Counterparty Risk - wrong way risk and liquidity issues. Antonio Castagna -
|
|
- Ruby Heath
- 5 years ago
- Views:
Transcription
1 Counterparty Risk - wrong way risk and liquidity issues Antonio Castagna antonio.castagna@iasonltd.com
2 Index Counterparty Wrong-Way Risk 1 Counterparty Wrong-Way Risk 2 Liquidity Risk Pricing in OTC Derivatives
3 Index Counterparty Wrong-Way Risk 1 Counterparty Wrong-Way Risk 2 Liquidity Risk Pricing in OTC Derivatives
4 Definition Counterparty Wrong-Way Risk Counterparty risk is the risk that a party to an OTC derivative contract may fail to perform on its contractual obligations, causing losses to the other party. Losses are usually quantified in terms of the replacement cost of the defaulted derivatives. Counterparty risk can be: 1 One-Way: One party faces the exposures depending on the (ever positive) value of the position it holds against the other party; 2 Two-Way: Both parties may face exposures depending on the value of the positions they hold against each other. The feature distinguishing counterparty risk from lending risk is uncertainty of exposure at any future date: 1 Loan: exposure at any future date is the outstanding balance, which is certain (not taking into account prepayments); 2 Derivative: exposure at any future date is the replacement cost, which is determined by the market value at that date and is, therefore, uncertain.
5 Index Counterparty Wrong-Way Risk 1 Counterparty Wrong-Way Risk 2 Liquidity Risk Pricing in OTC Derivatives
6 Counterparty Risk Exposure of a Contract We start by assuming that no netting or margin agreement is in place. The market value of contract i with a counterparty is known only for the current date t = 0. For any future date t, the value V i (t) is random. If the counterparty defaults at time τ cpt before the contract s maturity, the economic loss is equal to the replacement cost of the contract: if V i (τ cpt) > 0, we do not receive anything from defaulting counterparty, but have to pay V i (tau cpt) to another counterparty to replace the contract; if V i (τ cpt) < 0, we receive V i (τ cpt) from another counterparty, but have to give this amount to the defaulting counterparty. Combining these two scenarios, we can specify contract-level exposure E i (t) at time t as: E i (t) = max[v i (t),0]
7 Counterparty Risk Exposure of a Contract At a future time T > t, the exposure is uncertain:
8 Theoretical Approaches to Default Modelling The second building block of the Counterparty Risk measurement is the prediction of the default of the counterparty. Jointly to the contract and portfolio level exposure, default determines the counterparty risk fully. In theoretical literature two approaches to model single defaults: Structural Models: Default occurs as soon as the firm value crosses a given barrier (from above) Reduced (Intensity-based) Models: The default time is modelled as the first jump time of a given jump process (typically a Poisson process), occurring with an intensity λ(t), also called hazard rate. This is the probability of a default occurring at an infinitesimal time after t given that it did not occur before, and can be a stochastic process itself.
9 Index Counterparty Wrong-Way Risk 1 Counterparty Wrong-Way Risk 2 Liquidity Risk Pricing in OTC Derivatives
10 Wrong/Right-Way Risk Wrong/right-way risk arises from dependence between credit quality of a counterparty and exposure to that counterparty. The risk is wrong (right) way when the exposure tends to increase (decrease) when counterparty credit quality worsens. Wrong/right-way risk can be general (dependence caused by systematic risk factors) or specific (dependence caused by counterparty-specific risk factors). Some examples: 1 We sell credit protection on X to Y: general right-way 2 We enter into oil receiver swap with oil producer: general wrong-way 3 We buy a put option on X stock from Y: general wrong-way 4 We buy a put option on X stock from X: specific wrong-way Specific wrong-way risk should be avoided. We analyse the impact of wrong-way risk for a swap portfolio on: The CVA adjustment for the risk-free value of a portfolio of swaps, to account for the expected losses given the default of the counterparty; The counterparty credit VaR.
11 Theoretical Framework to Include Wrong-Way Risk We assume that the default probability of the counterparty is stochastic over the reference period. Default is a jump whose probability of occurrence is determined by an intensity λ(t), which is a stochastic process. Roughly speaking, the intensity indicates the annual probability of default, so that if λ(t) = 2%, there is a 2% probability that our counterparty will go defaulted in next year. In our framework, the intensity varies over time, so that the defalt probability is not constant. The Expected positive exposure (EPE) of a swap is computed assuming that all Euribor/Libor rates have a terminal Lognormal distribution. It is possible to determine the distribution of the swap rates from the distributions of the single Euribor/Libor rates (an analytical approximation is used in our framework) We correlate the default intensity with the swap rates, and we derive analytical approximation for the expected losses (EPE PD). We tested this approximation against Montecarlo simulations and we found it very accurate.
12 Credit Value Adjustment The Credit Valuation Adjustment (CVA) of an OTC derivatives portfolio with a given counterparty is the risk-neutral expectation of the discounted loss of value of the portfolio, due to default by the counterparty T n CVA = Sprd P(t,t k ) EPE(t k ) t k k=1 Sprd PD LGD is the CDS spread dealing in the market for the counterparty s debt. CVA can be computed analytically only at the contract level for several simple cases. Calculating discounted EPE at the counterparty level requires simulation. The market value of a portfolio of derivatives with a risky counterparty is given by the risk-free market value minus the relevant CVA, as defined above.
