On funding costs and the valuation of derivatives

Size: px
Start display at page:

Download "On funding costs and the valuation of derivatives"

Transcription

1 On funding costs and the valuation of derivatives Bert-Jan Nauta Double Effect September 9, 202 Abstract This paper contrasts two assumptions regarding funding costs of a bank in the context of the valuation of derivatives. One assumption, mostly used in the literature, is that funding costs are fixed. This leads to derivatives values that, in general, depend on the funding rate of the bank. The other, newly introduced, assumption is that funding costs immediately adjust after each new transaction to reflect the new asset composition. It is shown that under this assumption, in the Black-Scholes model, the funding costs of the bank do not affect the value of derivatives. It is argued that the latter assumption is more appropriate for the valuation of derivatives. Introduction This paper investigates the impact of funding costs on the valuation of derivatives. unding costs come into play since banks (or other financial actors) cannot simply borrow cash from the market at the risk free rate. Instead the bank pays a so-called funding rate for borrowing. The funding rate incorporates the premium for the default risk of the bank. In the valuation of derivatives this potentially has an impact, since the valuation methodology is based on the replication of pay-offs, that is cash flows. This replication involves borrowing and lending of the bank and potentially involves funding costs. A number of papers in recent years aim to include funding cost in the valuation of derivatives [, 2, 3, 4, 5]. E. g. Piterbarg [] derives the Black-Scholes valuation model including funding costs and collateral agreements, and Pallavicini, Perini, and Brigo [2] aim to include funding costs in a comprehensive pricing framework. bert-jan.nauta@doubleeffect.nl

2 Most of these papers assume the funding rate is fixed. However Dermine argues in [6] in the context of unds Transfer Pricing that the feedback effect of a transaction on the funding cost is important. In particular, Dermine argues that a bank with rating A should be able to invest in a AAA bond, even though seemingly its funding costs make it a loss generating deal. The argument is that the AAA bond increases the quality of the bank s assets which should be reflected in lower funding costs overall. The purpose of this paper is to include this feedback effect in the valuation of derivatives. To this end, I will contrast the assumption of fixed funding costs with the assumption that funding costs immediately reflect the new transaction. It is shown, in a Black-Scholes model, that under the latter assumption the funding costs of the bank have no impact on the value of the derivative. 2 Two cases In this section, I consider two cases based on two different assumptions. or both cases complications given by tax benefits of issued debt or other tax benefits are neglected. Specifically I will neglect the specific funding composition and assume funding to consist completely of equity. Under Modigliani-Miller conditions the results can be extended to more general funding compositions. Also I assume that the risk-free interest rate is equal to zero, since this allows for some notational simplicity. 2. Inelastic case This section reviews the derivation of the value of a zero-coupon (ZC) bond under the so-called inelastic assumption. This assumption states that the funding costs of a bank are fixed and a new transaction will not change these funding costs. Inelastic unding assumption: A new transaction does not affect the funding cost of a bank. As an example consider an uncollateralized zero coupon bond with notional m and maturity T. In the inelastic case the fair rate, s ZC, for this zero coupon bond is determined by considering the following strategy. At t = 0 bank invests m and receives s ZC bank borrows m at funding rate s At t = T if counterparty is not in default bank receives m exp(s ZC T ) if counterparty is in default bank receives 0 (under the assumption of zero recovery) 2

3 The fair rate is the rate, s ZC, that does not cause a profit or loss at time 0 for the bank: P D ZC 0 + ( P D ZC ) exp(s ZC T ) = exp(s T ) (2.) The probability of default of the counterparty, P D ZC, should be interpreted as market implied probability of default and can be related to the (implied) ZC spread, s implied ZC : exp(s implied ZC T ) = The fair rate for the ZC bond is thus given by P D ZC (2.2) s ZC = s + s implied ZC (2.3) The fair rate for the zero coupon bond includes the funding rate. 2.2 Elastic case In the elastic case, the derivation is based on the elastic funding assumption: Elastic unding assumption: The funding costs of the bank are adjusted immediately after each new transaction and fully reflect the new asset composition Under this assumption the funding rate is not given like in the inelastic case, but needs to be determined from an economic principle. To this end, the assumption is used that it should be equally attractive for investors to invest in the equity of the bank as in any other financial asset. This means that under the risk-neutral measure (and zero interest rates): E[E T ] = E 0, (2.4) where E 0 denotes the equity of the bank at time 0 and E T the equity of the bank at time T. Consider a simple balance sheet with a single asset A and funded completely with equity, for which the equity investor receives the funding rate s. The asset is a zero coupon bond with a maturity, T, and a probability of default, P D A. Then E 0 = A and E T is a random number that takes values A exp(s T ) if A is not in default and 0 if A is in default, where it is assumed that recovery is zero. The fair funding rate, s, is the rate that ensures (2.4) ( P D A )A exp(s T ) + P D A 0 = A (2.5) This results in exp(s T ) = P D A (2.6) 3

4 urthermore the fair rate on the asset, s A, can be determined by the requirement that at time 0 the equity investor should not make a profit or loss, which requires the value of the pay-off of the assets to equal the value of the equity (at time 0): E[A T ] = E[E T ]. (2.7) This requires that the return on the asset compensates the funding cost. Indeed in the simple case of a single asset, this implies that the fair rate on the asset equals the funding rate s A = s, (2.8) which gives in the familiar result exp(s A T ) = P D A. (2.9) Now the bank adds a ZC bond, ZC, with the same maturity T and a default probability P D ZC. Because of the elastic funding assumption the funding rate of the complete equity will re-adjust to the new fair funding rate. To calculate the new fair funding rate, also the joint default probability, the probability that A and ZC both default before time T, denoted by P D A&ZC, is required. As stated the assumption is that, with the new ZC bond, the funding rate is immediately adjusted to the new asset composition. or the equity investor there are now four cases: A and ZC do not default, A defaults and ZC survives, ZC defaults and A survives, and A and ZC both default. The new funding rate, s new, satisfies again (2.4), which gives ( P D A P D ZC + P D A&ZC )(A + ZC) exp(s new T ) +(P D A P D A&ZC )ZC exp(s new T ) +(P D ZC P D A&ZC )A exp(s new T ) +P D A&ZC 0 = A + ZC (2.0) The new funding rate is given by exp(s new T ) = A + ZC ( P D A )A + ( P D ZC )ZC (2.) This may be compared to the old funding rate in (2.6), which will be denoted by s old from now on. Note that depending on the difference between the PD of the zero coupon bond and the PD of the assets, the new funding rate will be higher or lower P D ZC = P D A s new P D ZC < P D A s new P D ZC > P D A s new = s old < s old > s old 4

