Smoking Adjoints: Fast Evaluation of Monte Carlo Greeks

Michael Giles and Paul Glasserman

Contents

Introduction

Preface to Chapter 1

1.

Being Two-Faced over Counterparty Credit Risk

2.

Risky Funding: A Unified Framework for Counterparty and Liquidity Charges

3.

DVA for Assets

4.

Pricing CDSs’ Capital Relief

5.

The FVA Debate

6.

The FVA Debate: Reloaded

7.

Regulatory Costs Break Risk Neutrality

8.

Risk Neutrality Stays

9.

Regulatory Costs Remain

10.

Funding beyond Discounting: Collateral Agreements and Derivatives Pricing

11.

Cooking with Collateral

12.

Options for Collateral Options

13.

Partial Differential Equation Representations of Derivatives with Bilateral Counterparty Risk and Funding Costs

14.

In the Balance

15.

Funding Strategies, Funding Costs

16.

The Funding Invariance Principle

17.

Regulatory-Optimal Funding

18.

Close-Out Convention Tensions

19.

Funding, Collateral and Hedging: Arbitrage-Free Pricing with Credit, Collateral and Funding Costs

20.

Bilateral Counterparty Risk with Application to Credit Default Swaps

21.

KVA: Capital Valuation Adjustment by Replication

22.

From FVA to KVA: Including Cost of Capital in Derivatives Pricing

23.

Warehousing Credit Risk: Pricing, Capital and Tax

24.

MVA by Replication and Regression

25.

Smoking Adjoints: Fast Evaluation of Monte Carlo Greeks

26.

Adjoint Greeks Made Easy

27.

Bounding Wrong-Way Risk in Measuring Counterparty Risk

28.

Wrong-Way Risk the Right Way: Accounting for Joint Defaults in CVA

29.

Backward Induction for Future Values

30.

A Non-Linear PDE for XVA by Forward Monte Carlo

31.

Efficient XVA Management: Pricing, Hedging and Allocation

32.

Accounting for KVA under IFRS 13

33.

FVA Accounting, Risk Management and Collateral Trading

34.

Derivatives Funding, Netting and Accounting

35.

Managing XVA in the Ring-Fenced Bank

36.

XVA: A Banking Supervisory Perspective

37.

An Annotated Bibliography of XVA

The efficient calculation of price sensitivities continues to be one of the greatest practical challenges facing users of Monte Carlo methods in the derivatives industry. Computing Greeks is essential to hedging and risk management, but typically requires substantially more computing time than pricing a derivative. This chapter shows how an adjoint formulation can be used to accelerate the calculation of the Greeks. This method is particularly well suited to applications requiring sensitivities to a large number of parameters. Examples include interest rate derivatives requiring sensitivities to all initial forward rates and equity derivatives requiring sensitivities to all points on a volatility surface.

The simplest methods for estimating Greeks are based on finite-difference approximations, in which a Monte Carlo pricing routine is rerun multiple times with different settings of the input parameters in order to estimate sensitivities to the parameters. In the fixed-income setting, for example, this would mean perturbing each initial forward rate and then rerunning the Monte Carlo simulation to reprice a security or a whole book. The main virtues of this method are that it is

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