Key Words: Probability Space, Sigma Field, Borel-Cantelli, Characteristic Function,  Radon-Nikodym, Conditional Expectation, Convergence Mode, Limiting Theorem, Point Process, Markov Chain in Discrete Time and Continuous Time, Martingale, Brownian Motion, Diffusion Process, First Passage, Hitting Times, Optional Stopping,

1). A Probability Path by Resnick

2).Adventures in Stochastic Processes by Resnick

3).Introduction to Probability Models by Ross

4).Basic Stochastic Process by Brzezniak and Zastawniak

5). A First Course in Stochastic Processes by Karlin Taylor

 

Key Words: Testing, Estimation, Bayesian, ARMA, Kalman Filter, GARCH, High Frequency, Unit Root, VAR, Cointegration

1. Lecture Notes on Statsitcal Inference

2. Various Time Series Books and Exercises

3. Introduction to high Frequency Finance

4. The Econometrics of Financial Markets

5. Kalman Filtering in Theory and Practice using Matlab

6. Kalman-Bucy Filters by Brammer and Siffling

 

Key Words: ITO Formula, Black-Scholes, Credit Derivatives, Interest Rate Models

1. Options, Futures and Other Derivatives by John Hull

2. Stochastic Calculus for finance by Steven Shreve

3. Stochastic Differential Equations by Oksendal

4. Introduction to Stochastic Calculus with Applications by Klebaner

5. Arbitrage Theory in Continuous Time by Bjork

6. Concepts and Practice in Mathematical Finance by MJ

7. Risk-Neutral Valuation by Bingham and Kiesel

 

Key Words: Series Solution, Fourier Series, Methods of Characteristics, Bessel Function, Green Function, Initial Value Problem, Boundary Value Problem,

1. Lecture Notes

2. Various Books

 

Key Words: Monte Carlo, Bootstrap, Computational Linear Algbra, Approximation Methods, Numerical Integration, Finite Difference, Finite Element

1. Lecture Notes

2. Various Books

3. Simulation by Sheldon Ross

4. Statistical computing with R by Rizzo

5. Monte Carlo Methods in Financial engineering by Glassman

6. Monte Carlo simulation and finance by McLeish

7. Introduction to Numerical analysis by Suli etc

8. Numerical Analysis by Burden and Faires

 

Key Words: Objected Orieted, Class and Inheritance, Vitual Function, Handle Class, Overload, Initialization, Vitual Destructor, Reference and Pointer, Template, Sorting, Search, R Language,

1. Thinking in C++

2. Accelerated C++

3. Effective C++

4. Schaum's Outlines

5. R Introduction

6. C++ Design Pattern and Derivative Pricing

关于目前大陆市场的红火情况，这位来自上海的大姐在鼓励大家继续制造泡沫。视频连接如下：

 

 

Sep. 5 - The CEO of China International Fund Management says despite the recent run-up in the Chinese stock market, corporate fundamentals are 'still very good.'

Mandy Wang, speaking at the Reuters China Century Summit in Shanghai on Wednesday, says Asian markets are "still the most attractive area in the world." Wang says one reason for being bullish on Asia is that over the "next two decades China and India will stimulate domestic consumption" and provide a great deal of opportunities for companies doing business in the region.

Speaker:

Many Wang, CEO, China Intl. Fund Mgmt

第一篇：

How Market Turmoil  Waylaid the 'Quants'

Peter Muller, a 43-year-old trader at Morgan Stanley, is used to markets behaving more or less as he expects. But in late July, some unusual patterns perplexed him. Certain investing strategies that historically had posted steady gains started faltering for no evident reason.

Soon, the unusual trading spread from U.S. to Japanese and European markets as well. Mr. Muller picked up rumors that one or more unknown investors were buying and selling giant positions similar to the ones he held, according to someone familiar with the matter. The next two weeks proved one of the biggest convulsions ever faced by a breed of market players that includes Mr. Muller: quantitative investors, known as "quants."

These traders use complex mathematical models to invest in markets around the globe. Their computers track a wide range of data and variables, such as how cyclical stocks do when a particular currency rises or falls. Formulas programmed into their computers spit out prices at which stocks or other instruments are to be bought and sold. In fact, the computers themselves often do the trading.

Quant strategies have been around for decades, but in recent years they have really come into their own, thanks in part to technology that has lowered the costs of their trading-intensive methods. Whereas investors like Warren Buffett and Peter Lynch defined an era of common-sense "value" investing in the 1980s -- and swashbuckling hedge funds betting on everything from metals to the British pound typified the 1990s -- quants have scaled the heights of the investing world in the past decade.

Quants' avoidance of the limelight has only amplified the aura of stars like James Simons of Renaissance Technologies Corp. and David Shaw of D.E. Shaw Group. Large investors such as pension funds seek the steady returns these funds have produced. Assets in just two common types of quant funds -- known as "statistical arbitrage" and "market neutral" funds -- have risen nearly 60% in two years, to $96 billion as of June 30, according to research group Hedgefund.net. The rise reflects both investment gains and new money.

