Mindless Fitting? Decoding Derivatives
Derivatives Technology Foundation 4th Annual Symposium
Amsterdam, 2 June 2005
Piotr Karasinski
Managing Director, Global Derivative Products Development
email: piotr.karasinski@hsbcgroup.com

Disclaimer :

2 Disclaimer The opinions expressed in this presentation are those of the author alone and do not necessarily represent the views of HSBC Bank plc its subsidiaries or affiliates (HSBC). HSBC does not make any representation or warranty (express or implied) of any nature or accept any responsibility or liability of any kind for completeness or accuracy of any information, statement, assumption or projection in this document, or for any loss or damage (whether direct, indirect, consequential or other) arising out of reliance upon this document.

Introduction :

3 Introduction We are required to mark-to-market non-plain, exotic, products consistently with the market-observed prices of liquid vanilla products.
Thus for each exotic we must have a one-to-one mapping between vanilla prices and the exotic’s price. Such mapping is called the mark-to-market model as it produces mark-to-market price and risk exposure. Risk management policies (risk limits, desire to minimise volatility of the mark-to-market P&L) typically compel traders to hedge exotics with vanillas such that the combined risk exposure, measured by the mark-to-market model, is close to zero.
In the traditional approach we set price of an exotic equal to its’ value given by a traditional derivatives valuation model that assumes a certain stochastic evolution of the relevant risk factors. To fit vanilla prices practitioners often use (are forced to use?) over-parametrised models in which risk factor dynamics can be counter-intuitive. Does this produce a good model, i.e., does hedging to such model’s risk exposure result in realised replication costs that is close to the initial exotic’s price the model produces? How can we find an answer to this question?
What are the alternatives? Can we start with a price of an exotic produced by a standard derivatives valuation model, with risk factors’ dynamics that makes sense (who is to judge?), and somehow, externally, adjust the price to reflect the difference between market and model prices of relevant vanilla options? Would the resulting mapping produce a hedging model that is better than the one based on the traditional approach?

Derivatives Modelling and Darwin’s “Natural Selection” :

4 Derivatives Modelling and Darwin’s “Natural Selection” “I once worked for a year or so, for what seemed good reasons at the time, as a fitter’s mate in a soap factory on the Wirral Peninsula, Liverpool’s Left Bank. It was a formative episode, and was also, by chance, my first exposure to the theory of evolution.
To make soap powder, a liquid is blown through a nozzle. As it streams out, the pressure drops and a cloud of particles forms. These fall into a tank and after some clandestine coloration and perfumery are packaged and sold. In my day, thirty years ago, the spray came through a simple pipe that narrowed from one end to the other. It did its job quite well, but had problems with changes in the size of the grains, liquid spilling through or worst of all blockages in the tube. Those problems have been solved. The success is in the nozzle. What used to be a simple pipe has become an intricate duct, longer than before, with many constrictions and chambers. The liquid follows a complex path before it sprays from the hole. Each type of powder has its own nozzle design, which does the job with great efficiency.
(continued) Food-for-thought on derivatives modelling provided by a quote from Steve Jones’s book
Almost Like a Whale: The Origin of Species Updated (*), Chapter IV Natural Selection, (*) Published 1999 by Doubleday

Derivatives Modelling and Darwin’s “Natural Selection”(continued) :

5 Derivatives Modelling and Darwin’s “Natural Selection”(continued) The engineers used the idea that moulds life itself: descent with modification. Take a nozzle that works quite well and make copies, each changed at random. Test them for how well they make powder. Then, impose a struggle for existence by insisting that not all can survive. Many of the altered devices are no better (or worse) than the parental form. They are discarded, but the few able to do a superior job are allowed to reproduce and are copied but again not perfectly. As generations pass there emerges, as if by magic, a new and efficient pipe of complex and unexpected shape.
Natural selection is a machine that makes almost impossible things.” What caused such progress? Soap companies hire plenty of scientists, who have long studied what happens when a liquid sprays out to become a powder. The problem is too hard to allow even the finest engineers to do what enjoy the most, to explore the question with mathematics and design the best solution. Because that failed, they tried another approach. It was the key to evolution, design without a designer: the preservation of favourable variations and the rejection of those injurious. It was, in other words, natural selection.