5 If the PD of the zero coupon bond equals the PD of the asset A, then the funding cost will remain the same. If the PD of the ZC bond is smaller than the PD of the asset A, then the funding cost will go down, and if the PD of the ZC bond is larger than the PD of the asset A the funding cost will go up. This is consistent with the intuition that if the quality of the assets deteriorate the funding costs should go up and vice versa. urthermore if the zero coupon bond is much smaller than the asset A, the funding cost will change only by a small amount. Nevertheless this small change in funding cost will compensate for the funding cost for the ZC bond, as is shown next. Now that the new funding rate is such that the value of the pay-off of the equity at time T is A + ZC, the fair rate on the ZC-bond can be calculated from (2.7). The fair rate is determined by ( P D A P D ZC + P D A&ZC )(A exp(s A T ) + ZC exp(s ZC T )) +(P D A P D A&ZC )ZC exp(s ZC T ) +(P D ZC P D A&ZC )A exp(s A T ) +P D A&ZC 0 = A + ZC. (2.2) Since s A is known from (2.9), the ZC rate can be easily calculated exp(s ZC T ) = P D ZC. (2.3) This is the main result of this section. It shows that under the assumption of immediate adjustment of funding costs of the bank after a transaction, the funding costs do not affect the fair rate. This may be contrasted with the fair rate in the inelastic case, (2.3), which includes the funding cost. The above derivation was based on an extremely simple initial balance sheet with only a single asset. In the appendix, it is shown that the result holds also for a general asset mix, and a non-zero correlation (or dependence-structure) with the ZC-bond. 3 Inelastic vs Elastic Which of the two assumptions is more realistic? The assumption that a transaction does not affect the funding cost or that funding cost are immediately adjusted to take the new asset composition including the new transaction into account? It seems that the inelastic case is more realistic, since investors will not immediately after each transaction of the bank adjust the compensation they require for funding. Although this statement may be difficult to test in practice, since the adjustment required in the example of the ZC-bond is of the order ZC/A and may be too small to be observable. 5

6 Also most funding will not be overnight but (much) longer term funding (amongst others to mitigate liquidity risk). Therefore even if investors would be aware of all the changes in the asset composition of the bank, only the cost of a small part of the total funding could be adjusted. Nevertheless it is clear that if the asset composition changes over time the funding costs will change as well. Therefore the inelastic assumption seems not to be fully realistic either. Although it might be argued that on small time scales (e.g. M) the funding costs can be assumed to be fixed. Another argument in favor of the inelastic assumption is that this better represents the way banks are managed. A derivatives trading desk is simply confronted with a certain funding curve at which it can borrow/lend (perhaps separate curves are defined for borrowing and lending), but the impact of the transaction of the desk on funding cost of the bank is not taken into account. However an important argument in favor of the elastic assumption is that it allows for consistent valuation of defaultable bonds and other derivatives. Under the inelastic assumption the valuation of credit bonds would include the funding cost of the bank, as in (2.3), however since these are traded and the market value can be observed, they should be valued without funding cost. Therefore this would lead to an ad hoc distinction of trades valued including funding cost (under the inelastic assumption) and without. However the question at the start of this section Which of the two assumptions is more realistic? is in my opinion not the right question, since it basically asks how the treasury of the bank is organised. That a typical bank uses fixed funding costs does not automatically imply that using fixed funding costs in the valuation leads to the right value. In particular, it is imporant in my opinion to have a consistent framework. The assumption of elastic funding rates seems more appropriate then, since it leads to a consistent valuation of e.g. traded credit bonds. Also, if the ZC-bond, in the example in section 2, is large enough it can be put in a separate entity (an SPV) and the funding of of this SPV will solely be determined by the quality of the ZC-bond and not by the funding cost of the bank. A similar argument is made by Hull and White in [7]. They argue that funding costs should not be accounted for in the derivatives value. Allthough both assumptions reflect some parts of reality, I believe the elastic funding assumption is more appropriate for the valuation of derivatives. 4 Black-Scholes under Inelastic and Elastic unding Assumption This section considers the derivation of the Black-Scholes model under the inelastic and elastic funding assumption. The derivation under the inelastic assumption has been done by Piterbarg in [] and his derivation is followed here. The derivation 6

7 under the elastic assumption is new to my best knowledge. In the following three rates are used: r rf the risk free rate, for which often the OIS rate is used. r R the repo rate. r the funding rate for the bank. 4. Black-Scholes under the Inelastic assumption or simplicity, assume the underlying asset is a non-dividend paying stock and that the transaction is uncollateralized. The stock, S(t), follows the following dynamics: ds(t) = µs(t)dt + σs(t)dw (t), (4.) where µ denotes the drift, σ the volatility and W (t) a Brownian motion under the real world measure. Consider a derivative on the stock, V (t, S). To replicate the derivative a portfolio consisting of (t) units of stock and an amount γ(t) of cash is used. Where as usual V (t, S) (t) =. (4.2) S Piterbarg [] distinguishes the different contributions to the cash amount γ(t). an amount V (t, S) that is borrowed/lent unsecured at a funding rate r 2. an amount (t)s(t) that is secured by the stock and therefore earns/costs the repo rate r R The growth of the cash amount is thus given by dγ(t) = [r V (t, S) r R (t)s(t)]dt (4.3) Using Ito s lemma a PDE can be derived [], similar to the usual Black-Scholes PDE V (t, S) V (t, S) + r R S + σ2 t S 2 S2 2 V (t, S) S 2 = r V (t, S) (4.4) Note that the value of the derivative depends on both the repo rate and the funding rate. The conclusion is that under the inelastic funding assumption the value of a derivative (in general) depends on the funding rate. 4.2 Black-Scholes under the Elastic assumption Under the elastic funding assumption the derivation is different. In that case, the derivative and Delta hedge V (t, S) (t)s(t) (4.5) are added to the existing assets. As in section 2.2 I assume the portfolio needs to be funded by equity. Since for a small time dt the portfolio is risk free, the reasoning 7

