfolios. Position limits and valuation reserves [ ] Kato and Yoshiba discuss qualitative and quantitative ways of controlling model risk. They write 'From a quantitative perspective, in the case of pricing models, we can set up a reserve to allow for the difference in estimations using alternative models. In the case of risk measurement models, scenario analysis can be undertaken for various fluctuation patterns of risk factors, or position limits can be established based on information obtained from scenario analysis.' Cont (2006) advocates the use of model risk exposure for computing such reserves. Mitigation [ ] Theoretical basis [ ] • Considering key assumptions. • Considering simple cases and their solutions (model boundaries).
Implementation [ ] • Pride of ownership. • Disseminating the model outwards in an orderly manner. Testing [ ] • and.
• Avoid letting small issues snowball into large issues later on. • Independent validation • Ongoing monitoring and against market Examples of model risk mitigation [ ] Parsimony [ ] Taleb wrote when describing why most new models that attempted to correct the inadequacies of the model failed to become accepted: 'Traders are not fooled by the Black–Scholes–Merton model. The existence of a 'volatility surface' is one such adaptation. But they find it preferable to fudge one parameter, namely volatility, and make it a function of time to expiry and strike price, rather than have to precisely estimate another.' However, Cherubini and Della Lunga describe the disadavantages of parsimony in the context of volatility and correlation modelling.
Using an excessive number of parameters may induce while choosing a severely specified model may easily induce model misspecification and a systematic failure to represent the future distribution. Model risk premium [ ] Fender and Kiff (2004) note that holding complex financial instruments, such as, 'translates into heightened dependence on these assumptions and, thus, higher model risk.
As this risk should be expected to be priced by the market, part of the yield pick-up obtained relative to equally rated single obligor instruments is likely to be a direct reflection of model risk.' Case studies [ ] • —Interest rate options and swaptions—incorrect model specification. • —Interest rate options and swaptions. • —lack of stress testing—Crouhy, Galai, and Mark.
• (BZW)—Mispriced currency options. • $3 Billion AUD loss on Homeside interest rate model.
• – Over-reliance on 's Gaussian copula model misprices the risk of collateralized debt obligations. See also [ ] • • • Notes [ ].
This book presents 20 peer-reviewed chapters on current aspects of derivatives markets and derivative pricing. The contributions, written by leading researchers in the field as well as experienced authors from the financial industry, present the state of the art in: • Modeling counterparty credit risk: credit valuation adjustment, debit valuation adjustment, funding valuation adjustment, and wrong way risk. • Pricing and hedging in fixed-income markets and multi-curve interest-rate modeling. • Recent developments concerning contingent convertible bonds, the measuring of basis spreads, and the modeling of implied correlations. The recent financial crisis has cast tremendous doubts on the classical view on derivative pricing. Now, counterparty credit risk and liquidity issues are integral aspects of a prudent valuation procedure and the reference interest rates are represented by a multitude of curves according to their different periods and maturities.
A panel discussion included in the book (featuring Damiano Brigo, Christian Fries, John Hull, and Daniel Sommer) on the foundations of modeling and pricing in the presence of counterparty credit risk provides intriguing insights on the debate.