Handbook of Fixed-Income Securities
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Notes on Contributors xix
Preface xxv
Part I Fixed Income Markets 1
1 Fixed Income Markets: An Introduction 3
1.1 Introduction 3
1.2 U.S. Treasury Bills, Notes, and Bonds 7
1.3 Interest Rates, Yields, and Discounting 8
1.4 The Term Structure of Interest Rates 9
1.4.1 The Economics of the Nominal Yield Curve 9
1.4.2 The Expectations Hypothesis 13
1.4.3 Forward Rates as Expectation of Future Interest Rates? 16
1.4.4 Interpreting a Steepening of the Yield Curve 17
1.5 Pricing Coupon Notes and Bonds 17
1.5.1 Estimating the Zero-Coupon Discount Function 18
1.5.2 Data and Bond Illiquidity 19
1.6 Inflation-Protected Securities 19
1.7 Floating Rate Notes 22
1.8 Conclusion 24
References 24
2 Money Market Instruments 25
2.1 Overview of the Money Market 25
2.2 U.S. Treasury Bills 26
2.3 Commercial Paper 27
2.3.1 General Facts about Commercial Paper 27
2.3.2 Nonasset-Backed Commercial Paper 27
2.3.3 Asset-Backed Commercial Paper 28
2.4 Discount Window 29
2.5 Eurodollars 29
2.5.1 Eurodollar Futures 31
2.6 Repurchase Agreements 32
2.6.1 Types of Repos and Haircuts 32
2.6.2 Basic Forms of Repo Collateral 33
2.6.3 Repo Rates and Collateral Value Risks 34
2.6.4 The Run on Repo During the Financial Crisis 34
2.7 Interbank Loans 35
2.7.1 Federal Funds 35
2.7.2 Libor 37
2.7.3 Overnight Index Swaps and LIBOR-OIS Spreads 38
2.7.4 A Model of LIBOR-OIS Spreads 38
2.8 Conclusion 40
References 40
3 Inflation-Adjusted Bonds and the Inflation Risk Premium 41
3.1 Inflation-Indexed Bonds 41
3.1.1 Mechanics of TIPS 42
3.1.2 Valuing an Inflation-Indexed Bond 42
3.2 Inflation Derivatives 42
3.2.1 Constructing a Synthetic Nominal Treasury Bond with Inflation Swaps 42
3.3 No-Arbitrage Pricing 43
3.3.1 Zero-Coupon Bonds 43
3.4 Inflation Risk Premium 43
3.4.1 Determinants of the Inflation Risk Premium 44
3.5 A Look at the Data 45
3.5.1 Break-Even Rates 45
3.5.2 Inflation Swap Rates 46
3.5.3 Inflation Risk Premium 49
3.6 Conclusion 50
3.7 Appendix 50
3.7.1 Breeden-Lucas-Rubinstein Example 50
3.7.2 Disaster Risk 51
3.8 Data Appendix 51
References 52
4 Mortgage-Related Securities (MRSs) 53
4.1 Purpose of the Chapter 53
4.2 Introduction to MRSs 54
4.2.1 Mortgage and Securitization 54
4.2.2 The Cash Flows of Mortgage Pools 55
4.3 Valuation Overview 57
4.3.1 OAS, OAD, and Negative Convexity 58
4.3.2 Modeling Prepayment and Default 60
4.4 Analyzing an MRS 62
4.4.1 Modeling Prepayment and Default 62
4.4.2 Freddie Mac's STACR 67
4.4.3 Analyzing the STACR Series 2013-DN1 71
4.5 Summary 72
References 73
Part II Monetary Policy and Fixed Income Markets 75
5 Bond Markets and Monetary Policy 77
5.1 Introduction 77
5.2 High-Frequency Identification of Monetary Policy Shocks 78
5.2.1 Learning About Monetary Policy Surprises 79
5.2.2 The Impact on Treasury Bond Yields 81
5.2.3 The Timing of Expected Fed Interventions 82
5.3 Target Versus Path Shocks 84
5.3.1 The Economics of FOMC Meetings and Bond Yields 86
5.4 Conclusions 90
References 91
6 Bond Markets and Unconventional Monetary Policy 93
6.1 Introduction 93
6.2 Unconventional Policies: The Fed, ECB, and BOE 94
6.2.1 Federal Reserve Operations 94
6.2.2 Bank of England Operations 96
6.2.3 European Central Bank Operations 97
6.3 Unconventional Policies: A Theoretical Framework 101
6.3.1 Portfolio Balance (Duration) Channel 102
6.3.2 Signaling Channel 103
6.3.3 Credit and Capital Constraint Channel 103
6.3.4 Preferred Habitat and Asset Scarcity Channel 104
6.4 Unconventional Policies: The Empirical Evidence 104
6.4.1 The Treasury Bond Market 104
6.4.2 The MBS Market 113
6.4.3 How Persistent is the Effect? 115