13 A Practical Example: Market Data We show an example, assuming the following market data for interest rates: Time Eonia Fwd Sread Fwd Libor % 0.65% 1.40% % 0.64% 1.39% % 0.64% 2.39% % 0.63% 2.63% % 0.63% 2.88% % 0.62% 2.99% % 0.61% 3.11% % 0.61% 3.26% % 0.60% 3.35% % 0.60% 3.47% % 0.59% 3.59% % 0.59% 3.69% % 0.58% 3.78% % 0.58% 3.88% % 0.57% 3.97% % 0.57% 4.07% % 0.56% 4.16% % 0.56% 4.23% % 0.55% 4.30% % 0.55% 4.37% % 0.54% 4.44% 5.00% 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% Euribor Fw d Eonia Fw d
14 A Practical Example: Market Data Market data for caps&floors and swaptions volatilities are: Caps&Floors Expiry Volatility % % % % % % % % % % % % % % % % % % % % Swaptions Expiry Tenor Volatility % % % % % % % % % % % % % % % % % % % 10 0
15 A Practical Example: Default Probabilities We assume that default is a jump occurring with an intensity λ following a CIR process: dλ t = κ(θ λ t)dt +ν λ tdz t Years PD % % % % % % % % % % Parameters are chosen to be: λ 0 3.0% κ 27.0% θ 3.0% ν 20.0% 25.00% 20.00% 15.00% 10.00% PD The resulting PD are shown beside. 5.00% 0.00%
16 A Practical Example: Constant Notional Portfolio We analyse how the CVA is affected by different levels of correlation between the (synthetic) swap rate of the portfolio and the intensity of default. The portfolio of swaps has a constant notional amount over next 10 years as shown below and it is a net receiver fixed rate. Years Notional We compute the CVA for different levels of correlation (nil=-0%, medium=-50%, high=-90%), and for different levels of the synthetic swap rate (at-the-money, in-the-money=atm+1%, out-of-the-money=atm-1%). Corr Swap- OTM ATM ITM Def.Intens. 1.63% 2.63% 3.63% 0% % % Adjustment over the risk-free swap rate to include the counterparty risk. Corr Swap- OTM ATM ITM Def.Intens. 1.63% 2.63% 3.63% 0% % % % -50% % % % -90% % % %
17 ELgd Zero Corr ELgd Medium Corr ELgd High Corr ELgd Zero Corr ELgd Medium Corr ELgd High Corr ELgd Zero Corr ELgd Medium Corr ELgd High Corr Counterparty Wrong-Way Risk A Practical Example: Constant Notional Portfolio Expected Positive Exposure (OTM,ATM,ITM) Expected Loss Given Default (OTM,ATM,ITM) EPE EPE EPE
18 A Practical Example: Decreasing Notional Portfolio The same analysis on how the CVA is affected by different levels of correlation between the (synthetic) swap rate of the portfolio and the intensity of default, is performed for a declining notional swap portfolio over next 10 years, as shown below. It is a net receiver fixed rate. Years Notional We compute the CVA for different levels of correlation (nil=-0%, medium=-50%, high=-90%), and for different levels of the synthetic swap rate (at-the-money, in-the-money=atm+1%, out-of-the-money=atm-1%). Corr Swap- OTM ATM ITM Def.Intens. 1.63% 2.63% 3.63% 0% % % Adjustment over the risk-free swap rate to include the counterparty risk. Corr Swap- OTM ATM ITM Def.Intens. 1.63% 2.63% 3.63% 0% % % % -50% % % % -90% % % %
19 ELgd Zero Corr ELgd Medium Corr ELgd High Corr ELgd Zero Corr ELgd Medium Corr ELgd High Corr ELgd Zero Corr ELgd Medium Corr ELgd High Corr Counterparty Wrong-Way Risk A Practical Example: Decreasing Notional Portfolio Expected Positive Exposure (OTM,ATM,ITM) Expected Loss Given Default (OTM,ATM,ITM) EPE EPE EPE
20 A Practical Example: Increasing Notional Portfolio Finally we operate the analysis on how the CVA is affected by different levels of correlation between the (synthetic) swap rate of the portfolio and the intensity of default, for an increasing notional swap portfolio over next 10 years, as shown below. It is still a net receiver fixed rate. Years Notional We compute the CVA for different levels of correlation (nil=-0%, medium=-50%, high=-90%), and for different levels of the synthetic swap rate (at-the-money, in-the-money=atm+1%, out-of-the-money=atm-1%). Corr Swap- OTM ATM ITM Def.Intens. 1.63% 2.63% 3.63% 0% % % Adjustment over the risk-free swap rate to include the counterparty risk. Corr Swap- OTM ATM ITM Def.Intens. 1.63% 2.63% 3.63% 0% % % % -50% % % % -90% % % %
21 ELgd Zero Corr ELgd Medium Corr ELgd High Corr ELgd Zero Corr ELgd Medium Corr ELgd High Corr ELgd Zero Corr ELgd Medium Corr ELgd High Corr Counterparty Wrong-Way Risk A Practical Example: Increasing Notional Portfolio Expected Positive Exposure (OTM,ATM,ITM) Expected Loss Given Default (OTM,ATM,ITM) EPE EPE EPE
22 Basel II Regulation: the IMM IMM approach is the most suited to properly take into account the market risks related to a given counterparty s portfolio The building blocks of the IMM approach: definition of a set of statistics for internal and regulatory purposes identification of market factors and generation of future scenarios pricing algorithms to price the contracts included in the books aggregation rules to evaluate the effects of the risk mitigation agreements a framework modelling the credit risk of the counterparties, the correlations amongst them and the correlation of counterparties with market risks to measure the full counterparty risk calculation of the counterparty exposure measures, i.e. its risk profile, credit value adjustment, economic and regulatory capital
23 Exposure Measures in the IMM Method Starting from exposure for each counterparty we can define other statistics and risk measures: Expected exposure (EE) [ ] EE i (t k ) = E max[v i (t k ),0] Expected positive exposure (EPE) EPE i (t k ) = 1 t k t 0 Effective Maturity M k EE i (t j )(t j t j 1 ) j=1 [ ] Σ T M = min k=1 Df(t k ) EPE i (t k ) Σ t k 1Y k=1 Df(t k ) EPE(t k ),5
24 Regulatory Capital Counterparty Wrong-Way Risk The Regulatory Capital (RC) is computed by means of the following quantities 1 EPE 2 The α factor (ratio of the EC calculated with full simulation, to the EC calculated with a constant exposure equal to EPE) 3 The Effective EPE (E EPE), which takes into account the roll-off risk EEPE(t k ) = max[eepe(t k 1 ),EPE(t k )] and it is actually the counterparty exposure measure which the RC is computed upon The α factor is calculated on a given time interval, but kept constant otherwise. The period between two calculations depends on the granularity and the time evolution of the portfolio
25 Regulatory Capital Counterparty Wrong-Way Risk For regulatory purposes, the capital can be determined as follows: MCR = α EEPE RW 8% dove RW = 12.5 K and ( ) N 1 (PD i )+r i N 1 (0.999) K = LGDN LGD PD 1 r 2 i i PD i = default probability for counterparty i LGD = loss given default r i = systemic risk load factor, also indicated by the Regulation equal to: 0.12 (1 e ( 50 PD) )/(1 e 50 )+0.24 [1 (1 e ( 50 PD) )/(1 e 50 )] α has to be estimated according to an internal model approved by the Surveillance Authority (with a 1.20 minimum), otherwise it has to be set equal to 1.40.