Slide 6:

6 Darwin on the Shop Floor: Evolution of a Nozzle (*) (*) Slide provided by Prof. Steve Jones

Outline :

7 Outline What Makes a Good Model?
Foundations of Derivatives Pricing
Marking-to-Market Non-Plain Products: Traditional Approach
New Approach to Marking-to-Market Non-Plain Products
Maths of the New Approach
Advantages of the New Approach?

Slide 8:

8 What Makes a Good Model?

What is a Good Mark-to-Market Model? :

9 What is a Good Mark-to-Market Model? Mark-to-Market model has two components
Term-structure IR model
The way the IR model is used: model calibration, etc.
Some of the criteria used in judging mark-to-market models
Matching prices of reference plain liquid products
Matching market prices of non-plain products (produced by other banks?)
Accepted market practice, market standard
Easy to explain and can be disclosed to wider audiences
Marketers, Model Validation, Risk Management, Auditors, Regulators, etc.
Conservatism
Ease of implementation and cost of running

What is a good hedging model? :

10 What is a good hedging model? Hedging models has two components
Term-structure IR model
The way the IR model is used: model calibration, risk measures against which we hedge, tradeoff between local risk and transactions costs, etc.
Criteria used in judging hedging models
Does it do the best possible job replicating non-plain products?
Does it capture value of a non-plain trade?
Does it provide proper tradeoff between local risk hedging and transactions costs
Does it minimize uncertainty in the realised replication cost?
How can we measure such uncertainty?

What Model Should We Use for Hedging? :

11 What Model Should We Use for Hedging? Is hedging to mark-to-market model the best way to replicate non-plain products with plain ones?
If not we should consider using:
Alternative term-structure models
Increasing number of factors, different skew properties, etc.
Alternative method of calibration, additional risk measures
Am I taking model risk if I hedge to a model is not the same as the model I use for marking-to-market?
My mark-to-market model is likely to report non-zero risk even though my portfolio has zero risk according to the hedging model

Non-Plain Product’s Model Risk :

12 Non-Plain Product’s Model Risk Model-Dependence of Prices
Prices depend on the terms-structure model used
Number of curve factors included and rate-level dependence of volatility
Assumptions about jumps and stochastic volatility
Prices depend on how a particular model is used
What we calibrate model parameters to and how
Mark-to-market methodology based on the chosen model
Risk in Capturing P&L
Realised replication cost is uncertain
depends on what model we use and how
the market doesn’t follow any particular model, can have structural changes, etc.
transactions costs

Model Risk in Capturing P&L :

13 Model Risk in Capturing P&L How do we know that our models, and the way we use them, will allow us to capture the economic value (lock-in P&L) of a non-plain portfolio or a single trade over the trade’s/portfolio’s lifetime which sometime may extend to 30 and even 40 years?
How can we convince ourselves, and importantly significant others, that Warren Buffet’s prophecy (from 2000 Berkshire Hathaway’s annaual report) which says “In extreme cases, mark-to-model degenerates into what I would call mark-to-myth” will not be fulfilled?
In order to build tractable pricing/hedging models we make simplifying assumptions about the random behaviour of risk factors that determine the size of our non-plain products’ cashflows and cost of replicating these cashflows with plain products.
Our cumulative experience in building and using models, as well as intuition derived from this experience, guide us in choosing models, and the way we use them, and provides a level of confidence.

Where Do Prices of Liquid Plain Products Come From? :

14 Where Do Prices of Liquid Plain Products Come From? Prices of liquid plain products are impacted by:
Supply-and-demand (end-users’ hedging needs and risk-aversion, risk limits and risk appetite of providers, …), overall market liquidity
Beliefs about future swap curve behaviour (volatilities and correlations, etc) as these beliefs can be translated into a term-structure model which produces prices of plain products that can be compared with currently observed market prices of those non-plain products.
Our beliefs about future swap curve behaviour are shaped:
by swap curve’s past behaviour
“economic theory”
Some of the relative value players are fitting parameters in their term-structure models to swaption market data and measure relative value by
Comparing current levels of such implied parameters against past levels: past highs/lows etc.
Looking at individual swaptions’ current and historical levels of fit residuals.
Such relative value measures impact trading decisions and thus feed-back to market mid prices.
Overall market liquidity and risk aversion is another factor. During the LTCM crisis the implied vols of long-dated swaptions collapsed.