6.5 Conclusions 115
References 116
Part III Interest Rate Risk Management 117
7 Interest Rate Risk Management and Asset Liability Management 119
7.1 Introduction 119
7.2 Literature Review 120
7.3 Interest Rate Risk Measures 120
7.3.1 Duration 121
7.3.2 Convexity 122
7.3.3 Key Rate Duration 123
7.3.4 Principal Component Analysis and Factor Duration 123
7.4 Application to Asset Liability Management 127
7.4.1 Nature of Liabilities 127
7.4.2 Cash Flow Matching 128
7.4.3 Classic Immunization and Duration Matching 130
7.4.4 Key Rate Duration Matching 133
7.4.5 Factor Duration Matching 137
7.5 Backtesting ALM Strategies 141
7.6 Liability Hedging and Portfolio Construction 142
7.7 Conclusions 144
7.8 Appendix: The Implementation of Principal Component Analysis 145
References 146
8 Optimal Asset Allocation in Asset Liability Management 147
8.1 Introduction 147
8.2 Yield Smoothing 150
8.3 ALM Problem 151
8.3.1 Return and Yield Dynamics 152
8.3.2 Preferences 153
8.3.3 Constraints 154
8.3.4 Data Description and Estimation 155
8.4 Method 155
8.5 Single-Period Portfolio Choice 156
8.5.1 ALM with a VaR Constraint 156
8.5.2 ALM with AFCs 158
8.6 Dynamic Portfolio Choice 160
8.6.1 Welfare and Portfolio Implications of Yield Smoothing 160
8.6.2 Hedging Demands and Regulatory Constraints 161
8.7 Conclusion 164
8.8 Appendix: Return Model Parameter Estimates 165
8.9 Appendix: Benchmark Without Liabilities 165
References 166
Part IV the Predictability of Bond Returns 169
9 International Bond Risk Premia 171
9.1 Introduction 171
9.2 Literature Review 172
9.3 Notation and International Bond Market Data 174
9.3.1 Notation 174
9.3.2 International Bond Market Data 174
9.4 Unconditional Risk Premia 174
9.4.1 A Long-Term Perspective 174
9.4.2 More Recent Evidence 176
9.5 Conditional Risk Premia 177
9.5.1 Local Predictors of Returns 178
9.5.2 Global Predictors of Returns 182
9.6 Understanding Bond Risk Premia 185
9.6.1 Links to Economic Growth 185
9.6.2 State Dependency 187
9.7 Conclusion and Outlook 187
References 189
10 Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity 191
10.1 Introduction 191
10.2 Brief Literature Review 192
10.3 Bond Data and Definitions 193
10.3.1 Bond Notation and Definitions 193
10.3.2 Yield Data 194
10.4 Estimating the Liquidity Differential Between Inflation-Indexed and Nominal Bond Yields 194
10.4.1 Estimation Strategy 196
10.4.2 Data on Liquidity and Inflation Expectation Proxies 197
10.4.3 Estimating Differential Liquidity 197
10.5 Bond Excess Return Predictability 201
10.5.1 Economic Significance of Bond Risk Premia 205
10.6 Conclusion 206
References 208
11 U.S. Treasury Market: The High-Frequency Evidence 210
11.1 Introduction 210
11.2 The U.S. Treasury Markets During the Financial Crisis 211
11.2.1 Yields 211
11.2.2 Volatility 212
11.2.3 Off-the-Run/On-the-Run Yield Spread 213
11.2.4 Trading Volume and Price Impact 214
11.2.5 Fails 215
11.2.6 Intraday Evidence on March 18, 2009 215
11.2.7 Summary 216
11.3 The Reaction of Bond Prices and Interest Rates to Macroeconomic News 217
11.3.1 Level Effects 217
11.3.2 The Impact of Monetary Policy 218
11.3.3 Realized-Volatility Patterns 219
11.3.4 Macro News and Option-Implied Volatilities 220
11.3.5 ARCH and GARCH Effects 222
11.3.6 Jumps 224
11.3.7 Summary 227
11.4 Market-Microstructure Effects 228
11.4.1 Microstructure Effects in the Cash Market 228
11.4.2 Joint Microstructure Effects in the Cash Market and Futures Markets 231
11.4.3 Summary 232
11.5 Bond Risk Premia 232
11.5.1 Daily Evidence 232
11.5.2 Intraday Evidence 233
11.5.3 Summary 234
11.6 The Impact of High-Frequency Trading 234
11.6.1 The Effects of HFT on Liquidity, Volatility, and Risk Premia 234