26 Regulatory Capital and Wrong-Way Risk We aim at introducing the wrong-way risk also in the counterparty credit VaR calculation. The idea is still to have a loan equivalent of the exposure, so that we can adjust the EPE or the EEPE measure to input into the regulatory formula. A possible approach to apply to the framework outlined above to compute the VaR is: Compute the stressed (99.9% c.l.) PD according to the supervisory formulae; Deduce which is the level of the default intensity consistent with the stressed PD; Compute the conditioned mean and variance of the risk factors determining the exposure of the derivative contracts; Compute the new EPE with the conditioned mean and variance; Calculate the wrong-way-adjusted Economic Capital with the new EPE or the corresponding E EPE.
27 Counterparty Credit VaR: an Example We analyze a swap portfolio expiring in 1 year, with monthly interest rate exchanges. The portfolio is net receiver fixed rate. The counterparty has a PD = 2.94% in next year, and this is produced by a jump process whose intensity is a stocahstic process with parameters seen above. We test 3 versions: constant (P1), moderately decreasing (P2) and incresing (P3) notional. According to the supervisory formula, the stressed PD at a 99.9% c.l. is 22.34%. Market rates and volatilities: Months Eonia 1M Libor Libor Vol % 0.99% 34.00% % 1.02% 34.50% % 1.05% 35.00% % 1.07% 35.50% % 1.10% 36.00% % 1.12% 36.50% % 1.15% 37.00% % 1.17% 37.50% % 1.20% 38.00% % 1.22% 38.50% % 1.25% 39.00% % 1.27% 39.50% Months P1 P2 P
28 Counterparty Credit VaR: an Example EPE and EEPE (OTM,ATM,ITM) Portfolio P1 (Constant Notional). Tables below show the effective EPE for different levels of correlation and average fixed rate of the portfolio, and the ratio (α) with the 0-correlation case. Beside the figures show the EPE and the effective EPE for a correlation of 10%, for the three fixed rate values indicated in the tables EPE EPE WV EEPE WV EEPE OTM ATM ITM Corr Swap-Def.Intens. 0.85% 1.10% 1.35% 0% % % EPE EPE WV EEPE WV EEPE OTM ATM ITM Corr Swap-Def.Intens. 0.85% 1.10% 1.35% 0% % % EPE EPE WV EEPE WV EEPE
29 Counterparty Credit VaR: an Example EPE and EEPE (OTM,ATM,ITM) Portfolio P2 (Decreasing Notional). Tables below show the effective EPE for different levels of correlation and average fixed rate of the portfolio, and the ratio (α) with the 0-correlation case. Beside the figures show the EPE and the effective EPE for a correlation of 10%, for the three fixed rate values indicated in the tables EPE EPE WV EEPE WV EEPE OTM ATM ITM Corr Swap-Def.Intens. 0.84% 1.09% 1.34% 0% % % EPE EPE WV EEPE WV EEPE OTM ATM ITM Corr Swap-Def.Intens. 0.84% 1.09% 1.34% 0% % % EPE EPE WV EEPE WV EEPE
30 Counterparty Credit VaR: an Example EPE and EEPE (OTM,ATM,ITM) Portfolio P3 (Increasing Notional). Tables below show the effective EPE for different levels of correlation and average fixed rate of the portfolio, and the ratio (α) with the 0-correlation case. Beside the figures show the EPE and the effective EPE for a correlation of 10%, for the three fixed rate values indicated in the tables EPE EPE WV EEPE WV EEPE OTM ATM ITM Corr Swap-Def.Intens. 0.88% 1.13% 1.38% 0% % % EPE EPE WV EEPE WV EEPE OTM ATM ITM Corr Swap-Def.Intens. 0.88% 1.13% 1.38% 0% % % EPE EPE WV EEPE WV EEPE
31 Counterparty Credit VaR: Some Conclusions The experiments we have presented above seem to show that, at least as far as the wrong-way risk is concerned, the regulatory coefficient α is in many cases too low. This is even more evident if consider the fact that we chose very small value for the correlation. Actually, the stressed PD s in a Merton (Gaussian copula) approach, such as the regulatory one, are determined by the stressed level of a common factor affecting all the debtors and producing a correlation amongst defaults. The correlation with respect to this factor does not need to be the same as the correlation between the single total PD and market risk factors (in our examples, the swap rates) and it is likely lower than the correlation with the specific factors, although not always this is the case. We have not explored in the analysis this extension, but it would be straightforward to set the default intensity of each conuterparty as: λ D = λ i +p i λ C where λ C is an intensity process common to all counterparties and λ i is specific to each counterparty.
32 Counterparty Credit VaR: Some Conclusions The α (for the part due to the wrong-way) is also a function of the features of the portfolio of contracts with a counterparty, in terms of average fixed rate received (or paid) and evolution of the aggregated notional over time. The main finding is that one α good for all occasions is not a wise choice. When considering also the correlation amongst the defaults of all the counterparties, some diversification effects may be expected, so that the α can actually be lower than 1.40 which is the standard level set by regulation if the bank is not able to compute a full deployed VaR. To introduce a rich structure of correlation amongst counterparties defaults, the regulatory formula has to be enhanced so as to include more factors. Extensions of the Merton s (Gaussian copula) approach are available and some of them can be effectively solved in very good analytic approximations. It is important to check which is the real contribution of the correlations to the Economic Capital for management purposes. For regulatory purposes, the proposed α = 1.40 seems to be very favorable to banks to save allocated capital.
33 Index Counterparty Wrong-Way Risk Liquidity Risk Pricing in OTC Derivatives 1 Counterparty Wrong-Way Risk 2 Liquidity Risk Pricing in OTC Derivatives
34 Collateral and Margin Agreements Liquidity Risk Pricing in OTC Derivatives Collateral agreement is a contract between two counterparties that requires one or both counterparties to post collateral (typically cash or high quality bonds) under certain conditions. Margin agreement is a legally binding collateral agreement with specific rules for posting collateral, which include: 1 Minimum transfer amount: defines the minimum amount of collateral that can be exchanged. If the exposure entails a collateral posting below the minimum, amount, no collateral is provided; 2 A threshold, defined for one (unilateral agreement) or both (bilateral agreement) counterparties. If the difference between the net portfolio value and already posted collateral exceeds the threshold, the counterparty must provide collateral sufficient to cover this excess (subject to minimum transfer amount); 3 Frequency: defines the periodicity of the exposure calculation and of the determination of the collateral to post. The terms of the rules depend mainly on the credit qualities of the counterparties involved.
