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fragmentsinspired by a blog seen @Xanga
http://max-tang.blogspot.com/
inspired by a blog seen @Xanga
http://max-tang.blogspot.com/
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fragments | max-tang.blogspot.com Reviews
https://max-tang.blogspot.com
inspired by a blog seen @Xanga
max-tang.blogspot.com
fragments: CAPM & Efficient frontier "results in error-maximizing investment-irrelevant portfolios"?
http://max-tang.blogspot.com/2013/03/capm-efficient-frontier-results-in.html
Inspired by a blog seen @Xanga. Thursday, March 21, 2013. CAPM and Efficient frontier "results in error-maximizing investment-irrelevant portfolios"? Parameter estimation error in expected return and covariance. Example, with a 2 asset portfolio, let's say you overestimates A1 return by e and underestimates A2 by 2. Your average error is=0, which is pretty good. With modern portfolio theory, you would invest a lot more in A1 and less in A2 and thus maximizing your error. How to maximize expected return?
fragments: 02/01/2013 - 03/01/2013
http://max-tang.blogspot.com/2013_02_01_archive.html
Inspired by a blog seen @Xanga. Wednesday, February 27, 2013. Early exercise on a dice game. You get to throw a fair die up to 3 times. The number of dollars you're gonna get is determined by the number you get on you last throw. How much would you pay to play this game? First of all, if the game is free, what would your strategy be? 1st throw - - - 2nd throw - - - - - - - - - - 3rd throw. Early exercise - early exercise. The above will be the decisions you'd have to make. After first throw, do you.
fragments: SQL with R, histogram for each group
http://max-tang.blogspot.com/2013/10/sql-with-r-histogram-for-each-group.html
Inspired by a blog seen @Xanga. Friday, October 04, 2013. SQL with R, histogram for each group. Sqldf("select * from a2 limit 10"). Subject condition time SR. 1 1 WM pre 11. 2 2 WM pre 13. 3 3 WM pre 16. 4 4 WM pre 11. 5 5 WM pre 8. 6 6 WM pre 15. 7 7 WM pre 13. 8 8 WM pre 11. 9 9 WM pre 12. 10 10 WM pre 12. Sqldf("select condition, time, sum(SR) from a2 group by 1, 2"). 1 DS post 235. 2 DS pre 196. 3 PE post 194. 4 PE pre 193. 5 WM post 209. 6 WM pre 188. Ha2 geom histogram() facet grid(time condition).
fragments: PHILOSOPHY in pricing derivatives
http://max-tang.blogspot.com/2013/04/philosophy-in-pricing-derivatives.html
Inspired by a blog seen @Xanga. Sunday, April 07, 2013. PHILOSOPHY in pricing derivatives. Specify a model under the Q(theta)-dynamics. Theta is a vector of parameters, e.g. volatility, drift, etc. Q() is the risk neutral framework. Price all securities at time t by discounting the next period (t 1) risk neutral prices. Calibrate the model by choosing theta so that market prices of appropriate liquid securities agree with model prices of those securities. Subscribe to: Post Comments (Atom).
fragments: How to improve the accuracy of prediction based on past performance?
http://max-tang.blogspot.com/2013/03/how-to-improve-accuracy-of-prediction.html
Inspired by a blog seen @Xanga. Thursday, March 21, 2013. How to improve the accuracy of prediction based on past performance? One way is, instead of using the expected value, to calculate the confidence interval of the expected value and use the lower end of the value. Another way is to find a period (period A)in the past when the conditions and the value similar to present, then use the period A 1 data to estimate the next period. As usual, back testing with out of sample data. View my complete profile.
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fragments
Inspired by a blog seen @Xanga. Friday, October 04, 2013. SQL with R, histogram for each group. Sqldf("select * from a2 limit 10"). Subject condition time SR. 1 1 WM pre 11. 2 2 WM pre 13. 3 3 WM pre 16. 4 4 WM pre 11. 5 5 WM pre 8. 6 6 WM pre 15. 7 7 WM pre 13. 8 8 WM pre 11. 9 9 WM pre 12. 10 10 WM pre 12. Sqldf("select condition, time, sum(SR) from a2 group by 1, 2"). 1 DS post 235. 2 DS pre 196. 3 PE post 194. 4 PE pre 193. 5 WM post 209. 6 WM pre 188. Ha2 geom histogram() facet grid(time condition).
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