Quantitative Finance Collector is a blog on Quantitative finance analysis, financial engineering methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management. Please help us spread the word:
Aug
16
Received an email just now from professor Dr. Freddy Delbaen that David Heath passed away last week, what a great loss! For those of you who don't know who he is, David Heath is one of the authors who propose the influential Coherent risk measure.
Silent Salute!
Below is the email by Prof. Freddy Delbaen.
Silent Salute!
Below is the email by Prof. Freddy Delbaen.
Dear All,
Last week I received the sad news that Dave Heath passed away. Dave was one of the four "gang members" who started risk measures (around 1993). His contribution to the development of this field cannot be underestimated. Dave was also the mathematical pillar of the Heath-Jarrow-Morton models for interest rates, by now the standard in interest rate modelling.
As a close collaborator he visited ETH many times and some of you certainly met him during these visits. They will remember him as a sharp, logic, independently thinking mathematician with a lot of common sense.
Some 6 years ago, Dave had to stop academic activities. He started to have memory problems and the diagnosis was Alzheimer. In November 2010, Artzner, Eber, Heath, Ku and myself got the David Garrick Halmstad prize for the best paper in actuarial sciences. Dave was happy to get the prize. However his condition deteriorated quickly and since January he was in a specialised hospital. Last week he had an accident and a couple of days later he passed away.
For those who knew him it represents a great loss.
Freddy Delbaen
Last week I received the sad news that Dave Heath passed away. Dave was one of the four "gang members" who started risk measures (around 1993). His contribution to the development of this field cannot be underestimated. Dave was also the mathematical pillar of the Heath-Jarrow-Morton models for interest rates, by now the standard in interest rate modelling.
As a close collaborator he visited ETH many times and some of you certainly met him during these visits. They will remember him as a sharp, logic, independently thinking mathematician with a lot of common sense.
Some 6 years ago, Dave had to stop academic activities. He started to have memory problems and the diagnosis was Alzheimer. In November 2010, Artzner, Eber, Heath, Ku and myself got the David Garrick Halmstad prize for the best paper in actuarial sciences. Dave was happy to get the prize. However his condition deteriorated quickly and since January he was in a specialised hospital. Last week he had an accident and a couple of days later he passed away.
For those who knew him it represents a great loss.
Freddy Delbaen
Jun
21
Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques.
Contents include:
Financial markets, prices and risk
Univariate volatility modeling
Multivariate volatility models
Risk measures
Implementing risk forecasts
Analytical value-at-risk for options and bonds
Simulation methods for VaR for options and bonds
Backtesting and stress testing
Extreme value theory
Endogenous risk
You can download the Matlab and R codes at http://www.financialriskforecasting.com/book-code, I would recommend the book “Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab” to anyone who work as a risk analyst and need an introductory, practical book, on top of that, with enough programming codes to play with.
Contents include:Financial markets, prices and risk
Univariate volatility modeling
Multivariate volatility models
Risk measures
Implementing risk forecasts
Analytical value-at-risk for options and bonds
Simulation methods for VaR for options and bonds
Backtesting and stress testing
Extreme value theory
Endogenous risk
You can download the Matlab and R codes at http://www.financialriskforecasting.com/book-code, I would recommend the book “Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab
” to anyone who work as a risk analyst and need an introductory, practical book, on top of that, with enough programming codes to play with.
May
23
Counterparty risk has been increasingly popular largely due to the recent credit crisis (a crisis timeline was shared at an older post credit crisis timeline), however, most of valuing, hedging and securitizing counterparty credit risk involves Monte Carlo simulations, we have to be careful to make sure those simulated measures are arbitrage free. Below is a great paper talking about Mathematics and the software architecture of a risk system that includes counterparty risk and guarantees the measures are coherent.
A working paper is available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1844711.
The usage pattern is based on an offline phase to calibrate and generate model libraries. Valuation and simulation algorithms are planned offline with portfolio specific optimizations. The interactive user-driven phase includes a coherent global market simulation taking a few minutes and a real time data exploration phase with response time below 10 seconds.
Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
Data exploration includes 3-dimensional risk visualization of portfolio loss distributions and sensitivities. It also includes risk resolution capability for outliers from the global portfolio level down to the single instrument level and hedge ratio optimization. The network bottleneck is bypassed by using heterogeneous boards with acceleration. The memory bottleneck is avoided at the algorithmic level by adapting the mathematical framework to revolve around a handful of compute-bound algorithms.
A working paper is available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1844711.
Feb
23
Found a site providing financial analytics & risk management tools, FinCalc, as introduced by its webmaster: "FinCalc provides you with the tools to build advanced financial functions under Excel. ...FinCalc covers bonds, money market, futures, options and interest rate derivatives."
Key points are:
Calendar with business holidays for the major financial centers.
Bond analytics: yield to maturity, duration, accrued interest; valuation functions and sensitivity measures; bond cash flows; forward price and repo rate.
Derivatives: valuation functions and sensitivity measures european and american options; exotic options.
Discount curve construction based on money market rates,short term futures and swap rates.
Interest rates derivatives: valuation and sensitivity for swaps,swaptions, caps & floors.
Credit derivatives: valuation and sensitivity for CDS.
Portfolio analytics: volatility, expected return, tracking error, value at risk, portfolio optimization on an absolute basis or relative to a benchmark.
User friendliness: meaningful function and parameter names; user's manual, numerous examples and applications.
Excel add-in and examples to download.
For example, after downloading FinCalc.xla, opening it and other files saved in a same directory, a user is able to use the following modules:
The author protects the macro code with password, unfortunately. Check http://homepage.hispeed.ch/FinCalc/Index.htm if interested.
Key points are:
Calendar with business holidays for the major financial centers.
Bond analytics: yield to maturity, duration, accrued interest; valuation functions and sensitivity measures; bond cash flows; forward price and repo rate.
Derivatives: valuation functions and sensitivity measures european and american options; exotic options.
Discount curve construction based on money market rates,short term futures and swap rates.
Interest rates derivatives: valuation and sensitivity for swaps,swaptions, caps & floors.
Credit derivatives: valuation and sensitivity for CDS.
Portfolio analytics: volatility, expected return, tracking error, value at risk, portfolio optimization on an absolute basis or relative to a benchmark.
User friendliness: meaningful function and parameter names; user's manual, numerous examples and applications.
Excel add-in and examples to download.
For example, after downloading FinCalc.xla, opening it and other files saved in a same directory, a user is able to use the following modules:
The author protects the macro code with password, unfortunately. Check http://homepage.hispeed.ch/FinCalc/Index.htm if interested.
May
19
Unlike launching rocket, managing risk is a combination of art and science that should incorporate a number of fundamental characteristics. This post is by no means another ex post analysis of the reasons of current credit crisis, instead, it is about eight simple while crucial rules a risk manager or risk analyst must keep in mind (print out and post it on your PC). Sources are from http://www.geocities.com/mrmelchi/rule.htm and http://nasdaq.riskgrades.com/clients/nasdaq/edu_course.cgi?href=Module4-L2.html.
1.There is no return without risk. Rewards go to those who take risks.
2. Be transparent. Risk should be fully understood.
3. Seek experience. Risk is measured and managed by people, not mathematical models.
4. Know what you don't know. Question the assumptions you make.
5. Communicate. Risk should be discussed openly.
6. Diversify. Multiple risks will produce more consistent rewards.
7. Show discipline. A consistent and rigorous approach will beat a constantly changing strategy.
8. Use common sense. It is better to be approximately right, than to be precisely wrong.
1.There is no return without risk. Rewards go to those who take risks.
2. Be transparent. Risk should be fully understood.
3. Seek experience. Risk is measured and managed by people, not mathematical models.
4. Know what you don't know. Question the assumptions you make.
5. Communicate. Risk should be discussed openly.
6. Diversify. Multiple risks will produce more consistent rewards.
7. Show discipline. A consistent and rigorous approach will beat a constantly changing strategy.
8. Use common sense. It is better to be approximately right, than to be precisely wrong.