35 Index Counterparty Wrong-Way Risk Liquidity Risk Pricing in OTC Derivatives 1 Counterparty Wrong-Way Risk 2 Liquidity Risk Pricing in OTC Derivatives
36 Pricing OTC Derivatives with CSA Liquidity Risk Pricing in OTC Derivatives In a very general fashion, the price at time 0 of a derivatives contract which is not subject to counterparty risk is: V 0 = E Q [ e T 0 r sds V T ] where V T is the terminal pay-off of the contract; r t is the (possibly time dependent) risk-free interest rate. When counterpaty risk is considered, then we have to include the so called CVA (the expected losses we suffer when on default of the counterparty) the and DVA (the expected losses the counterparty suffers on our default): V CCP 0 = E Q [ e T 0 r sds V T ] CVA+DVA The terminal value of the contract is still discounted ad the risk-free rate r t, but then the price is adjusted with the net effect due to the losses upon default of the two counterparties involved in the trade.
37 Pricing OTC Derivatives with CSA Liquidity Risk Pricing in OTC Derivatives Assume now we have a CSA agreement operating between the two counterparties. The CSA provides for a daily margining mechanism of the full variation of the NPV (nowadays a very common form of the CSA). The party that owns a positive balance on the collateral account (corresponding to a positive NPV of the contract) pays the rate c t to the other party. The pricing of the contract can be now be operated by excluding the default risk (there is still a very small residual risk between two daily margining). It can be shown that the pricing formula is very similar to the standard case we have seen above, but with the collateral rate c t replacing the risk-free rate r t.: V CSA 0 = E Q [ e T 0 c sds V T ] (1)
38 Pricing OTC Derivatives with CSA Liquidity Risk Pricing in OTC Derivatives This result is very convenient, since we have a well defined rate that has to be paid on the collateral balance (set within the contract), whereas the risk-free rate is very difficult to determine in the current market environment (it used to be the Libor in the interbank market). Usually the daily margined CSA agreements set the remuneration of the collateral at the EONIA for contracts in euro (or some equivalent OIS rate for other currencies). EONIA (OIS) rates can be considered the best approximation of a risk-free rate. Nevertheless there is still one assumption that is made when deriving the pricing formula with the CSA: The rate at which the bank can lend money is the same of the one it can borrow money. This assumption can be easily relaxed when we price contracts whose NPV can be always either positive or negative (e.g.: a long or a short position on an option contract). When the NPV of the contract can switch from positive to negative and/or from negative to positive (e.g.: forward and swap contracts) then relaxing the assumption is trickier.
39 Pricing OTC Derivatives with CSA Liquidity Risk Pricing in OTC Derivatives Assume we have a contract whose value during the life of the contract at any time 0 < t < T, V t can be positive or negative. We also assume that the bank can invest cash at a risk-free rate equal to the collateral rate r t = c t, but it has a funding spread f t when borrowing money over a short period, so that the total funding cost is r t +f t. When considering the funding spread in the pricing of a collateralized derivatives contract, it can be shown that the valuation equation can be written as: V CSA 0 = E Q [ e T 0 c u f u1 {Vu<0} du V T ] = E Q [ e T 0 c udu V T ]+LVA (2) where LVA = E Q [ T 0 ] e s 0 c udu min(v s(0),0)f sds (3)
40 Pricing OTC Derivatives with CSA Liquidity Risk Pricing in OTC Derivatives Equation (3) can be computed numerically with a good degree of approximation: it accounts for the funding cost that the bank has to pay when financing the cash injection in the collateral account, expressed has a spread f t over the risk-free rate r t = c t, which on the contrary is the rate the bank can invest at. We name this quantity Liquidity Value Adjustment (LVA) As an example, when we price a (K-fix rate receiver) swap contract starting in t i and ending in T = t N, the minimum value of the contract is min(v t,0) = min( V t,0) = max(s i,n K,0). Assume we divide the period between the evaluation time t 0 = 0 and the expiry in N intervals. The LVA can be written as: LVA Swp = N Pay(t i ;t i,t,k)f ti t i (4) i=1 where Pay is the value of a payer swaption struck at K, expiring in t i and written on a swap starting in t i and maturing in T.
41 A Practical Example Counterparty Wrong-Way Risk Liquidity Risk Pricing in OTC Derivatives We show an example, assuming the following market data for interest rates: Time Eonia Fwd Spread Fwd Libor % 0.65% 1.40% % 0.64% 1.39% % 0.64% 2.39% % 0.63% 2.63% % 0.63% 2.88% % 0.62% 2.99% % 0.61% 3.11% % 0.61% 3.26% % 0.60% 3.35% % 0.60% 3.47% % 0.59% 3.59% % 0.59% 3.69% % 0.58% 3.78% % 0.58% 3.88% % 0.57% 3.97% % 0.57% 4.07% % 0.56% 4.16% % 0.56% 4.23% % 0.55% 4.30% % 0.55% 4.37% % 0.54% 4.44% 5.00% 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% Euribor Fw d Eonia Fw d
42 A Practical Example Counterparty Wrong-Way Risk Liquidity Risk Pricing in OTC Derivatives Market data for caps&floors and swaptions volatilities are: Caps&Floors Expiry Volatility % % % % % % % % % % % % % % % % % % % % Swaptions Expiry Tenor Volatility % % % % % % % % % % % % % % % % % % % 10 0
43 Liquidity Risk Pricing in OTC Derivatives A Practical Example: Collateralized Swap We price under a CSA agreement a receiver swap whereby we we pay the Libor fixing semi-annually (set at the previous payment date) and we receive the fixed rate annually. With market data shown above, the fair rate can be easily calculated (we are using the new market standard approach to employ the EONIA/OIS curve for discounting and the 6M Libor curve to project forward rates). We assume also that we have to pay a funding spread of 15bps over the EONIA/OIS curve. This is applied to the ENE plotted beside. LVA % Fair Swap rate % Swap Rate + Coll. Fund % Difference % - (1.0000) (2.0000) (3.0000) (4.0000) (5.0000) (6.0000) (7.0000) ENE
44 Liquidity Risk Pricing in OTC Derivatives A Practical Example: Collateralized Swap We may be interested in calculating the impact of the liquidity of a collateralized swap with respect to a more conservative measure than the ENE, similarly to what happens in the counterparty risk management. We choose the Potential Future Exposure, which is the expected negative NPV of the swap at a given level of confidence, set in this example at the 99% and computed with market volatilities. The funding spread is still 15bps over the EONIA/OIS curve. The Potential Future Exposure (blue line), and the ENE (purple line, same as before) for comparison, are plotted beside. LVA % Fair Swap rate % Swap Rate + Coll. Fund % Difference % - (5.0000) ( ) ( ) ( ) ( ) ( ) PFE ENE
45 About Iason Counterparty Wrong-Way Risk Liquidity Risk Pricing in OTC Derivatives Iason is a company created by market practitioners, financial quants and programmers with valuable experience achieved in dealing rooms of financial institutions. Iason offers a unique blend of skills and expertise in the understanding of financial markets, in the pricing of complex financial instruments and in the measuring and the management of banking risks. The company s structure is very flexible and grants a fully bespoke service to our Clients. Iason believes that the ability to develop new quantitative finance approaches through research as well as to apply those approaches in practice, is critical to innovation in risk management and derivatives pricing. It brings into all the areas of the risk management a new and fresh approach based on the balance between rigour and efficiency Iason s people aimed at when working in the dealing rooms. Besides tailor made services, Iason offers software applications to calculate and monitor credit VaR and conterparty VaR, fund transfer pricing and loan pricing, liquidity-at-risk. c Iason This is a Iason s creation. The ideas and the model frameworks described in this presentation are the fruit of the intellectual efforts and of the skills of the people working in Iason. You may not reproduce or transmit any part of this document in any form or by any means, electronic or mechanical, including photocopying and recording, for any purpose without the express written permission of Iason ltd.
