Mar
20
This is a short review of web hosting.
Many folks would like to own Web sites nowdays, however they concern about the cost. Purchasing the real domain name is really cheap, around 10 US dolloars you can get a fancy dot com name, therefore it's not actually a problem. What's crucial, although, is the hosting. it is meaningless to have a domain name should you do not go forward and make a site on it, begin appealing subscribers and customers, and do something to earn revenue. Even if you are merely blogging, you may not prefer to utilize the free services. If you are not blogging and you're indeed marketing a product or service, you truly can not bear free hosting. You'll have to own a Web site that allows more than what you are able to do on free services, most probably. There are dozens of ways to acquire that hosting, though, and the best way for most people is to use the Web Hosting service that accompanies the host name. Numerous corporations who sell domain names feature these hosting packages, and they aren't really expensive. For instance, 4 Cheap Web Hosting is a guide to the best rated affordable web hosting packages available online. Take a look if you have plan to own a site in the near future.
Many folks would like to own Web sites nowdays, however they concern about the cost. Purchasing the real domain name is really cheap, around 10 US dolloars you can get a fancy dot com name, therefore it's not actually a problem. What's crucial, although, is the hosting. it is meaningless to have a domain name should you do not go forward and make a site on it, begin appealing subscribers and customers, and do something to earn revenue. Even if you are merely blogging, you may not prefer to utilize the free services. If you are not blogging and you're indeed marketing a product or service, you truly can not bear free hosting. You'll have to own a Web site that allows more than what you are able to do on free services, most probably. There are dozens of ways to acquire that hosting, though, and the best way for most people is to use the Web Hosting service that accompanies the host name. Numerous corporations who sell domain names feature these hosting packages, and they aren't really expensive. For instance, 4 Cheap Web Hosting is a guide to the best rated affordable web hosting packages available online. Take a look if you have plan to own a site in the near future.
Mar
19
The Oxford MFE Toolbox is the follow on to the UCSD GARCH toolbox. It has been widely used by students here at Oxford, and represents a substantial improvement in robustness over the original UCSD GARCH code, although in its current form it only contains univariate routines.
Contents include:
1 Stationary Time Series 5
1.1 ARMA Simulation
1.1.1 Simulation: armaxfilter_simulate . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 ARMA Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.1 Estimation: armaxfilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.2 Residual Plotting: tsresidualplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.3 Characteristic Roots: armaroots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2.4 Information Criteria: aicsbic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3 ARMA Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3.1 Forecasting: arma_forecaster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4 Sample autocorrelation and partial autocorrelation . . . . . . . . . . . . . . . . . . . . . . . 23
1.4.1 Sample Autocorrelations: sacf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.4.2 Sample Partial Autocorrelations: spacf . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.5 Theoretical autocorrelation and partial autocorrelation . . . . . . . . . . . . . . . . . . . . . 27
1.5.1 ARMA Autocorrelations: acf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.5.2 ARMA Partial Autocorrelations: pacf . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.6 Testing for serial correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.6.1 Ljung-BoxQ Statistic: ljungbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.6.2 LM Serial Correlation Test: lmtest1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2 Nonstationary Time Series 37
2.1 Unit Root Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.1.1 Augmented Dickey-Fuller testing: augdf . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.1.2 Augmented Dickey-Fuller testing with automated lag selection: augdfautolag . . . . 40
3 Vector Autoregressions 43
3.1 Stationary Vector Autoregression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1.1 Vector Autoregression estimation: vectorar . . . . . . . . . . . . . . . . . . . . . . . 43
3.1.2 Granger Causality Testing: grangercause . . . . . . . . . . . . . . . . . . . . . . . . 50
3.1.3 Impulse Response function calculation: impulseresponse . . . . . . . . . . . . . . 53
4 Volatility Modeling 57
4.1 GARCH Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.1.1 ARCH/GARCH/GJR-GARCH/TARCH/AVGARCH/ZARCH Estimation: tarch . . . . . . 57
4.1.2 Some behind the scenes choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1.3 EGARCH Estimation: egarch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1.4 APARCH Estimation: aparch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5 Density Estimation 71
5.1 Kernel Density Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Code and documention are available at: http://www.kevinsheppard.com/wiki/MFE_Toolbox
Contents include:
1 Stationary Time Series 5
1.1 ARMA Simulation