Slide 15:

15 Foundations of Derivatives Pricing

Pricing and Hedging of Non-Plain Products :

16 Pricing and Hedging of Non-Plain Products How much should we charge for an interest rate exotic?
How should we hedge it?
These two questions are obviously inter-related:
The price we should charge for an exotic should equal the present value of future hedging costs plus a reasonable profit margin.
So, how do we go about designing a hedging strategy?
Given a hedging strategy, how do we find the present value of realised hedging costs?
Will the present value of realised hedging costs depend on the future evolution of market prices/rates for our yield curve and volatility hedges? If so, what is the distribution of potential outcomes?

Fundamental Theorem of Derivatives Pricing :

17 Fundamental Theorem of Derivatives Pricing When:
All relevant market risk factors can be hedged, i.e., we can hedge yield curve, volatility and correlation risk with traded instruments.
We can hedge in continuous time — we can re-hedge as often as we need — without incurring transactions cost.
Market risk factors follow a diffusion process. The instantaneous covariance matrix between changes in risk factors’ levels is a known function of time and risk factors’ levels.
A derivative product has a unique price.
In plain English this means that there exists a unique hedging strategy for which the realised cost of replication is path-independent.

Slide 18:

18 Marking-to-Market Non-Plain Products:
Traditional Approach

Marking Non-Plain Products Relative to Plain Ones :

19 Marking Non-Plain Products Relative to Plain Ones We are required to mark-to-market non-plain products in a way that is consistent with mark-to-market prices of plain products
Thus for each non-plain product we need to design a functional form
for the dependence of the product’s mark-to-market price on:
valuation date
swap curve
parameters that enter into our mark-to-market model for plain products

Traditional Approach :

20 Traditional Approach We set the non-plain products mark-to-market value to its’ value given by a term-structure model
We calibrate model parameters to a product-dependent set of market prices of plain caps/floors and swaptions
Typical set of model parameters for a one-factor model: mean-reversion speed, parameters entering into the local volatility function, skew parameter.

Fitting Model Parameters for Bermudans :

21 Fitting Model Parameters for Bermudans Exact fit to a small set of “properly” chosen benchmarks
Number of fitted parameters equal to the number of benchmarks
Example: fix mean-reversion speed, fit the local volatility function so that we match “diagonal” swaptions with “properly” chosen strikes:
at-the-money
equal to the underlyer’s swap rate (for Bermudans on plain swaps)
etc.
Least-square fit (could be vega weighted)
Number of benchmarks > Number of model parameters
Issues with multiple local minima

Potential Problems with the Traditional Approach :

22 Potential Problems with the Traditional Approach Overparametrization
Risk factors’ dynamics that “does not make sense”
Excessive variability of model parameters
Proliferation of model calibrations
Inconsistent risk for plain products we calibrate to and/or hedge with:
Risk produced by our term structure model could differ from risk produced by the plain products’ mark-to-market model
Hedging to the “mark-to-model” model may not be the best way of capturing economic value.

Slide 23:

23 New Approach to Marking-to-Market
Non-Plain Products

New Framework for Marking Non-Plain Products :

24 New Framework for Marking Non-Plain Products We construct a portfolio that consists of a non‑plain product and so called “mark-to-market” hedge portfolio of plain products that:
hedges away sensitivities of the non-plain product’s model price to model parameters
satisfies additional criteria: “stability” conditions, etc.
We set the non-plain product’s mark-to-market price equal to:
the combined model price of the non-plain product and “mark-to-market” hedge portfolio
less the mark‑to-market price of the “mark-to-market” hedge portfolio
The above framework satisfies, by construction, the requirement that we mark non-plain products consistently with market prices of plain products.