Interrelations amongst Liquidity, Market and Credit Risks -
Interrelations amongst Liquidity, Market and Credit Risks - some proposals for integrated approaches Antonio Castagna www.iasonltd.com 28th February 2012 Index Balance Sheet Items Requiring Statistic-Financial
More informationAdvances 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 informationCredit 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 informationCredit 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 informationIFRS 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 informationModeling Credit Exposure for Collateralized Counterparties
Modeling Credit Exposure for Collateralized Counterparties Michael Pykhtin Credit Analytics & Methodology Bank of America Fields Institute Quantitative Finance Seminar Toronto; February 25, 2009 Disclaimer
More informationLecture notes on risk management, public policy, and the financial system Credit risk models
Lecture notes on risk management, public policy, and the financial system Allan M. Malz Columbia University 2018 Allan M. Malz Last updated: June 8, 2018 2 / 24 Outline 3/24 Credit risk metrics and models
More informationModelling Counterparty Exposure and CVA An Integrated Approach
Swissquote Conference Lausanne Modelling Counterparty Exposure and CVA An Integrated Approach Giovanni Cesari October 2010 1 Basic Concepts CVA Computation Underlying Models Modelling Framework: AMC CVA:
More informationCounterparty Credit Risk and CVA
Jon Gregory Solum Financial jon@solum-financial.com 10 th April, SIAG Consulting, Madrid page 1 History The Complexity of CVA Impact of Regulation Where Will This Lead Us? 10 th April, SIAG Consulting,
More informationBilateral 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 informationNegative Rates: The Challenges from a Quant Perspective
Negative Rates: The Challenges from a Quant Perspective 1 Introduction Fabio Mercurio Global head of Quantitative Analytics Bloomberg There are many instances in the past and recent history where Treasury
More informationDiscounting. Jeroen Kerkhof. 22 September c Copyright VAR Strategies BVBA 1 / 53
Discounting Jeroen Kerkhof 22 September 2010 c Copyright VAR Strategies BVBA 1 / 53 Overview c Copyright VAR Strategies BVBA 2 / 53 Time Value of Money c Copyright VAR Strategies BVBA 3 / 53 Time Value
More informationCVA. What Does it Achieve?
CVA What Does it Achieve? Jon Gregory (jon@oftraining.com) page 1 Motivation for using CVA The uncertainty of CVA Credit curve mapping Challenging in hedging CVA The impact of Basel III rules page 2 Motivation
More informationGuideline. Capital Adequacy Requirements (CAR) Chapter 4 - Settlement and Counterparty Risk. Effective Date: November 2017 / January
Guideline Subject: Capital Adequacy Requirements (CAR) Chapter 4 - Effective Date: November 2017 / January 2018 1 The Capital Adequacy Requirements (CAR) for banks (including federal credit unions), bank
More informationCalculating Counterparty Exposures for CVA
Calculating Counterparty Exposures for CVA Jon Gregory Solum Financial (www.solum-financial.com) 19 th January 2011 Jon Gregory (jon@solum-financial.com) Calculating Counterparty Exposures for CVA, London,
More informationCounterparty 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 informationDiscussion: Counterparty risk session
ISFA, Université Lyon 1 3rd Financial Risks International Forum Paris, 25 March 2010 Specic characteristics of counterparty risk Counterparty Risk is the risk that the counterparty to a nancial contract
More informationTraded Risk & Regulation
DRAFT Traded Risk & Regulation University of Essex Expert Lecture 14 March 2014 Dr Paula Haynes Managing Partner Traded Risk Associates 2014 www.tradedrisk.com Traded Risk Associates Ltd Contents Introduction
More informationModern 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 informationHedging 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 informationBasel Committee on Banking Supervision. Basel III counterparty credit risk - Frequently asked questions
Basel Committee on Banking Supervision Basel III counterparty credit risk - Frequently asked questions November 2011 Copies of publications are available from: Bank for International Settlements Communications
More informationPillar 3 and regulatory disclosures Credit Suisse Group AG 2Q17
Pillar 3 and regulatory disclosures Credit Suisse Group AG 2Q17 For purposes of this report, unless the context otherwise requires, the terms Credit Suisse, the Group, we, us and our mean Credit Suisse
More informationCounterparty Credit Risk
Counterparty Credit Risk The New Challenge for Global Financial Markets Jon Gregory ) WILEY A John Wiley and Sons, Ltd, Publication Acknowledgements List of Spreadsheets List of Abbreviations Introduction
More informationarxiv: 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 informationCVA 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 informationEconomic Scenario Generator: Applications in Enterprise Risk Management. Ping Sun Executive Director, Financial Engineering Numerix LLC
Economic Scenario Generator: Applications in Enterprise Risk Management Ping Sun Executive Director, Financial Engineering Numerix LLC Numerix makes no representation or warranties in relation to information
More informationAdvanced Quantitative Methods for Asset Pricing and Structuring
MSc. Finance/CLEFIN 2017/2018 Edition Advanced Quantitative Methods for Asset Pricing and Structuring May 2017 Exam for Non Attending Students Time Allowed: 95 minutes Family Name (Surname) First Name
More informationFrom Financial Risk Management. Full book available for purchase here.