8 leading to (2.3) (or its generalisation (A.)) applies here as well. Assuming no counterparty risk, the result is that the additional funding costs are determined by the risk free rate, r rf, without additional spread: dγ(t) = r rf [V (t, S) (t)s(t)]dt (4.6) Note that this is different from the argument in [5], where it is argued that the additional funding costs for a derivative are (in our notation) r rf V (t, S)dt, which is not consistent with the reasoning here, since the derivative is risky. At least, in the model developed here, only the combination of derivative and hedge will have an additional funding costs proportional to the risk free rate. In any case, from (4.6) the resulting PDE is V (t, S) t V (t, S) + r rf S + σ2 S 2 S2 2 V (t, S) S 2 = r rf V (t, S) (4.7) Under the elastic funding assumption the value of the derivative in the Black- Scholes model is independent of the funding rate. 4.3 A simple derivative To illustrate the difference of the two PDE s derived under the two assumptions, consider a simple derivative that at maturity, T, pays the stock, S(T ) V (T, S) = S(T ) (4.8) The Black-Scholes values according to the two assumptions are inelastic assumption: V (0, S) = exp( (r r R )T )S(0) (4.9) elastic assumption: V (0, S) = S(0) (4.0) The result for the inelastic assumption can be understood from noting that the option needs to be funded and the rate r is paid for this funding, whereas the hedge, a short position in the underlying stock, yields the repo rate, r R. The result from the elastic assumption is consistent with the simple hedge strategy to short the stock and from the cash received buy the derivative. 5 Conclusion In this paper the valuation of derivatives including funding costs is considered. Two opposite assumptions for the funding are considered: the inelastic and elastic funding assumption. It is shown that under the elastic funding assumption the funding costs do not affect the valuation of a zero-coupon bond or the valuation of a derivative in the Black-Scholes model. 8

9 One may observe that although this paper focuses on derivatives, the conclusions apply more generally to mark-to-market valuation. In particular, to the valuation of banking book items. Acknowledgements I would like to thank Drona Kandhai, Jaroslav Krystul, Tim Mexner, and Nikolai Zaitsev for useful discussions. References [] Piterbarg, V., unding beyond discounting: collateral agreements and derivatives pricing, Risk magazine, feb 200. [2] Pallavicini, A., D. Perini, and D. Brigo, unding Valuation Adjustment: a consistent framework including CVA, DVA, collateral, netting rules and rehypothecation arxiv:2.52v2 [q-fin.pr], dec 20. [3] Morini, M., and A. Prampolini, Risky funding: a unified framework for counterparty and liquidity charges, 200 [4] ries, C., Discounting Revisited. Valuations under unding Costs, Counterparty Risk and Collateralization, 200 [5] Burgard C. and M. Kjaer, In the Balance, Risk magazine, nov. 20. [6] Dermine J.,und Transfer Pricing (TP), Beyond the Global Banking Crisis in B. Swarup ed. Asset- Liability Management for inancial Institutions: Balancing inancial Stability with Strategic Objectives, Bloomsbury, London, 202. [7] Hull J., and A. White, The VA debate, Risk magazine, aug A Derivation for general loss distribution As in section 2 consider a balance sheet where the assets A are funded completely by equity. At time zero a zero-coupon bond is added, ZC, again funded by equity. The purpose of this appendix is to re-derive equation (2.) in case the assets A do not consist of a single asset with a single PD. The following notation is used: L T the loss at time T on the assets A. ρ A (L T ): the loss density for the assets A at time T (under the risk neutral/pricing measure). P D ZC (L T ): the probability of default of the zero coupon bond given a loss L T (on the assets A) at time T. 9

10 P D ZC : the unconditional probability of default of the zero coupon bond. LGD ZC : the loss given default of the zero coupon bond (e.g 60%). Given a loss L T, the total equity at time T is in expectaton: E[E T L T ] = A L T + ZC( P D ZC (L T )LGD ZC ) (A.) To calculate the time 0 value, the expectation value with respect to the risk neutral measure is taken: E[E[E T L T ]] = dlρ A (l) [A l + ZC( P D ZC (l)lgd ZC ] since = A EL A + ZC( P D ZC LGD ZC ), (A.2) dlρ A (l)p D ZC (l) = P D ZC, (A.3) and the expected loss is defined by EL A = dlρ A (l)l. (A.4) Requiring that the return on equity compensates the loss, (2.4), yields which results in [A EL A + ZC( P D ZC LGD ZC )] exp(s new T ) = A + ZC, (A.5) exp(s new T ) = A + ZC A EL A + ( P D ZC LGD ZC )ZC If there is only a single asset A, the expected loss can be expressed as and the result reduces to exp(s new T ) = EL A = P D A LGD A A A + ZC ( P D A LGD A )A + ( P D ZC LGD ZC )ZC (A.6) (A.7) (A.8) which is the same as (2.), except that the LGD can differ from 00%. Similar as in section 2.2, the fair rate for the ZC bond, s ZC is determined by (2.7). This gives dlρ A (L) [(A l) exp(s A T ) + ZC( P D ZC (l)lgd ZC ) exp(s ZC T ))] = (A EL A ) exp(s A T ) + ZC( P D ZC LGD ZC ) exp(s ZC T ) = A + ZC (A.9) Since the fair rate on the assets satisfies A exp(s A T ) = (A.0) A EL it follows that exp(s ZC T ) = (A.) P D ZC LGD ZC This extends the result (2.3) to a general asset mix and LGD. 0

The Funding Value Adjustment real or imaginary? Bert-Jan Nauta 21 November 2012

The Funding Value Adjustment real or imaginary? Bert-Jan Nauta 21 November 2012 The Funding Value Adjustment real or imaginary? Bert-Jan Nauta 21 The Funding Value Adjustment is topic of a heated debate For example, Risk magazine (risk.net) had a poll on its website: 2 The Funding

More information

arxiv: v1 [q-fin.pr] 22 Sep 2014

arxiv: v1 [q-fin.pr] 22 Sep 2014 arxiv:1409.6093v1 [q-fin.pr] 22 Sep 2014 Funding Value Adjustment and Incomplete Markets Lorenzo Cornalba Abstract Value adjustment of uncollateralized trades is determined within a risk neutral pricing

More information

Funding Value Adjustments and Discount Rates in the Valuation of Derivatives

Funding Value Adjustments and Discount Rates in the Valuation of Derivatives Funding Value Adjustments and Discount Rates in the Valuation of Derivatives John Hull Marie Curie Conference, Konstanz April 11, 2013 1 Question to be Considered Should funding costs be taken into account

More information

Valuation of Illiquid Assets on Bank Balance Sheets

Valuation of Illiquid Assets on Bank Balance Sheets MPRA Munich Personal RePEc Archive Valuation of Illiquid Assets on Bank Balance Sheets Bert-Jan Nauta RBS 1. April 2013 Online at http://mpra.ub.uni-muenchen.de/57663/ MPRA Paper No. 57663, posted 1. August

More information

Multi-Curve Discounting

Multi-Curve Discounting MPRA Munich Personal RePEc Archive Multi-Curve Discounting Bert-Jan Nauta RBS 20 April 2016 Online at https://mpra.ub.uni-muenchen.de/85657/ MPRA Paper No. 85657, posted 10 April 2018 11:45 UTC Multi-Curve

More information

arxiv: v1 [q-fin.pr] 7 Nov 2012

arxiv: v1 [q-fin.pr] 7 Nov 2012 Funded Bilateral Valuation Adjustment Lorenzo Giada Banco Popolare, Verona lorenzo.giada@gmail.com Claudio Nordio Banco Popolare, Verona c.nordio@gmail.com November 8, 2012 arxiv:1211.1564v1 [q-fin.pr]

More information

Credit Valuation Adjustment and Funding Valuation Adjustment

Credit Valuation Adjustment and Funding Valuation Adjustment Credit Valuation Adjustment and Funding Valuation Adjustment Alex Yang FinPricing http://www.finpricing.com Summary Credit Valuation Adjustment (CVA) Definition Funding Valuation Adjustment (FVA) Definition

More information

Changes in valuation of financial products: valuation adjustments and trading costs.