11.6.2 Summary 236
11.7 Conclusions 236
References 236
Part V Advanced Topics on Term Structure Models and Their Estimation 239
12 Structural Affine Models for Yield Curve Modeling 241
12.1 Purpose and Structure of This Chapter 241
12.2 Structural Models 242
12.3 A Simple Taxonomy 242
12.4 Why do we Need No-Arbitrage Models After All? 243
12.5 Affine Models and the Drivers of The Yield Curve 244
12.5.1 Expectations 244
12.5.2 Term (Risk) Premia 244
12.5.3 Convexity 246
12.6 Introducing No-Arbitrage 247
12.7 Which Variables Should One use? 247
12.8 Risk Premia Implied by Affine Models with Constant Market Price of Risk 249
12.9 Testable Predictions: Constant Market Price of Risk 251
12.10 What Do We Know About Excess Returns? 251
12.11 Understanding the Empirical Results on term Premia 252
12.12 Enriching the First-Generation Affine Models 254
12.13 Latent Variables: The D'Amico, Kim, and Wei Model 254
12.14 From Linear Regressors to Affine Models: the ACM Approach 255
12.15 Affine Models using Principal Components as Factors 256
12.16 The Predictions from the "Modern" Models 258
12.17 Conclusions 261
12.17.1 Models as Enforcers of Parsimony and Builders of Confidence 261
12.17.2 Models as Enforcers of Cross-Sectional Restrictions 262
12.17.3 Models as Revealers of Forward-Looking Informations 262
12.17.4 Models as Enhancers of Understanding 262
References 263
13 The Econometrics of Fixed-Income Markets 265
13.1 Introduction 265
13.2 Different Types of Term Structure Models 266
13.2.1 Factor Models 266
13.2.2 Observable Factors 267
13.2.3 Latent Factors: Filtering versus Indirect Observation 267
13.2.4 Macroeconomic Models 267
13.2.5 Affine Models 268
13.2.6 Yield-Based Models 268
13.2.7 Forward-Based Models 269
13.3 Parametric Estimation Methods 269
13.3.1 GMM 270
13.3.2 Maximum Likelihood 270
13.3.3 QML 271
13.3.4 Efficient Method of Moments 271
13.3.5 Estimation Bias in Mean-Reversion Parameters 272
13.4 Maximum Likelihood Estimation 272
13.4.1 Observed State Variables 272
13.4.2 Latent State Variables 273
13.5 Constructing the Likelihood Function: Expansion of the Transition Density 275
13.5.1 Reducibility 276
13.5.2 The Irreducible Case 277
13.6 Concluding Remarks 278
References 279
14 Recent Advances in Old Fixed-Income Topics: Liquidity, Learning, and the Lower Bound 282
14.1 Introduction 282
14.2 Liquidity 283
14.2.1 Bills, Notes, and Bonds 283
14.2.2 Market Liquidity and Short-Selling Costs 284
14.2.3 Hedging Demand 286
14.2.4 Risky Arbitrage 287
14.2.5 Segmented Markets and Preferred Habitats 287
14.2.6 Funding Risk 288
14.2.7 Implication for Term Structure Models 290
14.3 Learning 291
14.3.1 Yield Survey Forecasts 292
14.3.2 Affine Term Structure Models 293
14.3.3 Spanning Survey Forecasts 297
14.3.4 Adaptive Learning and Survey Forecasts 299
14.3.5 Equilibrium Models of the Term Structure 300
14.4 Lower Bound 301
14.4.1 Square-Root and Autoregressive Gamma Models 301
14.4.2 Black (1995) - Tobin (1958) 303
14.4.3 No-Dominance Term Structure Models 305
14.4.4 Recent Empirical Results 306
14.5 Conclusion 309
14.6 Appendix: Moments of Truncated Bivariate Distribution 310
References 311
15 The Economics of the Comovement of Stocks and Bonds 313
15.1 Introduction 313
15.2 A Brief Literature Survey 313
15.3 The Stock-Bond Covariance and Learning about Fundamentals 315
15.3.1 Investors' Beliefs About Composite Regimes 316
15.3.2 Valuations and the "Fed Model" 316
15.3.3 Explaining the Time Variation in the Stock-Bond Covariance 318
15.4 Beliefs from Surveys and from the Model 319
15.5 Survey and Model Beliefs and the Stock-Bond Covariance 319
15.6 Some International Evidence 322
