Quantitative Finance Collector is a blog on Quantitative finance analysis, methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management.
Feb
7
Standing on the shoulders of giants allows us to see further, from now on we will invite experts to share with us their valuable experience and lessons.
It is our great pleasure to have Thijs van den Berg joining this week's interview session, Thijs is the manager of Sitmo B.V founded in 1998, which was initially a derivative market-making firm operating on the European Options Exchange (now Euronext), but soon building customized derivative models and risk management software development became an important activity. In 2003 Sitmo started consultancy services in Energy trading and quantitative modeling.
I’m from The Netherlands. As long as I can remember I’ve been curious: math, physics. I got my first computer when I was 10 and things became magical: I had my personal desktop lab to experiment with! About that time my family decided to move to a sunny island. I had a great time windsurfing, surfing and skating, but education was a bit 2nd place. I went a year to a local Spanish school but didn’t speak much Spanish and so the only thing I could follow was the math classes. The second year I went to a British International school and that was very intense and good. Every morning sausages and beans etc. After that we moved back to The Netherlands, I skipped a school year, and eventually went to the Delft Technical University when I was 17 to study Computer Science. The first year was perfect -I was in the top 5-, but then I started to doubt my choices... I ended up working in a popular bar and was really enjoying that, ..until a professor knocked on my door and said he wanted to talk to me. He’s now a very good friend. After that I quickly finished university, did a thesis at a bank on forecasting with Wavelets.
I ran into QF when I started trading (equity) derivates on the floor in the 90s. I’d build our own option pricing models and risk management tools, those were great times, we always had different prices than other traders, but we got it right... After that I got a job running a quant department at an energy Company. Energy trading was in its infancy: there was extremely much to do from a modeling perspective. The commodities have very complex dynamics, exotic assets, optimization, load forecasting, credit, data warehouses. We managed to get a couple of good PhD on board who delivered good models on fundamental activities. It was a true startup: when I joined the company the trade floor was just 6 people, when I left 250 with full blows specialized departments.
It is our great pleasure to have Thijs van den Berg joining this week's interview session, Thijs is the manager of Sitmo B.V founded in 1998, which was initially a derivative market-making firm operating on the European Options Exchange (now Euronext), but soon building customized derivative models and risk management software development became an important activity. In 2003 Sitmo started consultancy services in Energy trading and quantitative modeling.
Tell us a little background info about yourself. Where are you from? What’s your education background?
I’m from The Netherlands. As long as I can remember I’ve been curious: math, physics. I got my first computer when I was 10 and things became magical: I had my personal desktop lab to experiment with! About that time my family decided to move to a sunny island. I had a great time windsurfing, surfing and skating, but education was a bit 2nd place. I went a year to a local Spanish school but didn’t speak much Spanish and so the only thing I could follow was the math classes. The second year I went to a British International school and that was very intense and good. Every morning sausages and beans etc. After that we moved back to The Netherlands, I skipped a school year, and eventually went to the Delft Technical University when I was 17 to study Computer Science. The first year was perfect -I was in the top 5-, but then I started to doubt my choices... I ended up working in a popular bar and was really enjoying that, ..until a professor knocked on my door and said he wanted to talk to me. He’s now a very good friend. After that I quickly finished university, did a thesis at a bank on forecasting with Wavelets.
Do you have any experience with quantitative finance? If yes, how long have you been in the quantitative finance industry and to what extent?
I ran into QF when I started trading (equity) derivates on the floor in the 90s. I’d build our own option pricing models and risk management tools, those were great times, we always had different prices than other traders, but we got it right... After that I got a job running a quant department at an energy Company. Energy trading was in its infancy: there was extremely much to do from a modeling perspective. The commodities have very complex dynamics, exotic assets, optimization, load forecasting, credit, data warehouses. We managed to get a couple of good PhD on board who delivered good models on fundamental activities. It was a true startup: when I joined the company the trade floor was just 6 people, when I left 250 with full blows specialized departments.
Feb
4
The manager of QuantShare has contacted me about this software, after several days trial I feel it may be of interest to some of you so I post a short introduction here.
QuantShare is a new technical/fundamental analysis software available since only few months.
The sharing server is what makes QuantShare apart from anything else. It is a place where users can share their trading systems, indicators, downloaders, custom drawing tools...
If you need intraday data for futures, simply search for a downloader in the sharing server and chances are you will find one already implement by a member of the community. There are more than 800 items there and this number keeps increasing every day.
Besides, the sharing server, QuantShare has an impressive number of tools (Charting, Simulator, Composite, Genetic Algorithm, Neural Network ...).
The simulator for example allows you to create trading systems and backtest them very easily. The money management tool can be used in case you want to implement strategies that are more advanced. Once your system is ready, the Portfolio tool allows you to generate buy and sell orders automatically using the trading system rules.
QuantShare is a new technical/fundamental analysis software available since only few months.