From Financial Risk Management. Full book available for purchase here. Contents Preface Acknowledgments xi xvii CHAPTER 1 Introduction 1 Banks and Risk Management 1 Evolution of Bank Capital Regulation
More informationNo 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 informationCVA Capital Charges: A comparative analysis. November SOLUM FINANCIAL financial.com
CVA Capital Charges: A comparative analysis November 2012 SOLUM FINANCIAL www.solum financial.com Introduction The aftermath of the global financial crisis has led to much stricter regulation and capital
More informationAnalytical Pricing of CDOs in a Multi-factor Setting. Setting by a Moment Matching Approach
Analytical Pricing of CDOs in a Multi-factor Setting by a Moment Matching Approach Antonio Castagna 1 Fabio Mercurio 2 Paola Mosconi 3 1 Iason Ltd. 2 Bloomberg LP. 3 Banca IMI CONSOB-Università Bocconi,
More informationPricing Counterparty Risk in Today s Market: Current Practices
Pricing Counterparty Risk in Today s Market: Current Practices Introduction to the Panel Discussion Jon Gregory jon@oftraining.com Counterparty Risk is Changing (I) Before the credit crisis Most counterparty
More informationPricing Swaps Including Funding Costs
Pricing Swaps Including Funding Costs Antonio Castagna July 28, 2011 1 Introduction In Castagna [3] we have tried to correctly define the Debit Value Adjustment (DVA) of a derivative contract, coming up
More informationORE Applied: Dynamic Initial Margin and MVA
ORE Applied: Dynamic Initial Margin and MVA Roland Lichters QuantLib User Meeting at IKB, Düsseldorf 8 December 2016 Agenda Open Source Risk Engine Dynamic Initial Margin and Margin Value Adjustment Conclusion
More informationMATH FOR CREDIT. Purdue University, Feb 6 th, SHIKHAR RANJAN Credit Products Group, Morgan Stanley
MATH FOR CREDIT Purdue University, Feb 6 th, 2004 SHIKHAR RANJAN Credit Products Group, Morgan Stanley Outline The space of credit products Key drivers of value Mathematical models Pricing Trading strategies
More informationAdvanced Quantitative Methods for Asset Pricing and Structuring
MSc. Finance/CLEFIN 2017/2018 Edition Advanced Quantitative Methods for Asset Pricing and Structuring May 2017 Exam for Attending Students Time Allowed: 55 minutes Family Name (Surname) First Name Student
More informationBasel Committee on Banking Supervision. Frequently asked questions on market risk capital requirements
Basel Committee on Banking Supervision Frequently asked questions on market risk capital requirements January 2017 This publication is available on the BIS website (www.bis.org). Bank for International
More informationCredit Risk Management: A Primer. By A. V. Vedpuriswar
Credit Risk Management: A Primer By A. V. Vedpuriswar February, 2019 Altman s Z Score Altman s Z score is a good example of a credit scoring tool based on data available in financial statements. It is
More information(J)CIR(++) Hazard Rate Model
(J)CIR(++) Hazard Rate Model Henning Segger - Quaternion Risk Management c 2013 Quaternion Risk Management Ltd. All Rights Reserved. 1 1 2 3 4 5 6 c 2013 Quaternion Risk Management Ltd. All Rights Reserved.
More informationRegulatory Capital Pillar 3 Disclosures
Regulatory Capital Pillar 3 Disclosures December 31, 2016 Table of Contents Background 1 Overview 1 Corporate Governance 1 Internal Capital Adequacy Assessment Process 2 Capital Demand 3 Capital Supply
More informationBank 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 informationStrategies For Managing CVA Exposures
Strategies For Managing CVA Exposures Sebastien BOUCARD Global Head of CVA Trading www.ca-cib.com Contact Details Sebastien.boucard@ca-cib.com IMPORTANT NOTICE 2013 CRÉDIT AGRICOLE CORPORATE AND INVESTMENT
More informationStandardized Approach for Capitalizing Counterparty Credit Risk Exposures
OCTOBER 2014 MODELING METHODOLOGY Standardized Approach for Capitalizing Counterparty Credit Risk Exposures Author Pierre-Etienne Chabanel Managing Director, Regulatory & Compliance Solutions Contact Us
More informationGoldman Sachs Group UK (GSGUK) Pillar 3 Disclosures
Goldman Sachs Group UK (GSGUK) Pillar 3 Disclosures For the year ended December 31, 2013 TABLE OF CONTENTS Page No. Introduction... 3 Regulatory Capital... 6 Risk-Weighted Assets... 7 Credit Risk... 7
More informationAdvanced 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 informationThe Impact of Initial Margin
The Impact of Initial Margin Jon Gregory Copyright Jon Gregory 2016 The Impact of Initial Margin, WBS Fixed Income Conference, Berlin, 13 th October 2016 page 1 Working Paper The Impact of Initial Margin,
More informationFMFinancial. GTR Analytics. Traded Prices From Around The World. Financial Models and Solutions. Machineries
GTR Analytics Traded Prices From Around The World Powered by Financial Machineries Financial Models and Solutions OTC Regulation Regulation International and national regulation have introduced the obligation
More informationGRANULARITY ADJUSTMENT FOR DYNAMIC MULTIPLE FACTOR MODELS : SYSTEMATIC VS UNSYSTEMATIC RISKS
GRANULARITY ADJUSTMENT FOR DYNAMIC MULTIPLE FACTOR MODELS : SYSTEMATIC VS UNSYSTEMATIC RISKS Patrick GAGLIARDINI and Christian GOURIÉROUX INTRODUCTION Risk measures such as Value-at-Risk (VaR) Expected
More informationRegulatory Capital Pillar 3 Disclosures
Regulatory Capital Pillar 3 Disclosures June 30, 2015 Table of Contents Background 1 Overview 1 Corporate Governance 1 Internal Capital Adequacy Assessment Process 2 Capital Demand 3 Capital Supply 3 Capital
More informationarxiv: 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 informationOnline appendices from The xva Challenge by Jon Gregory. APPENDIX 8A: LHP approximation and IRB formula
APPENDIX 8A: LHP approximation and IRB formula i) The LHP approximation The large homogeneous pool (LHP) approximation of Vasicek (1997) is based on the assumption of a very large (technically infinitely
More informationCREDIT RATINGS. Rating Agencies: Moody s and S&P Creditworthiness of corporate bonds
CREDIT RISK CREDIT RATINGS Rating Agencies: Moody s and S&P Creditworthiness of corporate bonds In the S&P rating system, AAA is the best rating. After that comes AA, A, BBB, BB, B, and CCC The corresponding
More information3.4 Copula approach for modeling default dependency. Two aspects of modeling the default times of several obligors