Changes in valuation of financial products: valuation adjustments and trading costs. Changes in valuation of financial products: valuation adjustments and trading costs. 26 Apr 2017, Università LUISS Guido Carli, Roma Damiano Brigo Chair in Mathematical Finance & Stochastic Analysis Dept.

More information

Bilateral counterparty risk valuation with stochastic dynamical models and application to Credit Default Swaps

Bilateral counterparty risk valuation with stochastic dynamical models and application to Credit Default Swaps Bilateral counterparty risk valuation with stochastic dynamical models and application to Credit Default Swaps Agostino Capponi California Institute of Technology Division of Engineering and Applied Sciences

More information

Credit Value Adjustment (CVA) Introduction

Credit Value Adjustment (CVA) Introduction Credit Value Adjustment (CVA) Introduction Alex Yang FinPricing http://www.finpricing.com Summary CVA History CVA Definition Risk Free Valuation Risky Valuation CVA History Current market practice Discounting

More information

Hedging Credit Derivatives in Intensity Based Models

Hedging Credit Derivatives in Intensity Based Models Hedging Credit Derivatives in Intensity Based Models PETER CARR Head of Quantitative Financial Research, Bloomberg LP, New York Director of the Masters Program in Math Finance, Courant Institute, NYU Stanford

More information

Change of Measure (Cameron-Martin-Girsanov Theorem)

Change of Measure (Cameron-Martin-Girsanov Theorem) Change of Measure Cameron-Martin-Girsanov Theorem Radon-Nikodym derivative: Taking again our intuition from the discrete world, we know that, in the context of option pricing, we need to price the claim

More information

The recursive nature of KVA: KVA mitigation from KVA

The recursive nature of KVA: KVA mitigation from KVA MPRA Munich Personal RePEc Archive The recursive nature of KVA: KVA mitigation from KVA Luis Manuel García Muñoz and Juan Esteban Palomar Burdeus and Fernando de Lope Contreras 19 April 2016 Online at

More information

Bank ALM and Liquidity Risk: Derivatives and FVA

Bank ALM and Liquidity Risk: Derivatives and FVA Bank ALM and Liquidity Risk: Derivatives and FVA CISI CPD Seminar 14 February 2013 Professor Moorad Choudhry Department of Mathematical Sciences Brunel University Agenda o Derivatives and funding risk

More information

No arbitrage conditions in HJM multiple curve term structure models

No arbitrage conditions in HJM multiple curve term structure models No arbitrage conditions in HJM multiple curve term structure models Zorana Grbac LPMA, Université Paris Diderot Joint work with W. Runggaldier 7th General AMaMeF and Swissquote Conference Lausanne, 7-10

More information

Assignment Module Credit Value Adjustment (CVA)

Assignment Module Credit Value Adjustment (CVA) Assignment Module 8 2017 Credit Value Adjustment (CVA) Quantitative Risk Management MSc in Mathematical Finance (part-time) June 4, 2017 Contents 1 Introduction 4 2 A brief history of counterparty risk

More information

Practical Hedging: From Theory to Practice. OSU Financial Mathematics Seminar May 5, 2008

Practical Hedging: From Theory to Practice. OSU Financial Mathematics Seminar May 5, 2008 Practical Hedging: From Theory to Practice OSU Financial Mathematics Seminar May 5, 008 Background Dynamic replication is a risk management technique used to mitigate market risk We hope to spend a certain

More information

The Black-Scholes Model

The Black-Scholes Model IEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh The Black-Scholes Model In these notes we will use Itô s Lemma and a replicating argument to derive the famous Black-Scholes formula

More information

Risky funding: a unified framework for counterparty and liquidity charges

Risky funding: a unified framework for counterparty and liquidity charges Risky funding: a unified framework for counterparty and liquidity charges Massimo Morini and Andrea Prampolini Banca IMI, Milan First version April 19, 2010. This version August 30, 2010. Abstract Standard

More information

Credit Risk Modelling This course can also be presented in-house for your company or via live on-line webinar

Credit Risk Modelling This course can also be presented in-house for your company or via live on-line webinar Credit Risk Modelling This course can also be presented in-house for your company or via live on-line webinar The Banking and Corporate Finance Training Specialist Course Overview For banks and financial

More information

Discounting Revisited. Valuations under Funding Costs, Counterparty Risk and Collateralization.

Discounting Revisited. Valuations under Funding Costs, Counterparty Risk and Collateralization. MPRA Munich Personal RePEc Archive Discounting Revisited. Valuations under Funding Costs, Counterparty Risk and Collateralization. Christian P. Fries www.christian-fries.de 15. May 2010 Online at https://mpra.ub.uni-muenchen.de/23082/

More information

Arbitrage, Martingales, and Pricing Kernels

Arbitrage, Martingales, and Pricing Kernels Arbitrage, Martingales, and Pricing Kernels Arbitrage, Martingales, and Pricing Kernels 1/ 36 Introduction A contingent claim s price process can be transformed into a martingale process by 1 Adjusting

More information

Credit Risk Modelling This in-house course can also be presented face to face in-house for your company or via live in-house webinar

Credit Risk Modelling This in-house course can also be presented face to face in-house for your company or via live in-house webinar Credit Risk Modelling This in-house course can also be presented face to face in-house for your company or via live in-house webinar The Banking and Corporate Finance Training Specialist Course Content

More information

The Black-Scholes Model

The Black-Scholes Model The Black-Scholes Model Liuren Wu Options Markets (Hull chapter: 12, 13, 14) Liuren Wu ( c ) The Black-Scholes Model colorhmoptions Markets 1 / 17 The Black-Scholes-Merton (BSM) model Black and Scholes

More information

FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A

FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A UNIVERSITY OF EAST ANGLIA School of Mathematics Main Series UG Examination 2016 17 FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A Time allowed: 3 Hours Attempt QUESTIONS 1 and 2, and THREE other

More information

1 Interest Based Instruments

1 Interest Based Instruments 1 Interest Based Instruments e.g., Bonds, forward rate agreements (FRA), and swaps. Note that the higher the credit risk, the higher the interest rate. Zero Rates: n year zero rate (or simply n-year zero)

More information

Attempt QUESTIONS 1 and 2, and THREE other questions. Do not turn over until you are told to do so by the Invigilator.