1.1.1 Simulation: armaxfilter_simulate . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 ARMA Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.1 Estimation: armaxfilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.2 Residual Plotting: tsresidualplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.3 Characteristic Roots: armaroots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2.4 Information Criteria: aicsbic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3 ARMA Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3.1 Forecasting: arma_forecaster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4 Sample autocorrelation and partial autocorrelation . . . . . . . . . . . . . . . . . . . . . . . 23
1.4.1 Sample Autocorrelations: sacf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.4.2 Sample Partial Autocorrelations: spacf . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.5 Theoretical autocorrelation and partial autocorrelation . . . . . . . . . . . . . . . . . . . . . 27
1.5.1 ARMA Autocorrelations: acf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.5.2 ARMA Partial Autocorrelations: pacf . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.6 Testing for serial correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.6.1 Ljung-BoxQ Statistic: ljungbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.6.2 LM Serial Correlation Test: lmtest1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2 Nonstationary Time Series 37
2.1 Unit Root Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.1.1 Augmented Dickey-Fuller testing: augdf . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.1.2 Augmented Dickey-Fuller testing with automated lag selection: augdfautolag . . . . 40
3 Vector Autoregressions 43
3.1 Stationary Vector Autoregression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1.1 Vector Autoregression estimation: vectorar . . . . . . . . . . . . . . . . . . . . . . . 43
3.1.2 Granger Causality Testing: grangercause . . . . . . . . . . . . . . . . . . . . . . . . 50
3.1.3 Impulse Response function calculation: impulseresponse . . . . . . . . . . . . . . 53
4 Volatility Modeling 57
4.1 GARCH Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.1.1 ARCH/GARCH/GJR-GARCH/TARCH/AVGARCH/ZARCH Estimation: tarch . . . . . . 57
4.1.2 Some behind the scenes choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1.3 EGARCH Estimation: egarch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1.4 APARCH Estimation: aparch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5 Density Estimation 71
5.1 Kernel Density Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Code and documention are available at: http://www.kevinsheppard.com/wiki/MFE_Toolbox
Mar
16
Hutson has published its quant salary survey results in Asian market, http://china.hudson.com/documents/Hudson-Asia-Banking-Financial-Services-Salary-Information.pdf, focusing on Banking and Financial services sector. The figure looks not bad at all, given the terrible market of 2008. I have to say for many cases the quant salary does not mean the exact number the quant get, especially in China, other income exceeding salary is pretty possible.
I also had a survey for quant salary in mainland, China, results are shown below (basic salary + bonus, about 1~3 years work experience in Chinese Yuan):
number percentage of voters
50K ~ 80K 11.4%
80K ~ 100K 7.89%
100K ~ 120K 5.26%
120K ~ 150K 22.81%
150K ~ 200K 7.02%
200K ~ 300K 7.89%
>300K 37.72%
Considering the average annual salary for Chinese fresh master graduates is about 60K, quant salary is exicting, isn't it?
I also had a survey for quant salary in mainland, China, results are shown below (basic salary + bonus, about 1~3 years work experience in Chinese Yuan):
number percentage of voters
50K ~ 80K 11.4%
80K ~ 100K 7.89%
100K ~ 120K 5.26%
120K ~ 150K 22.81%
150K ~ 200K 7.02%
200K ~ 300K 7.89%
>300K 37.72%
Considering the average annual salary for Chinese fresh master graduates is about 60K, quant salary is exicting, isn't it?
Mar
13
Just share with you guys two free online video lecture sites I have recently used, both time- and cost- saving, isn't it? enjoy.
http://videolectures.net/Top/Computer_Science/Machine_Learning/
http://videolectures.net/
http://videolectures.net/Top/Computer_Science/Machine_Learning/
http://videolectures.net/
Mar
12
http://www.mathfinance.cn/Grouped-T-copula-simulation-estimation/ shared a sample code for grouped-t copula simulation, further, several copula estimation and simulation package can be found. But, most of the case we talk about an exchangeble copula due to its relatively easier to explain, however, it has limited applications especially in the area of credit risk, or derivative markets where asymmetric dependence plays a crutial role. For example, a desire to maintain the competitiveness of Japanese exports to the United States. with German exports to the United States. would lead the Bank of Japan to intervene to ensure a matching depreciation of the yen against the dollar whenever the Deutsche mark (DM) depreciated against the U.S. dollar. Such rebalancing behavior would also lead to greater dependence during depreciations of the DM and yen against the dollar than during appreciations. It is certainly natural to enquire whether there are extensions that are not rigidly exchangeble.
A scatter plot of the return of S&P 500 index and that of its implied volatility difference series is shown above, clearly the dependence is stronger in left-up corner than right-down corner.
A scatter plot of the return of S&P 500 index and that of its implied volatility difference series is shown above, clearly the dependence is stronger in left-up corner than right-down corner.