3.4 Copula approach for modeling default dependency Two aspects of modeling the default times of several obligors 1. Default dynamics of a single obligor. 2. Model the dependence structure of defaults
More informationMulti-Curve Pricing of Non-Standard Tenor Vanilla Options in QuantLib. Sebastian Schlenkrich QuantLib User Meeting, Düsseldorf, December 1, 2015
Multi-Curve Pricing of Non-Standard Tenor Vanilla Options in QuantLib Sebastian Schlenkrich QuantLib User Meeting, Düsseldorf, December 1, 2015 d-fine d-fine All rights All rights reserved reserved 0 Swaption
More informationFinancial Risk Management
Financial Risk Management Professor: Thierry Roncalli Evry University Assistant: Enareta Kurtbegu Evry University Tutorial exercices #4 1 Correlation and copulas 1. The bivariate Gaussian copula is given
More informationTraded Risk & Regulation
DRAFT Traded Risk & Regulation University of Essex Expert Lecture 13 March 2015 Dr Paula Haynes Managing Director Traded Asset Partners 2015 www.tradedasset.com Traded Asset Partners Ltd Contents Introduction
More informationChallenges 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 informationDependence Modeling and Credit Risk
Dependence Modeling and Credit Risk Paola Mosconi Banca IMI Bocconi University, 20/04/2015 Paola Mosconi Lecture 6 1 / 53 Disclaimer The opinion expressed here are solely those of the author and do not
More informationGLOBAL CREDIT RATING CO. Rating Methodology. Structured Finance. Global Consumer ABS Rating Criteria Updated April 2014
GCR GLOBAL CREDIT RATING CO. Local Expertise Global Presence Rating Methodology Structured Finance Global Consumer ABS Rating Criteria Updated April 2014 Introduction GCR s Global Consumer ABS Rating Criteria
More informationBank of Japan Workshop - Credit Value Adjustment Trends. 14 th June 2010
Bank of Japan Workshop - Credit Value Adjustment Trends 14 th June 2010 Senior Director Theodoros Stampoulis Agenda 1. History 2. Why now Survey; background 2-1 Highlight 2-2 Key findings 3. Updated! CVA
More informationFinancial Risk Management
Financial Risk Management Professor: Thierry Roncalli Evry University Assistant: Enareta Kurtbegu Evry University Tutorial exercices #3 1 Maximum likelihood of the exponential distribution 1. We assume
More informationCVA and CCR: Approaches, Similarities, Contrasts, Implementation
BUILDING TOMORROW CVA and CCR: Approaches, Similarities, Contrasts, Implementation Part 1. Economic and Legal Background of Counterparty Risk Andrey Chirikhin Managing Director Head of CVA and CCR(IMM)
More informationOn Credit Valuation Adjustment (CVA) under Article 456(2) of Regulation (EU) No 575/2013 (Capital Requirements Regulation CRR)
EBA Report on CVA 25 February 2015 EBA Report On Credit Valuation Adjustment (CVA) under Article 456(2) of Regulation (EU) No 575/2013 (Capital Requirements Regulation CRR) and EBA Review On the application
More informationCounterparty Credit Risk, Collateral and Funding With Pricing Cases for all Asset Classes
Counterparty Credit Risk, Collateral and Funding With Pricing Cases for all Asset Classes Damiano Brigo, Massimo Morini and Andrea Pallavicini Order now, and save!! The book s content is focused on rigorous
More informationRISKMETRICS. Dr Philip Symes
1 RISKMETRICS Dr Philip Symes 1. Introduction 2 RiskMetrics is JP Morgan's risk management methodology. It was released in 1994 This was to standardise risk analysis in the industry. Scenarios are generated
More informationThe 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 informationPillar III Disclosure Report 2017
Pillar III Disclosure Report 2017 Content Section 1. Introduction and basis for preparation 3 Section 2. Risk management objectives and policies 5 Section 3. Information on the scope of application of
More informationThe Goldman Sachs Group, Inc. PILLAR 3 DISCLOSURES
The Goldman Sachs Group, Inc. PILLAR 3 DISCLOSURES For the period ended June 30, 2015 TABLE OF CONTENTS Page No. Index of Tables 1 Introduction 2 Regulatory Capital 5 Capital Structure 6 Risk-Weighted
More informationDynamic Wrong-Way Risk in CVA Pricing
Dynamic Wrong-Way Risk in CVA Pricing Yeying Gu Current revision: Jan 15, 2017. Abstract Wrong-way risk is a fundamental component of derivative valuation that was largely neglected prior to the 2008 financial
More informationTECHNICAL ADVICE ON THE TREATMENT OF OWN CREDIT RISK RELATED TO DERIVATIVE LIABILITIES. EBA/Op/2014/ June 2014.
EBA/Op/2014/05 30 June 2014 Technical advice On the prudential filter for fair value gains and losses arising from the institution s own credit risk related to derivative liabilities 1 Contents 1. Executive
More informationConsultation paper on CEBS s Guidelines on Liquidity Cost Benefit Allocation
10 March 2010 Consultation paper on CEBS s Guidelines on Liquidity Cost Benefit Allocation (CP 36) Table of contents 1. Introduction 2 2. Main objectives.. 3 3. Contents.. 3 4. The guidelines. 5 Annex
More informationChallenges in Counterparty Credit Risk Modelling
Challenges in Counterparty Credit Risk Modelling Alexander SUBBOTIN Head of Counterparty Credit Risk Models & Measures, Nordea November 23 th, 2015 Disclaimer This document has been prepared for the purposes
More informationThe Role of Counterparty Risk in the Credit Crisis
The Role of Counterparty Risk in the Credit Crisis Jon Gregory jon@oftraining.com www.oftraining.com Jon Gregory (jon@oftraining.com), Credit Risk Summit, 15 th October 2009 page 1 Jon Gregory (jon@oftraining.com),
More informationPILLAR 3 DISCLOSURES
The Goldman Sachs Group, Inc. December 2012 PILLAR 3 DISCLOSURES For the period ended June 30, 2014 TABLE OF CONTENTS Page No. Index of Tables 2 Introduction 3 Regulatory Capital 7 Capital Structure 8
More informationValuation of Forward Starting CDOs
Valuation of Forward Starting CDOs Ken Jackson Wanhe Zhang February 10, 2007 Abstract A forward starting CDO is a single tranche CDO with a specified premium starting at a specified future time. Pricing
More informationThe Goldman Sachs Group, Inc. PILLAR 3 DISCLOSURES
The Goldman Sachs Group, Inc. PILLAR 3 DISCLOSURES For the period ended September 30, 2016 TABLE OF CONTENTS Page No. Index of Tables 1 Introduction 2 Regulatory Capital 5 Capital Structure 6 Risk-Weighted
More informationFINCAD s Flexible Valuation Adjustment Solution
FINCAD s Flexible Valuation Adjustment Solution Counterparty credit risk measurement and valuation adjustment (CVA, DVA, FVA) computation are business-critical issues for a wide number of financial institutions.
