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Tim Xiao: This paper argues that the reduced-form jump diffusion model may not be appropriate for credit
risk modeling. To correctly value hybrid defaultable financial instruments, e.g., convertible bonds, we
present a new framework that relies on the probability distribution of a default jump rather than the
default jump itself, as the default jump is usually inaccessible. The model is quite accurate. A prevailing
belief in the market is that convertible arbitrage is mainly due to convertible underpricing. Empirically,
however, we do not find evidence supporting the underpricing hypothesis. Instead, we find that
convertibles have relatively large position gammas. As a typical convertible arbitrage strategy employs
delta-neutral hedging, a large positive gamma can make the portfolio high profitable, especially for a
large movement in the underlying stock price.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2273296
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Tim Xiao: This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2267517
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Tim Xiao: This article presents a comprehensive framework for valuing financial instruments subject to credit risk and collateralization. In particular, we focus on the impact of default dependence on asset pricing, as correlated default risk is one of the most pervasive threats to financial markets. Some well-known risky valuation models in the markets can be viewed as special cases of this framework. We introduce the concept of comvariance (or comrelation) into the area of credit risk modeling to capture the default relationship among three or more parties. Accounting for default correlations and comrelations becomes important, especially during the credit crisis. Moreover, we find that collateralization works well for financial instruments subject to bilateral credit risk, but fails for ones subject to multilateral credit risk.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2268179
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Tim Xiao: This paper presents a new framework for credit value adjustment (CVA) that is a relatively new area of financial derivative modeling and trading. In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the prices of risky contracts are normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2267508
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notes
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Paul Soderlind
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Lecture of Prof. H. J. Herrmann

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AFFINE MODELS IN MATHEMATICAL FINANCE:
AN ANALYTICAL APPROACH
ARTUR SEPP

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book by Robert Merton
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by Nicolas Privault
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by Claus Munk
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well illustrated with examples and java code. enjoy!
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Fontier in QF
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well, it is a syllabus book, but i use it to print out concise formula pages, and hang it as a cheat sheet in my cubicle. enjoy!
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includes Hull, Brigo, Rebonato, Tavella and much more..
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Dozent Prof. Dr. Didier Sornette
A set of 7 lectures on Financial Market Risks
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A book on lot of topics in QF by Wolfgang hardle, Torsten Kleinow, Gerhard Stahl
Examples are based on a proprietery software called quantlet, can be downloaded from http://fedc.wiwi.hu-berlin.de/xplore.php

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Checkout his interesting home page: http://www.mathematik.uni-kl.de/~sass/

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Quantitative Analysis of Financial Markets II (MFM)
Vance Martin
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A nice set of notes, as good as a book prepared by Marcel B. Finan
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excellent set of lecture notes by Kyriakos Chourdakis:
FINANCIAL ENGINEERING
A brief introduction using the Matlab system

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by Christian-Oliver Ewald

Abstract:
These are my Lecture Notes for a course in Discrete Time Finance which I taught in the Winter term 2005 at the University of Leeds. I am aware that the notes are not yet free of error and the manuscrip needs further improvement. I am happy about any comment on the notes. Please send your comments via e-mail to ce16@st-andrews.ac.uk.

Keywords: Discrete Time Finance, Mathematical Finance
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I am not able to get the main page. There are around 20 lectures in this directory, as well as when you click on each of the links with [DIR] Icon.
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by Andrzej Palczewski

Slides (in PDF format)

1. Binomial model.
2. Greeks.
3. Random number generators.
4. Correlated Brownian motions.
5. Monte Carlo simulation.
6. Crash course in stochastic analysis.
7. Numerical schemes for SDEs.
8. Black-Scholes PDE.
9. Numerical methods for PDEs.
10. Numerical methods for American options.
11. Implied volatility. The vanna-volga method and beyond.


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Course Description
Lecture 1: The Fundamental Theorem
Lecture 2: Multiperiod Models
Lecture 3&4: Martingales
Lecture 5: Brownian Motion
Lecture 6: The Ito Integral
Lecture 7: The Black-Scholes Formula
Lecture 8: The Cameron-Martin Formula and Barrier Options
Lecture 9: Foreign Exchange
Lecture 10: Girsanov's Theorem

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by A. G. (Tassos) Malliaris
This chapter introduces the reader to the Black-Scholes -Merton model by identifying its assumptions and illustrating its mathematical derivation using intuitive financial reasoning. Numerical examples are also presented to help the reader understand practical aspects of this celebrated model. The analytical power of the Black-Scholes-Merton model comes from the brilliant assumption that the returns of the underlying asset follow an Ito process. This assumption allowed financial theorists to use financial reasoning with an extensive inventory of mathematical techniques to solve successfully for the pricing of contingent claims. Unlike many other scientific discoveries that are not often easily modified, the Black-Scholes-Merton model has been successfully extended and adapted to numerous underlying assets, thus offering pricing solutions as benchmark prices. This in turn has encouraged the development and implementation of numerous trading strategies that involved hedging, speculation and arbitrage.
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thesis by Joonghee Huh
In this thesis, we consider computational methods of
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notes by Nevena Selic and Prof. David Taylor
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book by Andrew J.G. Cairns
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a paper of notes by Vicky Henderson
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slides and some example code by Paolo Foschi
Table of Contents I
1. Binomial Model
Mono-period binomial model
Parameter Calibration
Early exercise
Computational and numerical issues
2 Finite Differences
Discretization
Explicit Method
Implicit Method
The Black and Scholes PDE
Bi-dimensional equations
3 Monte Carlo
Numerical Integration
Monte Carlo Method
Generating Random Variables
Variance Reduction Techniques
SDE Integration
4 Fourier Transform methods
Exponential Damping
Time Value Approach
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could not get info on title and author
I found this while surfing, looks interesting. Discusses several topics in Quant finance
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Lecture Notes, Intro to MBS, Intro to FI Securities, FRM, Term Structure of Interest Rates, MBS Lect Notes 3.4, Agenda