15.7 Summary 325
References 325
Part VI Derivatives: Markets and Pricing 327
16 Interest Rate Derivatives Products and Recent Market Activity in the New Regulatory Framework 329
16.1 Introduction 329
16.2 Background on the New Derivatives Regulatory Framework 331
16.2.1 Clearing 332
16.2.2 Execution 333
16.2.3 Reporting 333
16.3 Exchange-Traded Derivatives 335
16.3.1 Major Products 335
16.3.2 Execution 336
16.3.3 Clearing 336
16.3.4 Market Activity 339
16.4 Noncleared Swaps 341
16.4.1 Major Products 341
16.4.2 Execution 342
16.4.3 Credit Risk Mitigation 345
16.4.4 Market Activity 351
16.5 Cleared Swaps 354
16.5.1 Major Products 354
16.5.2 Market Activity 355
16.6 Comparative Market Activity Across Execution Venues 360
16.6.1 OTC versus Exchange-Traded Interest Rate Derivatives 360
16.6.2 Bilateral versus SEF Execution of OTC Interest Rate Derivatives 363
16.7 Liquidity Fragmentation in Nondollar Swaps 366
16.8 Prospects for the Future 368
16.8.1 Cleared Swaps and Exchange-Traded Interest Rate Derivatives 369
16.8.2 Swap Futures 370
16.8.3 Noncleared Swaps and End Users 370
16.9 Appendix: The New Regulatory Framework for Interest Rate Derivatives in the United States and European Union 371
16.9.1 Classifications of Market Participants 371
16.9.2 Clearing 373
16.9.3 Execution 375
16.9.4 Reporting 376
16.9.5 Margin Requirements for Noncleared Swaps 377
16.9.6 Capital Requirements for Noncleared Swaps 379
16.9.7 Cross-Border and Extraterritoriality Issues 381
References 385
17 Risk-Neutral Pricing: Trees 389
17.1 Introduction 389
17.2 Binomial Trees 389
17.2.1 One-Step Binomial Trees 389
17.2.2 The Market Price of Risk 393
17.3 Risk-Neutral Pricing on Multistep Trees 394
17.3.1 Calibration of Risk-Neutral Trees to the Yield Curve 395
17.3.2 The Pricing of European Options 397
17.3.3 The Pricing of American Options 400
17.4 From Diffusion Models to Binomial Trees 403
17.4.1 The Hull and White Model 405
17.5 Trinomial Trees 406
17.5.1 Calibration to the Yield Curve 407
17.5.2 Pricing Bermudan Contracts Using the Trinomial Tree 410
17.5.3 Calibration to the Volatility Curve 412
References 413
18 Discounting and Derivative Pricing Before and After the Financial Crisis: An Introduction 414
18.1 Introduction 414
18.2 Forward Rate Agreements (FRAs) 415
18.2.1 Forward Rates 417
18.2.2 Forward Rates after the Crisis 418
18.2.3 A Simple Explanation for the "Arbitrage" 420
18.3 Overnight Index Swaps (OISs) 422
18.3.1 OIS Discount Curve 424
18.4 LIBOR-Based Swaps 424
18.4.1 LIBOR Discount Curve with Single-Curve Pricing 426
18.5 The Crisis and the Double-Curve Pricing of LIBOR-Based Swaps 426
18.5.1 Extracting FRA Rates from Swap Quotes 428
18.5.2 Extracting the Discount Curve from FRA Rates 428
18.5.3 Summing Up 429
18.6 The Pricing of LIBOR-Based Interest Rate Options 430
18.6.1 Black's Option Pricing Formula 430
18.6.2 Caps and Floors before and after the Crisis 431
18.6.3 Swaptions before and after the Crisis 432
18.7 Conclusions 433
References 433
Part VII Advanced Topics in Derivatives Pricing 435
19 Risk-Neutral Pricing: Monte Carlo Simulations 437
19.1 Introduction 437
19.2 Risk-Neutral Pricing 437
19.2.1 Interest Rate Models 440
19.2.2 The Market Price of Risk 441
19.2.3 Valuation under P and under Q 441
19.2.4 Multifactor Models 442
19.3 Risk-Neutral Pricing: Monte Carlo Simulations 446
19.3.1 Discretization of the Vasicek Model 447
19.3.2 Discretization of the Cox-Ingersoll-Ross Model 448
19.3.3 Interest Rate Modeling at the Zero Lower Bound 451
19.4 Valuation by Monte Carlo Simulation 451
19.4.1 Valuation of Securities with Payoff at Fixed Date 452
19.4.2 mc Valuation of Callable Bonds 455