More informationContagion models with interacting default intensity processes
Contagion models with interacting default intensity processes Yue Kuen KWOK Hong Kong University of Science and Technology This is a joint work with Kwai Sun Leung. 1 Empirical facts Default of one firm
More informationRisk e-learning. Modules Overview.
Risk e-learning Modules Overview Risk Sensitivities Market Risk Foundation (Banks) Understand delta risk sensitivity as an introduction to a broader set of risk sensitivities Explore the principles of
More informationBreak clauses and Derivatives Valuation
Break clauses and Derivatives Valuation Mizuho International Plc 23 rd - 25 th September 2013 Disclaimer This publication has been prepared by Gaël Robert of Mizuho International solely for the purpose
More information2nd Order Sensis: PnL and Hedging
2nd Order Sensis: PnL and Hedging Chris Kenyon 19.10.2017 Acknowledgements & Disclaimers Joint work with Jacques du Toit. The views expressed in this presentation are the personal views of the speaker
More informationCounterparty Risk Modeling for Credit Default Swaps
Counterparty Risk Modeling for Credit Default Swaps Abhay Subramanian, Avinayan Senthi Velayutham, and Vibhav Bukkapatanam Abstract Standard Credit Default Swap (CDS pricing methods assume that the buyer
More informationPILLAR 3 DISCLOSURES
. The Goldman Sachs Group, Inc. December 2012 PILLAR 3 DISCLOSURES For the period ended December 31, 2014 TABLE OF CONTENTS Page No. Index of Tables 2 Introduction 3 Regulatory Capital 7 Capital Structure
More informationDiscounting 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 informationMargining and Collateral as CCR Mitigation Tools
Netting Effects in Credit Counterparty Risk Margining and Collateral as CCR Mitigation Tools We present review of margining as Credit Counterparty Risk mitigation tool in OTC derivative trading based on
More informationIntroduction to Financial Mathematics
Department of Mathematics University of Michigan November 7, 2008 My Information E-mail address: marymorj (at) umich.edu Financial work experience includes 2 years in public finance investment banking
More informationBasel III Pillar 3 disclosures 2014
Basel III Pillar 3 disclosures 2014 In various tables, use of indicates not meaningful or not applicable. Basel III Pillar 3 disclosures 2014 Introduction 2 General 2 Regulatory development 2 Location
More informationThe Goldman Sachs Group, Inc. PILLAR 3 DISCLOSURES
The Goldman Sachs Group, Inc. PILLAR 3 DISCLOSURES For the period ended December 31, 2015 TABLE OF CONTENTS Page No. Index of Tables 1 Introduction 2 Regulatory Capital 5 Capital Structure 6 Risk-Weighted
More informationA study of the Basel III CVA formula
A study of the Basel III CVA formula Rickard Olovsson & Erik Sundberg Bachelor Thesis 15 ECTS, 2017 Bachelor of Science in Finance Supervisor: Alexander Herbertsson Gothenburg School of Business, Economics
More informationUnderstanding Bank Returns on Derivative Transactions with Corporate Counterparties. July 10, 2014
Understanding Bank Returns on Derivative Transactions with Corporate Counterparties July 10, 2014 Overview Recent regulatory changes, including Basel III, are have far reaching implications for banks pricing
More informationBook value (supervisory scope)
1.2. BANKING GROUP - MARKET RISKS As already highlighted in the introduction, the Intesa Sanpaolo Group policies relating to financial risk acceptance are defined by the Parent Company s Management Bodies,
More informationCredit Valuation Adjustment
Credit Valuation Adjustment Implementation of CVA PRMIA Credit Valuation Adjustment (CVA) CONGRESS IMPLEMENTATION UND PRAXIS Wolfgang Putschögl Köln, 20 th July 2011 CVA in a nutshell Usually pricing of
More informationCOUNTERPARTY RISK FOR CREDIT DEFAULT SWAPS
Updated version forthcoming in the International Journal of Theoretical and Applied Finance COUNTERPARTY RISK FOR CREDIT DEFAULT SWAPS impact of spread volatility and default correlation Damiano Brigo
More informationFunctional Training & Basel II Reporting and Methodology Review: Derivatives
Functional Training & Basel II Reporting and Methodology Review: Copyright 2010 ebis. All rights reserved. Page i Table of Contents 1 EXPOSURE DEFINITIONS...2 1.1 DERIVATIVES...2 1.1.1 Introduction...2
More informationRegulatory Capital Pillar 3 Disclosures
Regulatory Capital Pillar 3 Disclosures June 30, 2014 Table of Contents Background 1 Overview 1 Corporate Governance 1 Internal Capital Adequacy Assessment Process 2 Capital Demand 3 Capital Supply 3 Capital
More informationASTIN Helsinky, June Jean-Francois Decroocq / Frédéric Planchet
ASTIN Helsinky, June 2009 Jean-Francois Decroocq / Frédéric Planchet Euler Hermes Winter & Associés MODELING CREDIT INSURANCE 2 Credit insurance has some specificities Most Existing model derived from
More informationQuantitative and Qualitative Disclosures about Market Risk.
Item 7A. Quantitative and Qualitative Disclosures about Market Risk. Risk Management. Risk Management Policy and Control Structure. Risk is an inherent part of the Company s business and activities. The
More informationBasel III Pillar 3 disclosures
Basel III Pillar 3 disclosures 6M13 For purposes of this report, unless the context otherwise requires, the terms Credit Suisse, the Group, we, us and our mean Credit Suisse Group AG and its consolidated
More informationCentre for Central Banking Studies
Centre for Central Banking Studies Modelling credit risk Somnath Chatterjee CCBS Handbook No. 34 Modelling credit risk Somnath Chatterjee Somnath.Chatterjee@bankofengland.co.uk Financial institutions have
More informationEBF response to the EBA consultation on prudent valuation
D2380F-2012 Brussels, 11 January 2013 Set up in 1960, the European Banking Federation is the voice of the European banking sector (European Union & European Free Trade Association countries). The EBF represents
More information