Impact of negative rates on pricing models. Veronica Malafaia ING Bank - FI/FM Quants, Credit & Trading Risk Amsterdam, 18 th November 2015
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1 Impact of negative rates on pricing models Veronica Malafaia ING Bank - FI/FM Quants, Credit & Trading Risk Amsterdam, 18 th November 2015
2 Disclaimer: The views and opinions expressed in this presentation are those of the author and may not necessarily reflect the views and opinions of her employer.
3 Negative rates!
4 Negative rates! If you haven't found something strange during the day, it hasn't been much of a day. John Archibald Wheeler
5 Negative rates!
6 Negative rates! How do negative rates affect pricing models and their validation?
7 Mind map Market data Model NPV Pay off Risk management Numerical method
8 Outline 1. Brief market overview 2. A walk through pricing models 3. Models: are we done with DF>1 and σ L S σ N? 4. And what about validations? 5. Final remarks How do negative rates affect pricing models and their validation?
9 Brief market overview Bloomberg data
10 Brief market overview Bloomberg data
11 A walk through pricing models Two general remarks: All pricing models assuming a lognormal dynamics for the underlying interest rate are not suitable for a negative interest rate environment; For other asset classes than IR, most of the models will simply take DF>1.
12 A walk through pricing models Two general remarks All pricing models assuming a lognormal dynamics for the underlying interest rate are not suitable for a negative interest rate environment; For other asset classes than IR, most of the models will simply take DF>1. Let s examine some concrete cases: Black-76 Short rate models Libor market models SABR
13 A walk through pricing models IR models Black model Until recently, market paradigm for IR options Move towards shifted lognormal models/normal models due to low rate environment.
14 A walk through pricing models IR models Black model Until recently, market paradigm for IR options Move towards shifted lognormal models/normal models due to low rate environment. However, In theory, no lower limit for negative rates in a normal model. Is this realistic? How to fix the shift if a shifted lognormal model is chosen? Beware of tweaking the system! Converting normal prices to lognormal volatilities; Creating shifted curves to feed lognormal models;.
15 Prices A walk through pricing models IR models Lognormal, Shifted Lognormal and Normal prices 240 Normal Lognormal 220 Shifted Lognormal (0.5%) Shifted Lognormal (1%) % 0.50% 1.00% 1.50% 2.00% Normal vols
16 A walk through pricing models IR models Short rate models The most popular allow for negative rates (Vasicek, Hull-White, etc ). However, Is the implied level for negative rates compatible with reality (old problem)? Beware if calibration is done to lognormal volatilities!
17 A walk through pricing models IR models Swap rate distribution Y_LR 5Y_LR 1Y_HR 5Y_HR % -3.00% -1.00% 1.00% 3.00% 5.00% 7.00% 9.00%
18 A walk through pricing models IR models Swap rate distribution Y_LR 5Y_LR 1Y_HR 5Y_HR % -3.00% -1.00% 1.00% 3.00% 5.00% 7.00% 9.00%
19 A walk through pricing models IR models Libor market models Lognormal and normal formulations possible. However, Beware if calibration is done to lognormal volatilities/correlations!
20 A walk through pricing models IR models SABR Hagan et al s formula is the market convention for interpolating swaption volatilities. This formula corresponds to an expansion of the SABR model
21 A walk through pricing models IR models SABR Hagan et al s formula is the market convention for interpolating swaption volatilities. This formula corresponds to an expansion of the SABR model Hagan et. al. expansion fails to work well for high volatility, long maturities and very out-of-the money options. Negative density probability at low strike for long expiry options (particularly relevant in a low rate environment).
22 A walk through pricing models IR models SABR Hagan et. al. expansion fails to work well for high volatility, long maturities and very out-of-the money options. Negative density probability at low strike for long expiry options (particularly relevant in a low rate environment). Several approaches proposed in the literature but no market consensus yet: Improving the expansion (for e.g. expansion around normal SABR); Analytic approximations from SABR (for instance solution for uncorrelated case + mapping to the correlated case); Improving Hagan s implied density;
23 A walk through pricing models IR models SABR
24 Are we done with DF>1 and σ L S σ N? Negative interest rates can also trigger implicit floors thus affecting the payoff Main examples: Clauses preventing negative coupons in floating rate bonds More than 2.2 billion EUR of notes secured with residential mortgages in Europe are among asset-backed securities priced with spreads over Euribor of five basis points or less. [Source: Bloomberg] Clauses preventing negative interest on mortgages CSAs does the collateral poster need to pay interest if the reference rate turns negative?
25 Discounted Cashflow diff (bp) Are we done with DF>1 and σ L S σ N? What is the impact of a 0% in the collateral rate? % /12/ /05/ /09/ /02/2020 Pay date 0.30% EONIA (Apr 2015) 0.20% 0.10% ZC ZC_floor 0.00% -0.10% -0.20% 23/04/ /03/ /03/2025
26 And what about validations? In risk management, negative rates also changed the environment we were used to: Sensitivities Smile Old relations: American call options on non-dividend paying stocks have the same price as European calls provided IR are positive!
27 And what about validations? Sensitivities EUR 6Y 10Y ATM FLR Lognormal Normal BPV - 71, ,651 Vega 319, ,570 40,000 20, ,000 5Y 7Y 10Y 15Y 30Y -40,000-60,000-80, , , ,000 Lognormal Normal -160,000
28 And what about validations? Sensitivities EUR 6Y 10Y ATM FLR Lognormal Normal BPV - 71, ,651 Vega 319, ,570 40,000 20, ,000 5Y 7Y 10Y 15Y 30Y -40,000-60,000-80, , , ,000 Lognormal Normal -160,000
29 (bp) And what about validations? Sensitivities EUR 6Y 10Y ATM FLR Lognormal Normal BPV - 71, ,651 Vega 319, , Vega_N Vega_LN Eq N shift % -1.00% 0.00% 1.00% 2.00% Relative strike
30 And what about validations? Smile 10Y swaption volatility 0.9% 140% 0.8% 120% 0.7% 100% 0.6% 80% 0.5% 60% Logn Normal 0.4% 0.3% 40% 0.2% 20% 0.1% 0% 0.0% -2.00% -1.50% -1.00% -0.50% 0.00% 0.50% 1.00% 1.50% 2.00%
31 And what about validations? Smile
32 In short Market data Model - SABR and arbitrage - Volatility conversion - Shift size NPV - Level of negative rates - Shift size and variability - Calibration formulas (volatilities, correlations ) - Collateral rates (floors) Pay off - Implicit floors Risk management - VaR - Smile - Sensitivities behaviour - Old relations Numerical method
33 Questions?
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