Consistent Long-Term Yield Curve Prediction: an arbitrage-free non-parametric yield curve prediction model which takes the full (discretized) yield curve as state variable, allowing us to separate clearly the tasks of estimating the volatility structure and of calibrating market prices of risk.
An Introduction to 6 Machine Learning Models: a high level summary of underlying algorithmic approach.
Backstage Wall Street: An Insider’s Guide to Knowing Who to Trust, Who to Run From, and How to Maximize Your Investments: Josh lays out in great detail what every person needs to know about what is going on today in the world of investments that are being offered to you from every broker, advisor etc that wants your money.
Tags - yield , prediction , machine , wall-street
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Hidden Markov model applied to FX prediction: can we use Markov Switching model for trading strategy?
3 ways to the use the 200 day moving average: 3 ways to use 200 day moving average to identify trend, slope and crossover.
Modeling Interest Rates with One Factor and Maturity-Dependent Volatility: detailed example of using Heath Jarrow and Morton (HJM) interest rate model.
Interview: Patrick Burns Quantitative Finance in R: the founder of Burns Statistics, providing consulting and bespoke software specializing in quantitative finance, programming in the S language, and optimization via genetic algorithms and simulated annealing.
Multiple Factor Model – Building 130/30 Index: detailed R example how to build 130/30 Index based on the a multiple factor model.
Tags - prediction , markov , moving-average , trend , rate , hjm , factor
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genetic algorithms, quantitative finance, software, burns statistics, mathematics, markov models, applied mathematics, statistics, markov chain, mathematical finance, hidden markov model, economic model, short rate model, index of statistics articles, markov switching model for trading strategy, founder
Dr. Patrick Burns is the founder of Burns Statistics, providing consulting and bespoke software specializing in quantitative finance, programming in the S language, and optimization via genetic algorithms and simulated annealing. Patrick has written many papers on quantitative finance and statistics, he is also the author of the book The R Inferno and the R package BurStFin.
I was born on the edge of a wheat field in the Empty Quarter. I made my way to Seattle for university where I received a PhD in statistics (with an emphasis on computing and a smattering of economics). Much later I moved to London.
In graduate school one of my office mates was Robert Gentleman, who would a few years later be half of the team that originated R.
I first touched R in the early 90's when Robert came around with it on his laptop. However I didn't seriously make the switch from S-PLUS to R until I started Burns Statistics in 2002.
A big reason I use R is because I used to be a developer of S-PLUS (R's sibling) and so I'm naturally fluent in R.
Tags - r , quant , portfolio , random
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genetic algorithms, software, computing, quantitative finance, burns statistics, seattle, london, patrick burns, robert gentleman, mathematics, science, quantitative analyst, valuation, burns, mathematical finance, author, inferno, founder, r
LABORSTA Internet: View and download data for over 200 countries or territories from LABORSTA, an International Labour Office database on labour statistics operated by the ILO Department of Statistics, excellent!
Estimating the Value-at-Risk: A Comparative Study of the Extreme Value Theory and Transformed Kernel Density Approach: peak-over-threshold (POT) method outperforms the transformed kernel density and the generalized extreme value block-maxima approaches to estimate Value-at-Risk.
Volatility timing and portfolio selection: How best to forecast volatility: the frequency of data used to construct volatility estimates, and the loss function used to estimate the parameters of a volatility model.
Interview: Donald R. van Deventer Risk Management: interview Donald, the Chairman and Chief Executive Officer of Kamakura Corporation, one of the 50 members RISK Magazine Hall of Fame in 2002.
The "Out of Sample" Performance of Long-run Risk Models: This paper studies the ability of long-run risk models to explain out-of-sample asset returns during 1931-2009.
GARP, 2011 Risk Manager of the Year Awarded to Aaron Brown: the 2011 Risk Manager of the Year Award to Aaron Brown, Head of Risk Management at AQR Capital Management, author of the book Red-Blooded Risk: The Secret History of Wall Street
Tags - data , var , volatility , risk
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kamakura corporation, aqr capital management, aaron brown, donald r. van deventer, international labour office, ilo department of statistics, actuarial science, finance, applied mathematics, mathematical finance, statistics, value at risk, volatility, risk, extreme value theory, kamakura corp, manager of the year award to aaron brown, risk manager, author, manager of the year week, head, chairman and chief executive officer, one of the 50 members, manager of the year awarded to aaron brown, head of risk management
Dr. Donald R. van Deventer is the Chairman and Chief Executive Officer of Kamakura Corporation, the world's leading provider of risk management solutions. His primary financial consulting and research interests involve the practical application of leading edge financial theory to solve critical financial risk management problems. He was elected to the 50 member RISK Magazine Hall of Fame in 2002. Dr. Donald R. van Deventer has served on the editorial board of the Journal of Credit Risk since 2005, and has written numerous papers and several books covering a wide range of risk management.