Attempt QUESTIONS 1 and 2, and THREE other questions. Do not turn over until you are told to do so by the Invigilator. UNIVERSITY OF EAST ANGLIA School of Mathematics Main Series UG Examination 2016 17 FINANCIAL MATHEMATICS MTHE6026A Time allowed: 3 Hours Attempt QUESTIONS 1 and 2, and THREE other questions. Notes are

More information

Challenges in Managing Counterparty Credit Risk

Challenges in Managing Counterparty Credit Risk Challenges in Managing Counterparty Credit Risk Jon Gregory www.oftraining.com Jon Gregory (jon@oftraining.com), Credit Risk Summit, London, 14 th October 2010 page 1 Jon Gregory (jon@oftraining.com),

More information

2 f. f t S 2. Delta measures the sensitivityof the portfolio value to changes in the price of the underlying

2 f. f t S 2. Delta measures the sensitivityof the portfolio value to changes in the price of the underlying Sensitivity analysis Simulating the Greeks Meet the Greeks he value of a derivative on a single underlying asset depends upon the current asset price S and its volatility Σ, the risk-free interest rate

More information

IFRS 13 - CVA, DVA AND THE IMPLICATIONS FOR HEDGE ACCOUNTING

IFRS 13 - CVA, DVA AND THE IMPLICATIONS FOR HEDGE ACCOUNTING WHITEPAPER IFRS 13 - CVA, DVA AND THE IMPLICATIONS FOR HEDGE ACCOUNTING By Dmitry Pugachevsky, Rohan Douglas (Quantifi) Searle Silverman, Philip Van den Berg (Deloitte) IFRS 13 ACCOUNTING FOR CVA & DVA

More information

Counterparty Risk and CVA

Counterparty Risk and CVA Counterparty Risk and CVA Stephen M Schaefer London Business School Credit Risk Elective Summer 2012 Net revenue included a $1.9 billion gain from debit valuation adjustments ( DVA ) on certain structured

More information

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Final Exam

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Final Exam The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (40 points) Answer briefly the following questions. 1. Consider

More information

The Black-Scholes Model

The Black-Scholes Model The Black-Scholes Model Liuren Wu Options Markets Liuren Wu ( c ) The Black-Merton-Scholes Model colorhmoptions Markets 1 / 18 The Black-Merton-Scholes-Merton (BMS) model Black and Scholes (1973) and Merton

More information

The Effects of Credit Risk and Funding on the Pricing of Uncollateralized Derivative Contracts

The Effects of Credit Risk and Funding on the Pricing of Uncollateralized Derivative Contracts Journal of Financial Risk Management, 2015, 4, 57-71 Published Online June 2015 in SciRes. http://www.scirp.org/journal/jfrm http://dx.doi.org/10.4236/jfrm.2015.42006 The Effects of Credit Risk and Funding

More information

θ(t ) = T f(0, T ) + σ2 T

θ(t ) = T f(0, T ) + σ2 T 1 Derivatives Pricing and Financial Modelling Andrew Cairns: room M3.08 E-mail: A.Cairns@ma.hw.ac.uk Tutorial 10 1. (Ho-Lee) Let X(T ) = T 0 W t dt. (a) What is the distribution of X(T )? (b) Find E[exp(

More information

Youngrok Lee and Jaesung Lee

Youngrok Lee and Jaesung Lee orean J. Math. 3 015, No. 1, pp. 81 91 http://dx.doi.org/10.11568/kjm.015.3.1.81 LOCAL VOLATILITY FOR QUANTO OPTION PRICES WITH STOCHASTIC INTEREST RATES Youngrok Lee and Jaesung Lee Abstract. This paper

More information

arxiv: v1 [q-fin.rm] 1 Jan 2017

arxiv: v1 [q-fin.rm] 1 Jan 2017 Net Stable Funding Ratio: Impact on Funding Value Adjustment Medya Siadat 1 and Ola Hammarlid 2 arxiv:1701.00540v1 [q-fin.rm] 1 Jan 2017 1 SEB, Stockholm, Sweden medya.siadat@seb.se 2 Swedbank, Stockholm,

More information

Advances in Valuation Adjustments. Topquants Autumn 2015

Advances in Valuation Adjustments. Topquants Autumn 2015 Advances in Valuation Adjustments Topquants Autumn 2015 Quantitative Advisory Services EY QAS team Modelling methodology design and model build Methodology and model validation Methodology and model optimisation

More information

January Ira G. Kawaller President, Kawaller & Co., LLC

January Ira G. Kawaller President, Kawaller & Co., LLC Interest Rate Swap Valuation Since the Financial Crisis: Theory and Practice January 2017 Ira G. Kawaller President, Kawaller & Co., LLC Email: kawaller@kawaller.com Donald J. Smith Associate Professor

More information

Statistical Methods in Financial Risk Management

Statistical Methods in Financial Risk Management Statistical Methods in Financial Risk Management Lecture 1: Mapping Risks to Risk Factors Alexander J. McNeil Maxwell Institute of Mathematical Sciences Heriot-Watt University Edinburgh 2nd Workshop on

More information

Valuing Coupon Bond Linked to Variable Interest Rate

Valuing Coupon Bond Linked to Variable Interest Rate MPRA Munich Personal RePEc Archive Valuing Coupon Bond Linked to Variable Interest Rate Giandomenico, Rossano 2008 Online at http://mpra.ub.uni-muenchen.de/21974/ MPRA Paper No. 21974, posted 08. April

More information

Heston Model Version 1.0.9

Heston Model Version 1.0.9 Heston Model Version 1.0.9 1 Introduction This plug-in implements the Heston model. Once installed the plug-in offers the possibility of using two new processes, the Heston process and the Heston time

More information

arxiv: v1 [q-fin.pr] 17 Sep 2010

arxiv: v1 [q-fin.pr] 17 Sep 2010 Completing CVA and Liquidity: Firm-Level Positions and Collateralized Trades Chris Kenyon arxiv:1009.3361v1 [q-fin.pr] 17 Sep 2010 16 September 2010, Version 1.01 Abstract Bilateral CVA as currently implement

More information

Multi-level Stochastic Valuations

Multi-level Stochastic Valuations Multi-level Stochastic Valuations 14 March 2016 High Performance Computing in Finance Conference 2016 Grigorios Papamanousakis Quantitative Strategist, Investment Solutions Aberdeen Asset Management 0

More information

Pricing CDOs with the Fourier Transform Method. Chien-Han Tseng Department of Finance National Taiwan University

Pricing CDOs with the Fourier Transform Method. Chien-Han Tseng Department of Finance National Taiwan University Pricing CDOs with the Fourier Transform Method Chien-Han Tseng Department of Finance National Taiwan University Contents Introduction. Introduction. Organization of This Thesis Literature Review. The Merton

More information

Basic Concepts and Examples in Finance

Basic Concepts and Examples in Finance Basic Concepts and Examples in Finance Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 5, 2017 ICMAT / UC3M The Financial Market The Financial Market We assume there are

More information

How Best To Incorporate The Leverage Ratio, LCR and NSFR into XVA?