The sharing server is what makes QuantShare apart from anything else. It is a place where users can share their trading systems, indicators, downloaders, custom drawing tools...
If you need intraday data for futures, simply search for a downloader in the sharing server and chances are you will find one already implement by a member of the community. There are more than 800 items there and this number keeps increasing every day.
Besides, the sharing server, QuantShare has an impressive number of tools (Charting, Simulator, Composite, Genetic Algorithm, Neural Network ...).
The simulator for example allows you to create trading systems and backtest them very easily. The money management tool can be used in case you want to implement strategies that are more advanced. Once your system is ready, the Portfolio tool allows you to generate buy and sell orders automatically using the trading system rules.
Feb
2
A Sea Change in Quantitative Finance: thoughts on P - Q Convergence in Quantitative Finance.
An Alternative Three-Factor Model: A new factor model consisting of the market factor, an investment factor, and a return-on-equity factor reduces the magnitude of the abnormal returns of a wide range of anomalies-based trading strategies.
People of Quant Research: a list of influential people in academy on Quantitative Finance research.
What Strategy Worked in 2011: what might cause the different performance of funds in 2011, is it due to trading strategies?
Bloomberg Open Market Data: Now you can adopt Bloomberg's market data interfaces without cost or restriction.
Kalman Filtering in R: Pros and Cons of existing R packages for Kalman Filtering.
An Alternative Three-Factor Model: A new factor model consisting of the market factor, an investment factor, and a return-on-equity factor reduces the magnitude of the abnormal returns of a wide range of anomalies-based trading strategies.
People of Quant Research: a list of influential people in academy on Quantitative Finance research.
What Strategy Worked in 2011: what might cause the different performance of funds in 2011, is it due to trading strategies?
Bloomberg Open Market Data: Now you can adopt Bloomberg's market data interfaces without cost or restriction.
Kalman Filtering in R: Pros and Cons of existing R packages for Kalman Filtering.
Jan
26
Time Series Matching with Dynamic Time Warping: a follow-up post for time series matching mentioned in last week.
Risk-Based Dynamic Asset Allocation with Extreme Tails and Correlations: a unique dynamic portfolio construction framework that improves portfolio performance by adjusting asset allocation in accordance with a forecast of market risk.
Problems with Using CDS to Infer Default Probabilities: banking regulations and risk management decisions should not be based on CDS implied default probabilities.
Why Borrowing Rates Should Never Be Tied to Credit Default Swap Spreads: shortfall of doing so.
Risk-Based Dynamic Asset Allocation with Extreme Tails and Correlations: a unique dynamic portfolio construction framework that improves portfolio performance by adjusting asset allocation in accordance with a forecast of market risk.
Problems with Using CDS to Infer Default Probabilities: banking regulations and risk management decisions should not be based on CDS implied default probabilities.
Why Borrowing Rates Should Never Be Tied to Credit Default Swap Spreads: shortfall of doing so.
Jan
19
Consumer Confidence and Equity Returns: what can we learn from Michigan Consumer Confidence index to reflect future equity returns?
Time Series Matching: If history repeat itself, we can "predict" futures return. Using historical data and time series matching analysis to make an educated guess what S&P 500 will do in the next week, month, quarter. Detailed R codes are provided.
Best Practices for Programming MATLAB: List of Best Practices for Matlab coding.
Systematic Investor Toolbox: a collection of tools that we use in everyday quantitative investment research written in R.
My Life in Finance: by Eugene F. Fama.
Option Prices Leading Equity Prices: Do Option Traders Have an Information Advantage?: the answer is, not surprisingly, YES.
Trend-following and Momentum Strategies in Futures Markets: momentum trading signals generated by fitting a linear trend on the asset price path maximise the out-of-sample performance while minimizing the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past return. Second, the results show strong momentum patterns at the monthly frequency of rebalancing, relatively strong momentum patterns at the weekly frequency and relatively weak momentum patterns at the daily frequency.
Forecasting with Internet Search Data: can we?
Time Series Matching: If history repeat itself, we can "predict" futures return. Using historical data and time series matching analysis to make an educated guess what S&P 500 will do in the next week, month, quarter. Detailed R codes are provided.
Best Practices for Programming MATLAB: List of Best Practices for Matlab coding.
Systematic Investor Toolbox: a collection of tools that we use in everyday quantitative investment research written in R.
My Life in Finance: by Eugene F. Fama.
Option Prices Leading Equity Prices: Do Option Traders Have an Information Advantage?: the answer is, not surprisingly, YES.
Trend-following and Momentum Strategies in Futures Markets: momentum trading signals generated by fitting a linear trend on the asset price path maximise the out-of-sample performance while minimizing the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past return. Second, the results show strong momentum patterns at the monthly frequency of rebalancing, relatively strong momentum patterns at the weekly frequency and relatively weak momentum patterns at the daily frequency.
Forecasting with Internet Search Data: can we?