Calibrating Model Parameters to Plain Products :

25 Calibrating Model Parameters to Plain Products We may prefer to have a small number of model parameters and model parameterizations that make economic sense.
We need to decide which model parameters to keep fixed. For example, we may keep correlations between factor shocks constant.
We may want to impose priors on the model parameters we calibrate based on historically fitted parameter levels and other criteria.
We need to decide which specific caps/floors and swaptions to include in our calibration set: we need to pick expiries, underlyers’ tenors, and strikes.
While we may prefer not to have trade-specific calibration sets, one model calibration may not work for all non-plain products. We hope that it will suffice to have a small number of product-group specific model calibrations.

Choosing Plain Product Hedges :

26 Choosing Plain Product Hedges We need to decide up-front which plain products to include in the mark-to-market hedge portfolio for a given non-plain product.
The number of plain products we choose could be larger than the number of model parameters.
We fix swaptions’ strike levels, expiry dates and underlier tenors, cap strikes and start/end dates.
For Bermudan swaptions we would include both on-diagonal and off-diagonal swaptions in the hedge portfolio.

Finding Hedge Portfolio Weights :

27 Finding Hedge Portfolio Weights We find the hedge portfolio weights by requiring that:
We hedge away sensitivities of the non-plain product’s model price to model parameters while at the same time we:
minimize the total portfolio gamma
satisfy hedge stability condition, i.e., that the hedged non-plain product portfolio’s delta with respect to model parameters is insensitive to small curve and model parameter shifts
satisfy “other” constraints

Slide 28:

28 Maths of the New Approach

Maths of Mark-to-Market: Notation :

29 Maths of Mark-to-Market: Notation

Maths of Mark-to-Market :

30 Maths of Mark-to-Market We set the combined time mark-to-market value of the non-plain product and the hedge portfolio equal to the respective model value:
The mark-to-market value of the non-plain product equals the above value less the mark-to-market value of the hedge portfolio:

Decomposing Changes in Mark-to-Market :

31 Decomposing Changes in Mark-to-Market Profit-and-Loss attribution plays an important role in managing a portfolio of derivative products. Here is how it works for non-plain products marked to market using our prescription.
We write the change in mark-to-market value of a non-plain product as a sum of three terms:

Terms in the P&L Decomposition :

32 Terms in the P&L Decomposition Change in the model value of a non-plain product due to yield curve shift, change in model calibration, and passage of time:
Change in the price adjustment due to change in discrepancy between model and market values of the time hedge portfolio (unchanged hedge portfolio weights):
Adjustment to mark-to-market price due to discrepancy between model and market values of the hedge added at time :

Non-Plain Product’s Delta Risk Hedge portfolio based on Parameter Hedging :

33 Non-Plain Product’s Delta Risk Hedge portfolio based on Parameter Hedging Non-plain product’s vega risk is equal to the total derivative of the product’s mark-to-market value with respect to volatility parameter vector

Non-Plain Product’s Vega RiskHedge portfolio based on Parameter Hedging :

34 Non-Plain Product’s Vega RiskHedge portfolio based on Parameter Hedging What is vega risk?
Risk due to small shifts in parameters entering mark-to-market model for plain caps/floors and swaptions
ATM vols or other vol-type variables, skew/smile parameters, etc.
What is the vega risk of a non-plain product equal to?
When the “mark-to-market” hedge portfolio is chosen to hedge away deltas of the non-plain product model value with respect to model calibration
The vega risk of a non-plain product’s mark-to-market value equals to:
negative of vega risk of the “mark-to-market” hedge portfolio plus a small adjustment

Non-Plain Product’s Vega Risk Formula Hedge portfolio based on Parameter Hedging :

35 Non-Plain Product’s Vega Risk Formula Hedge portfolio based on Parameter Hedging Non-plain product’s vega is equal to the partial derivative of the product’s mark-to-market value with respect to volatility parameter vector

Slide 36:

36 Advantages of the New Approach

Advantages of the New Approach :

37 Advantages of the New Approach Reduced number of model parameters
We can have risk factors’ dynamics that makes economic sense
Reduced variability of model parameters
Small number of model calibrations
Better aggregation of risk

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