How Best To Incorporate The Leverage Ratio, LCR and NSFR into XVA? How Best To Incorporate The Leverage Ratio, LCR and NSFR into XVA? Risk Minds 2015, Amsterdam Andrew Green Contents 1 Introduction 2 Leverage Ratio 3 LCR 4 5 Conclusion 6 Bibliography Disclaimer Joint

More information

Financial Derivatives Section 5

Financial Derivatives Section 5 Financial Derivatives Section 5 The Black and Scholes Model Michail Anthropelos anthropel@unipi.gr http://web.xrh.unipi.gr/faculty/anthropelos/ University of Piraeus Spring 2018 M. Anthropelos (Un. of

More information

NOT FOR REPRODUCTION. Lois: credit and liquidity. Most. ) max N. ) maxn (1)

NOT FOR REPRODUCTION. Lois: credit and liquidity. Most. ) max N. ) maxn (1) Lois: credit and liquidity he spread between Libor and overnight index swap rates used to be negligible until the crisis. Its behaviour since can be explained theoretically and empirically by a model driven

More information

Slides for DN2281, KTH 1

Slides for DN2281, KTH 1 Slides for DN2281, KTH 1 January 28, 2014 1 Based on the lecture notes Stochastic and Partial Differential Equations with Adapted Numerics, by J. Carlsson, K.-S. Moon, A. Szepessy, R. Tempone, G. Zouraris.

More information

Pricing Convertible Bonds under the First-Passage Credit Risk Model

Pricing Convertible Bonds under the First-Passage Credit Risk Model Pricing Convertible Bonds under the First-Passage Credit Risk Model Prof. Tian-Shyr Dai Department of Information Management and Finance National Chiao Tung University Joint work with Prof. Chuan-Ju Wang

More information

Valuation of Equity Derivatives

Valuation of Equity Derivatives Valuation of Equity Derivatives Dr. Mark W. Beinker XXV Heidelberg Physics Graduate Days, October 4, 010 1 What s a derivative? More complex financial products are derived from simpler products What s

More information

Asset Pricing Models with Underlying Time-varying Lévy Processes

Asset Pricing Models with Underlying Time-varying Lévy Processes Asset Pricing Models with Underlying Time-varying Lévy Processes Stochastics & Computational Finance 2015 Xuecan CUI Jang SCHILTZ University of Luxembourg July 9, 2015 Xuecan CUI, Jang SCHILTZ University

More information

Rho and Delta. Paul Hollingsworth January 29, Introduction 1. 2 Zero coupon bond 1. 3 FX forward 2. 5 Rho (ρ) 4. 7 Time bucketing 6

Rho and Delta. Paul Hollingsworth January 29, Introduction 1. 2 Zero coupon bond 1. 3 FX forward 2. 5 Rho (ρ) 4. 7 Time bucketing 6 Rho and Delta Paul Hollingsworth January 29, 2012 Contents 1 Introduction 1 2 Zero coupon bond 1 3 FX forward 2 4 European Call under Black Scholes 3 5 Rho (ρ) 4 6 Relationship between Rho and Delta 5

More information

4. Black-Scholes Models and PDEs. Math6911 S08, HM Zhu

4. Black-Scholes Models and PDEs. Math6911 S08, HM Zhu 4. Black-Scholes Models and PDEs Math6911 S08, HM Zhu References 1. Chapter 13, J. Hull. Section.6, P. Brandimarte Outline Derivation of Black-Scholes equation Black-Scholes models for options Implied

More information

Slides for Risk Management Credit Risk

Slides for Risk Management Credit Risk Slides for Risk Management Credit Risk Groll Seminar für Finanzökonometrie Prof. Mittnik, PhD Groll (Seminar für Finanzökonometrie) Slides for Risk Management Prof. Mittnik, PhD 1 / 97 1 Introduction to

More information

Credit Risk Modelling: A Primer. By: A V Vedpuriswar

Credit Risk Modelling: A Primer. By: A V Vedpuriswar Credit Risk Modelling: A Primer By: A V Vedpuriswar September 8, 2017 Market Risk vs Credit Risk Modelling Compared to market risk modeling, credit risk modeling is relatively new. Credit risk is more

More information

Pricing of a European Call Option Under a Local Volatility Interbank Offered Rate Model

Pricing of a European Call Option Under a Local Volatility Interbank Offered Rate Model American Journal of Theoretical and Applied Statistics 2018; 7(2): 80-84 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20180702.14 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online)

More information

FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A

FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A UNIVERSITY OF EAST ANGLIA School of Mathematics Main Series UG Examination 2016 17 FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A Time allowed: 3 Hours Attempt QUESTIONS 1 and 2, and THREE other

More information

The OIS and FVA relationship. Ion Mihai, PhD Client Solutions Group

The OIS and FVA relationship. Ion Mihai, PhD Client Solutions Group The OIS and FVA relationship Ion Mihai, PhD Client Solutions Group About Our Presenter Contact Our Presenter: Ion Mihai, PhD, Presenter Client Solutions Group imihai@numerix.com Follow Us: Twitter: @nxanalytics

More information

European call option with inflation-linked strike

European call option with inflation-linked strike Mathematical Statistics Stockholm University European call option with inflation-linked strike Ola Hammarlid Research Report 2010:2 ISSN 1650-0377 Postal address: Mathematical Statistics Dept. of Mathematics

More information

Actuarial Models : Financial Economics

Actuarial Models : Financial Economics ` Actuarial Models : Financial Economics An Introductory Guide for Actuaries and other Business Professionals First Edition BPP Professional Education Phoenix, AZ Copyright 2010 by BPP Professional Education,

More information

The Different Guises of CVA. December SOLUM FINANCIAL financial.com

The Different Guises of CVA. December SOLUM FINANCIAL  financial.com The Different Guises of CVA December 2012 SOLUM FINANCIAL www.solum financial.com Introduction The valuation of counterparty credit risk via credit value adjustment (CVA) has long been a consideration

More information

The Black-Scholes PDE from Scratch

The Black-Scholes PDE from Scratch The Black-Scholes PDE from Scratch chris bemis November 27, 2006 0-0 Goal: Derive the Black-Scholes PDE To do this, we will need to: Come up with some dynamics for the stock returns Discuss Brownian motion

More information

Derivative Securities Fall 2012 Final Exam Guidance Extended version includes full semester

Derivative Securities Fall 2012 Final Exam Guidance Extended version includes full semester Derivative Securities Fall 2012 Final Exam Guidance Extended version includes full semester Our exam is Wednesday, December 19, at the normal class place and time. You may bring two sheets of notes (8.5

More information

Attempt QUESTIONS 1 and 2, and THREE other questions. Do not turn over until you are told to do so by the Invigilator.