19.4.3 mc Valuation of Securities with American or Bermudan Exercise Style 456
19.5 Monte Carlo Simulations in Multifactor Models 461
19.5.1 Discretization Procedure of the Affine Factor Models 462
19.5.2 mc Simulations for Callable Securities in Multifactor Models 462
19.6 Conclusion 467
References 467
20 Interest Rate Derivatives and Volatility 469
20.1 Introduction 469
20.2 Markets and the Institutional Context 469
20.2.1 Market Size 469
20.2.2 OTC IRD Trading and Volatility 471
20.2.3 Exchange-Listed IRD Trading and Volatility 472
20.2.4 Recent Developments in the IRD Market 473
20.3 Dissecting the Instruments 473
20.3.1 Government Bonds 474
20.3.2 Time Deposits 476
20.3.3 Forwards Rate Agreements and Interest Rate Swaps 476
20.3.4 Caps, Floors, and Swaptions 478
20.4 Evaluation Paradigms 479
20.4.1 Models of the Short-term Rate 479
20.4.2 No-Arbitrage Models 481
20.4.3 Volatility 485
20.5 Pricing and Trading Volatility 487
20.5.1 Standard Volatility Trading Practice 488
20.5.2 An Introduction to Interest Rate Variance Swaps 489
20.5.3 Pricing Volatility in Three Markets 497
20.5.4 Current Forward-Looking Indexes of IRV 502
20.5.5 Products on IRV Indexes 505
20.6 Conclusions 507
20.7 Appendix 508
References 512
21 Nonlinear Valuation under Margining and Funding Costs with Residual Credit Risk: A Unified Approach 514
21.1 Introduction 514
21.2 Collateralized Credit and Funding Valuation Adjustments 516
21.2.1 Trading under Collateralization and Closeout Netting 517
21.2.2 Trading under Funding Risk 520
21.3 General Pricing Equation Under Credit, Collateral, and Funding 522
21.3.1 Discrete-Time Solution 523
21.3.2 Continuous-Time Solution 524
21.4 Numerical Results: Extending the Black-Scholes Analysis 527
21.4.1 Monte Carlo Algorithm 527
21.4.2 Market, Credit, and Funding Risk Specification 529
21.4.3 Preliminary Analysis without Credit Risk and with Symmetric Funding Rates 529
21.4.4 Full Analysis with Credit Risk, Collateral, and Funding Costs 531
21.4.5 Nonlinearity Valuation Adjustment 533
21.5 Extensions 535
21.6 Conclusions: Bilateral Prices or Nonlinear Values? 536
References 537
Part VIII Corporate and Sovereign Bonds 539
22 Corporate Bonds 541
22.1 Introduction 541
22.2 Market and Data 542
22.2.1 Data on Bond Characteristics 542
22.2.2 Data on Market Prices 542
22.2.3 Understanding Market Data from TRACE 543
22.3 A Very Simple Model 544
22.3.1 The Credit Spread Arising from Expected Loss 545
22.3.2 Adding a Risk Premium 545
22.4 Structural Models 546
22.4.1 Merton's Model with Beta 546
22.4.2 Bankruptcy Costs 549
22.4.3 Early Default 550
22.5 Reduced-form Models 550
22.5.1 A Useful Approximation 552
22.5.2 Closed-Form Solutions 553
22.6 Risk Premia in Intensity Models 554
22.7 Dealing with Portfolios 556
22.8 Illiquidity as a Source of Spreads 557
22.9 Some Additional Readings 558
22.10 Conclusion 559
References 559
23 Sovereign Credit Risk 561
23.1 Introduction 561
23.2 Literature Review 563
23.3 Modeling Sovereign Default 564
23.3.1 Risk-Neutral Pricing 564
23.3.2 Pricing Sovereign Credit Default Swaps 567
23.3.3 Pricing in a Lognormal Model 568
23.4 Credit Risk Premia 568
23.5 Estimating Intensity Models 569
23.6 Application to Emerging Markets 570
23.6.1 Credit Markets of Emerging Economies 571
23.6.2 Credit Risk Premia in Emerging Credit Markets 572
23.7 Application to the European Debt Crisis 575
23.7.1 Credit Risk Premia in the Eurozone 578
23.8 Conclusion 580
23.9 Appendix: No Arbitrage Pricing 580
23.9.1 The Risk-Neutral Default Intensity 583
References 584
Index 587
Notes on Contributors