I grew up in Los Angeles and was a double major at Occidental College in mathematics and economics. I went to Harvard University and earned my Ph.D. in business economics in 1977. The business economics program is a joint program of the Department of Economics and the Harvard Business School.
If one has the chance to work for a very innovative firm like Kamakura, there’s the challenge and the pleasure of making the state of the art better every day. Within large financial institutions, a junior risk analyst is often trapped using an old fashioned legacy risk system purchased years before from a mediocre vendor. That’s bad for one’s career for two reasons. First, you don’t learn state of the art risk management and you run the risk of turning into a risk dinosaur at a young age. Second, if the firm is not using best practice risk management, the odds of failure are high even at a large bank as we’ve seen in the last five years.
My partner Prof. Robert Jarrow has a nice paper on the misuse of financial models and a video on the front page of the Kamakura web site www.kamakuraco.com on exactly this topic. Black and Scholes certainly shouldn’t be blamed if an analyst uses the Black model (which assumes interest rates are constant) to price interest rate options. The incorrect usage of financial models is astonishingly widespread.
Tags - risk , consulting , copula , black scholes , cds , crisis
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department of economics, occidental college, harvard business school, harvard university, kamakura corporation, large bank, risk management, risk management solutions, donald r. van deventer, robert jarrow, los angeles, finance, actuarial science, mathematics, applied mathematics, mathematical finance, financial risk management, value at risk, risk, credit risk, asset liability management, risk modeling, kamakura corp, chairman and chief executive officer, www.kamakuraco.com, junior risk analyst, analyst
Econometric measures of connectedness and systemic risk in the finance and insurance sectors: We propose several econometric measures that can identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests.
Math: Free Courses: Get free Math courses from the world’s leading universities.
Morgan Stanley's Commodity Thermometer:
Tags - crisis , math , commodity
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Stochastic Volatility Models and the Pricing of VIX Options: this paper examines and compares the performance of a variety of continuous-time volatility models in their ability to capture the behavior of the VIX.
Finding the best distribution that fits the data: the title tells, select a best fitted distribution among dozens candidates for a given data series.
No-Hype Options Trading: Myths, Realities, and Strategies That Really Work realistic strategies to consistently generate income every month, while debunking many myths about options trading that tend to lead retail traders astray.
RStudio in the cloud, for dummies: run cloud computing version of R with RStudio, cool!
Tags - volatility , stochastic , distribution , option , strategy , r , trading
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no-hype options trading, cloud computing version, retail traders, mathematical finance, derivatives, options, vix, volatility, stochastic volatility, economic model, local volatility, implied volatility
Stochastic Volatility Models and the Pricing of VIX Options is written by Joanna Goard, Mathew Mazur and published in Mathematical Finance. It examines and compares the performance of several volatility models to estimate the VIX, a measure of the implied volatility of S&P 500 index options. You can get access to the paper here.
An accurate estimation of VIX is obviously important given its special role as the fear gauge, there is extensive literature trying to do so, among them, mean-reverting models are especially popular. The authors compare eight different mean-reverting models, with each having different mean reversion speed or diffusion term, specifically, they can be summarized as follows in table 2.1:
Tags - volatility , stochastic , vix , option
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Dr. Ernest P. Chan is an expert in the development and application of statistical models and software for trading currencies, futures, and stocks. He is the principal of QTS Capital Management, LLC., which manages a hedge fund as well as individual clients’ accounts. He also offers training to clients via workshops or individualized consulting to trade for themselves using Matlab. Dr. Ernest P. Chan is the author of the famous book "Quantitative Trading: How to Build Your Own Algorithmic Trading Business".
I was born in Hong Kong, and I moved with my family to Toronto, Canada, when I was 17. I studied physics as an undergrad at U of Toronto, and received a Ph.D. in theoretical condensed matter physics from Cornell University. But after graduation, I never did any work in physics. I first worked as a researcher at IBM T. J. Watson Research Center’s Human Language Technologies group, where I designed statistical pattern recognition algorithms. Quite a few of my colleagues in that group moved on to become hugely successful algorithmic traders. (The current heads of Renaissance Technologies, Robert Mercer and Peter Brown, were both managers of that group.) After a few years, I too moved on to a career in finance, beginning at Morgan Stanley.
toronto, ibm, qts capital management llc., morgan stanley, ernest p. chan quantitative trading, quantitative trading, cornell university, ibm t. j. watson research center, statistical pattern recognition algorithms, finance, chan, peter brown, robert mercer, canada, financial markets, mathematical finance, investment, algorithmic trading, quantitative investing, quantitative analyst, investment banking, hedge fund, morgan stanley, author, researcher, international business machines corporation, quantitative trader, manager