Attempt QUESTIONS 1 and 2, and THREE other questions. Do not turn over until you are told to do so by the Invigilator. UNIVERSITY OF EAST ANGLIA School of Mathematics Main Series UG Examination 2016 17 FINANCIAL MATHEMATICS MTHE6026A Time allowed: 3 Hours Attempt QUESTIONS 1 and 2, and THREE other questions. Notes are

More information

2 Modeling Credit Risk

2 Modeling Credit Risk 2 Modeling Credit Risk In this chapter we present some simple approaches to measure credit risk. We start in Section 2.1 with a short overview of the standardized approach of the Basel framework for banking

More information

Utility Indifference Pricing and Dynamic Programming Algorithm

Utility Indifference Pricing and Dynamic Programming Algorithm Chapter 8 Utility Indifference ricing and Dynamic rogramming Algorithm In the Black-Scholes framework, we can perfectly replicate an option s payoff. However, it may not be true beyond the Black-Scholes

More information

CVA in Energy Trading

CVA in Energy Trading CVA in Energy Trading Arthur Rabatin Credit Risk in Energy Trading London, November 2016 Disclaimer The document author is Arthur Rabatin and all views expressed in this document are his own. All errors

More information

1. 2 marks each True/False: briefly explain (no formal proofs/derivations are required for full mark).

1. 2 marks each True/False: briefly explain (no formal proofs/derivations are required for full mark). The University of Toronto ACT460/STA2502 Stochastic Methods for Actuarial Science Fall 2016 Midterm Test You must show your steps or no marks will be awarded 1 Name Student # 1. 2 marks each True/False:

More information

LECTURE 4: BID AND ASK HEDGING

LECTURE 4: BID AND ASK HEDGING LECTURE 4: BID AND ASK HEDGING 1. Introduction One of the consequences of incompleteness is that the price of derivatives is no longer unique. Various strategies for dealing with this exist, but a useful

More information

1.1 Implied probability of default and credit yield curves

1.1 Implied probability of default and credit yield curves Risk Management Topic One Credit yield curves and credit derivatives 1.1 Implied probability of default and credit yield curves 1.2 Credit default swaps 1.3 Credit spread and bond price based pricing 1.4

More information

Derivative Securities

Derivative Securities Derivative Securities he Black-Scholes formula and its applications. his Section deduces the Black- Scholes formula for a European call or put, as a consequence of risk-neutral valuation in the continuous

More information

Nonlinearity Valuation Adjustment Nonlinear Valuation under Collateralization, Credit Risk, & Funding Costs

Nonlinearity Valuation Adjustment Nonlinear Valuation under Collateralization, Credit Risk, & Funding Costs To appear in: Grbac, Z., Glau, K, Scherer, M., and Zagst, R. (Eds), Challenges in Derivatives Markets Fixed Income Modeling, Valuation Adjustments, Risk Management, and Regulation. Springer series in Mathematics

More information

Risk Modeling: Lecture outline and projects. (updated Mar5-2012)

Risk Modeling: Lecture outline and projects. (updated Mar5-2012) Risk Modeling: Lecture outline and projects (updated Mar5-2012) Lecture 1 outline Intro to risk measures economic and regulatory capital what risk measurement is done and how is it used concept and role

More information

Practical example of an Economic Scenario Generator

Practical example of an Economic Scenario Generator Practical example of an Economic Scenario Generator Martin Schenk Actuarial & Insurance Solutions SAV 7 March 2014 Agenda Introduction Deterministic vs. stochastic approach Mathematical model Application

More information

P1.T6. Credit Risk Measurement & Management

P1.T6. Credit Risk Measurement & Management Bionic Turtle FRM Practice Questions P1.T6. Credit Risk Measurement & Management Global Topic Drill By David Harper, CFA FRM CIPM www.bionicturtle.com GLOBAL TOPIC DRILL: CREDIT RISK MEASUREMENT & MANAGEMENT...

More information

Short-time-to-expiry expansion for a digital European put option under the CEV model. November 1, 2017

Short-time-to-expiry expansion for a digital European put option under the CEV model. November 1, 2017 Short-time-to-expiry expansion for a digital European put option under the CEV model November 1, 2017 Abstract In this paper I present a short-time-to-expiry asymptotic series expansion for a digital European

More information

Risk Neutral Measures

Risk Neutral Measures CHPTER 4 Risk Neutral Measures Our aim in this section is to show how risk neutral measures can be used to price derivative securities. The key advantage is that under a risk neutral measure the discounted

More information

Simultaneous Hedging of Regulatory and Accounting CVA

Simultaneous Hedging of Regulatory and Accounting CVA Simultaneous Hedging of Regulatory and Accounting CVA Christoph Berns Abstract As a consequence of the recent financial crisis, Basel III introduced a new capital charge, the CVA risk charge to cover the

More information

INTRODUCTION TO THE ECONOMICS AND MATHEMATICS OF FINANCIAL MARKETS. Jakša Cvitanić and Fernando Zapatero

INTRODUCTION TO THE ECONOMICS AND MATHEMATICS OF FINANCIAL MARKETS. Jakša Cvitanić and Fernando Zapatero INTRODUCTION TO THE ECONOMICS AND MATHEMATICS OF FINANCIAL MARKETS Jakša Cvitanić and Fernando Zapatero INTRODUCTION TO THE ECONOMICS AND MATHEMATICS OF FINANCIAL MARKETS Table of Contents PREFACE...1

More information

arxiv: v1 [q-fin.rm] 21 Dec 2018

arxiv: v1 [q-fin.rm] 21 Dec 2018 arxiv:1812.947v1 [q-fin.rm] 21 Dec 218 An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk Lucia Cipolina-Kun 1, Ignacio Ruiz 2, and Mariano Zeron-Medina Laris 3 1 Morgan Stanley 2

More information

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets Chapter 5: Jump Processes and Incomplete Markets Jumps as One Explanation of Incomplete Markets It is easy to argue that Brownian motion paths cannot model actual stock price movements properly in reality,

More information

Risk Management Using Derivatives Securities

Risk Management Using Derivatives Securities Risk Management Using Derivatives Securities 1 Definition of Derivatives A derivative is a financial instrument whose value is derived from the price of a more basic asset called the underlying asset.