Excel Files, FRM, TS/Duration/Immunization, TS Calcs, PT, CMO, HL 4Y, HL 15Y, HL CMO, HL IO OAS,
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Lecture notes on Mathematical Finance by Alvaro Cartea
Has notes,slides and exercises
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very good set of notes. will prove handy
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a set of lectures notes.
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good introductory book for beginners
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slides and notes by Bruno Dupire,Peter Carr
Lecture Notes:
Lecture 2.1: Binomial Hedging
Lecture 2.2: Poisson Hedging
Lecture 4: Replicating Risky Bonds
Lecture 5: Black's Model With Default
Slides:
Slides 2.1: Binomial Hedging
Slides 2.2: Poisson Hedging
Slides 3: Review of Stochatic Calculus
Slides 4: Replicating Risky Bonds
Slides 5.1: Black's Model with Default
Slides 5.2: Merton's Jump Diffusion Model
Slides 6: Choice of Numeraire
Slides 7: Modeling Volatility (pdf format)
Slides 7: Modeling Volatility (ppt format)
Slides 8: Robust Dynamic Spanning
Slides 9: More On Volatility
Slides 10.1: Intro to Interest Rate Models
Slides 10.2: The Vasicek Model
Slides 10.3: The Hull-White Model
Slides 11: Volatility Expansion
Slides 12: The BGM Model
Slides 13.1: Forward Measure and the Ho-Lee Model

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documentation provided by StatSoft
I like this website - it says elementary but find nice exmaplanation to advanced topics to
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book by PROEFSCHRIFT
Contents
Preface i
List of Tables vii
List of Figures xi
1 Introduction and Summary 1
1.1 Introduction and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
A Modeling and Forecasting Stock Return Volatility 9
2 Testing for Changes in Volatility in Heteroskedastic Time Series 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 CUSUM tests for changes in volatility . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Testing for a single structural change . . . . . . . . . . . . . . . . . 13
2.2.2 Testing for multiple structural changes . . . . . . . . . . . . . . . . 16
2.2.3 Finite sample critical values . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Simulation design and results . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.1 Size properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.2 Power properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 Volatility changes in emerging stock markets . . . . . . . . . . . . . . . . . 33
2.5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3 Modeling and Forecasting S&P 500 Volatility 49
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
iv CONTENTS
3.3 Nonlinear Long Memory models . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4 Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.5 Forecasting volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.5.1 Alternative forecasting models . . . . . . . . . . . . . . . . . . . . . 66
3.5.2 Forecast evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.5.3 Forecast comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.5.4 Forecast results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.5.5 Value-at-Risk forecasts . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.6 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4 Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday
Data 87
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.1 Volatility-timing strategies . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.2 The economic value of volatility timing . . . . . . . . . . . . . . . . 98
4.3.3 Transaction costs and rebalancing frequency . . . . . . . . . . . . . 98
4.3.4 Conditional covariance matrix estimators . . . . . . . . . . . . . . . 100
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.4.1 Optimal decay rates . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.4.2 Portfolio performance . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.4.3 Transaction costs and rebalancing frequency . . . . . . . . . . . . . 108
4.4.4 Genuine out-of-sample forecasting . . . . . . . . . . . . . . . . . . . 112
4.5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4A S&P 100 constituents on June 18, 2004 . . . . . . . . . . . . . . . . . . . . 116
B Modeling and Forecasting Interest Rates 119
5 Examining the Nelson-Siegel Class of Term Structure Models 121
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.2 Term structure estimation methods . . . . . . . . . . . . . . . . . . . . . . 123
5.3 Nelson-Siegel class of models . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.3.1 Three-factor base model . . . . . . . . . . . . . . . . . . . . . . . . 125
v
5.3.2 Alternative Nelson-Siegel specifications . . . . . . . . . . . . . . . . 129
5.3.3 Two-factor model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
5.3.4 Bj
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Dealing with Derivatives:Studies on the role, informational content and pricing of financial derivatives
Prof.dr. C.G. Koedijk
Part I: Microstructure studies in derivatives markets
Chapter 2: Introduction to the first part 11
Chapter 3: Stock market quality in the presence of a traded option 15
3.1 Experimental design and procedures 18
3.2 Results 24
3.3 Discussion 44
3.4 Conclusion 45
Chapter 4: Insider strategies with options 47
4.1 The model 48
4.2 Market quality criteria 53
4.3 Results 56
4.4 Conclusion 70
Chapter 5: Conclusion of the first part 73
Part II: Empirical studies in derivatives markets
Chapter 6: Introduction to the second part 79
Chapter 7: The skewed-t implied distribution model 83
7.1 Methodology 86
7.2 Empirical results 91
7.3 Concluding remarks 102
Chapter 8: Implied GARCH volatility forecasting 105
8.1 Methodology 108
8.2 Data 114
8.3 Empirical results 119
8.4 Concluding remarks 128
Chapter 9: Pricing the spikes in power options 131
9.1 The two regimes model for spot electricity prices 134
9.2 Model estimation results 140
9.3 Option valuation 148
9.4 Concluding remarks 161
Chapter 10: Conclusion of the second part 165
Chapter 11: Summary and concluding remarks 169
11.1 Summary first part 169
11.2 Summary second part 170
11.3 Concluding remarks and future research 172
References 175
Samenvatting (Summary in Dutch) 189
Curriculum vitae 195
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Implementing Derivative Valuation Models
Dr. Andrew Ferraris
Deutsche Bank
Global Markets Equity
Quantitative Products

Presentation on integrating of various software and data components in real world pricing library
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