Yacine Aït-Sahalia is the Otto A. Hack '03 Professor of Finance and Economics at Princeton University. He served as the inaugural Director of the Bendheim Center for Finance from 1998 to 2014. He was previously an Assistant Professor (1993-1996), Associate Professor (1996-1998), and Professor of Finance (1998) at the University of Chicago&s Graduate School of Business. He is a Fellow of the Econometric Society, a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, an Alfred P. Sloan Foundation Research Fellow, a Fellow of the Guggenheim Foundation, and a Research Associate for the National Bureau of Economic Research (NBER). His research has been widely published in the leading academic journals in finance, econometrics, and statistics. He co-wrote the book High Frequency Financial Econometrics, published in 2014, with Jean Jacod. He is the recipient of the 1997 Michael Brennan Award, the 1998 Cornerstone Research Award, the 2001 FAME Research Award, and the 2003 Dennis J. Aigner Award. He recently served as the editor of the Review of Financial Studies and currently serves as the editor of the Journal of Econometrics. He received his PhD in Economics from the Massachusetts Institute of Technology in 1993 and is a graduate of France&s Ecole Polytechnique.
Pierluigi Balduzzi is Professor of Finance at the Carroll School of Management at Boston College, where he teaches Masters and PhD-level courses in Finance. His main area of research is empirical asset pricing, with a focus on the fixed-income markets. His research has appeared in leading finance and economic journals, such as the Journal of Finance, the Journal of Financial Economics, and the American Economic Review. He currently serves as an Associate Editor of the Journal of Business and Economic Statistics and the Journal of Financial Econometrics.
Jules van Binsbergen is an Associate Professor at the Wharton School of the University of Pennsylvania and conducts theoretical and empirical research in finance. His current work focuses on asset pricing, in particular the relationship between financial markets and the macroeconomy, and the organization, skill and performance of financial intermediaries. Some of his recent research focuses on measuring the skill of mutual fund managers, the term structure of cash flow growth and stock return predictability, and the implications of good-specific habit formation for asset prices. His research has appeared in leading academic journals, such as the American Economic Review, the Journal of Finance, the Journal of Financial Economics, and the Journal of Monetary Economics. He received his PhD from the Fuqua School of Business at Duke University. He previously taught at the Stanford Graduate School of Business and the Kellogg School of Management.
Michael W. Brandt is the Kalman J. Cohen Professor of Business Administration at the Fuqua School of Business of Duke University. He conducts empirical and theoretical research in finance. His current work focuses on quantitative portfolio management, risk management, currency and fixed-income markets, and financial econometrics. Professor Brandt&s research has appeared in leading academic journals, including the American Economic Review, Journal of Business, Journal of Finance, Journal of Financial Economics, Journal of Monetary Economics, and Review of Financial Studies. He has served as co-editor of the Review of Finance, the official journal of the European Finance Association, and as associate editor of the Journal of Finance, the official journal of the American Finance Association. He is also a Faculty Research Associate of the NBER. Before joining Fuqua in 2003, Professor Brandt was at the Wharton School of the University of Pennsylvania for 6 years.