More information

On the Correlation Approach and Parametric Approach for CVA Calculation

On the Correlation Approach and Parametric Approach for CVA Calculation On the Correlation Approach and Parametric Approach for CVA Calculation Tao Pang Wei Chen Le Li February 20, 2017 Abstract Credit value adjustment (CVA) is an adjustment added to the fair value of an over-the-counter

More information

Institute of Actuaries of India. Subject. ST6 Finance and Investment B. For 2018 Examinationspecialist Technical B. Syllabus

Institute of Actuaries of India. Subject. ST6 Finance and Investment B. For 2018 Examinationspecialist Technical B. Syllabus Institute of Actuaries of India Subject ST6 Finance and Investment B For 2018 Examinationspecialist Technical B Syllabus Aim The aim of the second finance and investment technical subject is to instil

More information

F1 Results. News vs. no-news

F1 Results. News vs. no-news F1 Results News vs. no-news With news visible, the median trading profits were about $130,000 (485 player-sessions) With the news screen turned off, median trading profits were about $165,000 (283 player-sessions)

More information

Pricing theory of financial derivatives

Pricing theory of financial derivatives Pricing theory of financial derivatives One-period securities model S denotes the price process {S(t) : t = 0, 1}, where S(t) = (S 1 (t) S 2 (t) S M (t)). Here, M is the number of securities. At t = 1,

More information

MSC FINANCIAL ENGINEERING PRICING I, AUTUMN LECTURE 6: EXTENSIONS OF BLACK AND SCHOLES RAYMOND BRUMMELHUIS DEPARTMENT EMS BIRKBECK

MSC FINANCIAL ENGINEERING PRICING I, AUTUMN LECTURE 6: EXTENSIONS OF BLACK AND SCHOLES RAYMOND BRUMMELHUIS DEPARTMENT EMS BIRKBECK MSC FINANCIAL ENGINEERING PRICING I, AUTUMN 2010-2011 LECTURE 6: EXTENSIONS OF BLACK AND SCHOLES RAYMOND BRUMMELHUIS DEPARTMENT EMS BIRKBECK In this section we look at some easy extensions of the Black

More information

OIS and Its Impact on Modeling, Calibration and Funding of OTC Derivatives. May 31, 2012 Satyam Kancharla SVP, Client Solutions Group Numerix LLC

OIS and Its Impact on Modeling, Calibration and Funding of OTC Derivatives. May 31, 2012 Satyam Kancharla SVP, Client Solutions Group Numerix LLC OIS and Its Impact on Modeling, Calibration and Funding of OTC Derivatives May 31, 2012 Satyam Kancharla SVP, Client Solutions Group Numerix LLC Agenda Changes in Interest Rate market dynamics after the

More information

Hedging CVA. Jon Gregory ICBI Global Derivatives. Paris. 12 th April 2011

Hedging CVA. Jon Gregory ICBI Global Derivatives. Paris. 12 th April 2011 Hedging CVA Jon Gregory (jon@solum-financial.com) ICBI Global Derivatives Paris 12 th April 2011 CVA is very complex CVA is very hard to calculate (even for vanilla OTC derivatives) Exposure at default

More information

A NOVEL BINOMIAL TREE APPROACH TO CALCULATE COLLATERAL AMOUNT FOR AN OPTION WITH CREDIT RISK

A NOVEL BINOMIAL TREE APPROACH TO CALCULATE COLLATERAL AMOUNT FOR AN OPTION WITH CREDIT RISK A NOVEL BINOMIAL TREE APPROACH TO CALCULATE COLLATERAL AMOUNT FOR AN OPTION WITH CREDIT RISK SASTRY KR JAMMALAMADAKA 1. KVNM RAMESH 2, JVR MURTHY 2 Department of Electronics and Computer Engineering, Computer

More information

A VALUATION MODEL FOR INDETERMINATE CONVERTIBLES by Jayanth Rama Varma

A VALUATION MODEL FOR INDETERMINATE CONVERTIBLES by Jayanth Rama Varma A VALUATION MODEL FOR INDETERMINATE CONVERTIBLES by Jayanth Rama Varma Abstract Many issues of convertible debentures in India in recent years provide for a mandatory conversion of the debentures into

More information

Modern Derivatives. Pricing and Credit. Exposure Anatysis. Theory and Practice of CSA and XVA Pricing, Exposure Simulation and Backtest!

Modern Derivatives. Pricing and Credit. Exposure Anatysis. Theory and Practice of CSA and XVA Pricing, Exposure Simulation and Backtest! Modern Derivatives Pricing and Credit Exposure Anatysis Theory and Practice of CSA and XVA Pricing, Exposure Simulation and Backtest!ng Roland Lichters, Roland Stamm, Donal Gallagher Contents List of Figures

More information

Option Pricing Models for European Options

Option Pricing Models for European Options Chapter 2 Option Pricing Models for European Options 2.1 Continuous-time Model: Black-Scholes Model 2.1.1 Black-Scholes Assumptions We list the assumptions that we make for most of this notes. 1. The underlying

More information

Valuation of a New Class of Commodity-Linked Bonds with Partial Indexation Adjustments

Valuation of a New Class of Commodity-Linked Bonds with Partial Indexation Adjustments Valuation of a New Class of Commodity-Linked Bonds with Partial Indexation Adjustments Thomas H. Kirschenmann Institute for Computational Engineering and Sciences University of Texas at Austin and Ehud

More information

2.4 Industrial implementation: KMV model. Expected default frequency

2.4 Industrial implementation: KMV model. Expected default frequency 2.4 Industrial implementation: KMV model Expected default frequency Expected default frequency (EDF) is a forward-looking measure of actual probability of default. EDF is firm specific. KMV model is based

More information

Advanced Topics in Derivative Pricing Models. Topic 4 - Variance products and volatility derivatives

Advanced Topics in Derivative Pricing Models. Topic 4 - Variance products and volatility derivatives Advanced Topics in Derivative Pricing Models Topic 4 - Variance products and volatility derivatives 4.1 Volatility trading and replication of variance swaps 4.2 Volatility swaps 4.3 Pricing of discrete

More information

Anchoring Heuristic in Option Pricing 1

Anchoring Heuristic in Option Pricing 1 Anchoring Heuristic in Option Pricing 1 Hammad Siddiqi School of Economics The University of Queensland h.siddiqi@uq.edu.au This Version: May, 2015 An anchoring-adjusted option pricing model is developed

More information

Extensions to the Black Scholes Model

Extensions to the Black Scholes Model Lecture 16 Extensions to the Black Scholes Model 16.1 Dividends Dividend is a sum of money paid regularly (typically annually) by a company to its shareholders out of its profits (or reserves). In this

More information