Professor Damiano Brigo is Chair in Mathematical Finance and Stochastic Analysis at the Department of Mathematics at Imperial College London. Former roles include Gilbart Professor at King&s College London, Managing Director at Fitch Ratings, Director of the Capco Institute, and Head of Credit Models in Banca IMI. Dr Brigo published 80+ works in Quantitative Finance, Probability and Statistics, and field reference books in interest rate and credit modeling, and he is in the editorial board of several journals including Mathematics of Control, Signals and Systems and the International Journal of Theoretical and Applied Finance. Dr Brigo has been the most cited author in Risk Magazine in 2006, 2010, and 2012. His interests include valuation, funding, risk, credit, and stochastic differential geometry. Dr Brigo holds a PhD in Mathematics.
Andrea Buraschi is the Chair in Finance at Imperial College London and a research fellow at the Center for Economic and Policy Research. He has been a visiting professor at the University of Chicago Booth and Columbia University. He earned his PhD from the University of Chicago specializing in Financial Economics. His area of research is empirical asset pricing and general equilibrium models of bond and derivative markets. His work has appeared in several publications, including the Journal of Finance, the Journal of Financial Economics, Review of Financial Studies, Journal of Derivatives, European Financial Management, and the Journal of Banking and Finance. Prof. Buraschi is a recipient of several best paper and teaching awards and serves as Associate Editor for the Review of Finance.
Christopher Culp is a Senior Advisor with Compass Lexecon, Adjunct Professor at the Swiss Finance Institute, Research Fellow at the Johns Hopkins Institute for Applied Economics, and Honorarprofessor at Universität Bern in the Institut für Finanzmanagement. His research and teaching specializations include derivatives, structured finance, insurance, risk management, credit markets, and clearing/settlement, and he also provides advisory consulting services and testimonial expertise in these areas. He has written four books, co-edited two books (one with Merton Miller and the other with William Niskanen), and has authored numerous articles on the same topics. Dr Culp is on the editorial advisory boards of the Journal of Applied Corporate Finance, the Journal of Structured Finance, and Futures Industry magazine, and was previously on the editorial advisory boards of Derivatives Quarterly (where he was also co-editor), FMA Online, and the Journal of Risk Finance. He holds a PhD in finance from the University of Chicago&s Booth School of Business, where he was also an Adjunct Professor of Finance from 1998 to 2013.
Magnus Dahlquist is the Peter Wallenberg Professor of Finance at the Stockholm School of Economics (SSE). He is also a Research Fellow with the Centre for Economic Policy Research (CEPR), London, and with the Network for Studies on Pensions, Aging and Retirement (NETSPAR) in the Netherlands. Dahlquist&s research interests lie in asset management, asset pricing, and international finance. His current research focuses on (i) individuals& and institutions& investment behavior and the design of pension plans, (ii) trading strategies in the bond and currency markets and their relation to fundamentals, and (iii) performance evaluation and practical problems related to portfolio selection. Dahlquist has been an advisor to several financial institutions as well as government authorities.
Alexander David, PhD, is the Haskayne Research Professor of Finance at the Haskayne School of Business at the University of Calgary. Before joining Haskayne, he worked at the Federal Reserve Board in Washington DC as a staff economist and the Olin School of Business at Washington University in St Louis. He has taught classes in financial risk management, advanced corporate finance, options and futures, energy finance, investments, and asset pricing in the undergraduate, MBA, Executive MBA, and PhD programs. His main research interest is the modeling of changing investors& uncertainty about the state of economic fundamentals and their impact on asset prices. His research has been published in leading academic journals such as the Journal of Political Economy, Journal of Finance, Review of Financial Studies, and Journal of Financial Economics.
Jens Dick-Nielsen is Associate Professor of Finance at the Center for Financial Frictions (FRIC), Department of Finance, Copenhagen Business School where he teaches Masters and Executive-level courses in finance and credit risk modeling. His research on credit risk and liquidity risk has impacted European Banking regulation and has been published among others in the Journal of Financial Economics and the Journal of Fixed Income.
Jefferson Duarte is the Gerald D. Hines Professor of Real Estate Finance at the Jesse H. Jones Graduate School of Business at Rice University. He is an expert on fixed-income and mortgage-backed securities. His research has received many awards including the prestigious Fama-DFA prize for the best asset pricing paper published in the Journal of Financial Economics. Dr. Duarte&s research has also been covered in United States and international media, including the following papers and magazines: The Wall Street Journal, Financial Times, and The Economist. Prior to joining academia, Dr. Duarte worked at the proprietary trading desk of JP Morgan Chase in...