Against this backdrop, quant funds' turmoil in late July and early August was all the more disconcerting. The broader U.S. stock market fell about 4% in that stretch. But Renaissance Institutional Equities slid 8.7%. Another big quant fund, AQR Capital Management, lost 13%. A Goldman Sachs Group Inc. quant fund called Global Equity Opportunities fell about 30%. Tykhe Capital LLC saw losses of roughly 20%. And Mr. Muller suffered along with them.

Though few on Wall Street know about it, his group at Morgan Stanley has been among the investment bank's most profitable operations in recent years. Known as PDT, for Process Driven Trading, it produced profits of roughly $3.5 billion in the 10 years through 2006, people familiar with it say. They add that PDT, which now contains about $6 billion of Morgan Stanley's money, accounted for 7.2% of the bank's net income last year by producing $540 million in profits.

But between the last week of July and Aug. 9, PDT lost approximately $500 million, according to traders. Neither Morgan Stanley nor Mr. Muller would comment on the losses or on PDT's trading strategy. Morgan Stanley said it is fully committed to the quantitative trading business.

"It's a very humbling event for [quants] to take these kinds of losses. These guys think of themselves as masters of the universe," said Lee Maclin, manager of Pragma Financial Systems, a New York "fund of funds," or hedge fund that invests in other hedge funds.

The quants' summer woes remind some of the near-meltdown almost a decade ago of high-flying hedge fund Long-Term Capital Management. Like quant funds, LTCM was steered by brainy academics who made money exploiting out-of-kilter relationships between different securities. Unlike LTCM, though, today's quant funds are far less leveraged and thus unlikely to sustain huge losses as LTCM did.

Quants say their bad patch will be forgotten as their strategies continue to churn out steady profits. Most of the funds, including Mr. Muller's, have recouped some of the losses. By the end of August, AQR had bounced back by roughly 10% from its lows, and the Goldman fund by 12%, according to people familiar with these funds.

Even so, the outsize drops could dim the luster of the quant approach -- especially since quants themselves still don't know for certain what triggered the carnage. A common theory is that one or more large funds was forced, possibly because of losses on subprime mortgages in other parts of its business, to rapidly dump stock to raise cash, and this set off a ripple effect among quant traders. Others say that stocks that were expected to fall began rising when traders who had borrowed shares and sold them were forced to start buying shares back. Meanwhile, the proliferation of quant funds holding a lot of the same positions may have been a recipe for magnifying the losses.

Mathematical, computer-driven trading was an arcane corner of the financial industry when Mr. Muller joined Morgan Stanley in 1992. He had been exposed to it, however, for several years at Barra Inc., a risk-analysis firm in Berkeley, Calif.

Secretive quantitative trader Peter Muller also plays the piano professionally, with the bravado of loose artist.

A 1985 math graduate of Princeton, Mr. Muller impressed some investment elders early on. Jeremy Grantham, chairman of the big money-management firm GMO LLC, recalls seeing a youthful Mr. Muller as a panelist at a conference 20 years ago. "I caught both super-quants [on the panel] in a logical fallacy," Mr. Grantham says. "The first one kind of choked on it, but Peter danced around the minefield like a tap dancer. I thought, 'That guy can really think on his feet.' "

Restless after Barra went public in the early 1990s, Mr. Muller interviewed for a job at Morgan Stanley. He told executives there that he didn't really think any amount of money could get him to leave his laid-back California life for Wall Street. But he accepted when the bank offered to let him set up a group that would invest some of its own money using a quantitative strategy.

Morgan Stanley -- which eventually bought Barra -- wasn't new to such techniques. Years earlier, it had employed Nunzio Tartaglia, a onetime astrophysicist and Jesuit seminarian who was an early practitioner of a particular quant strategy. And one of Mr. Tartaglia's underlings in the 1980s was Mr. Shaw, now the proprietor of one of the largest quant funds, D.E. Shaw.

The strategy Mr. Tartaglia used gives an idea of how quants operate. Called "pairs trading," it involves betting on two stocks that have a strong historical relationship.

Suppose that General Motors and Ford Motor stocks usually move more or less together. If they aren't doing so at a particular time, and there is no clear reason why, there's a good chance the past relationship will reassert itself. So if Ford has risen while GM languished, a quant might buy GM shares and sell Ford short, betting on it to decline. The "pairs trade" will pay off if the historic correlation returns.

Quants play the game on a massive scale -- betting on many different securities and using borrowed money to magnify the effect of any market anomalies detected by their computers. So although they expect to lose on many trades, the gains tend to outweigh the losses, thanks to their formulas and computing power. Ideas like pairs trading have blossomed into others such as "statistical arbitrage," a more complex version that is one of Mr. Muller's specialties at PDT.

By the late 1990s, PDT group had become so successful it commanded the biggest chunk of Morgan Stanley's stock trading for its own account. Mr. Muller let members of the group dress down when their returns were up -- and forced them to dress up when their returns were down -- so everyone else at the firm knew how they were doing.

But according to a short biography on Mr. Muller's Web site, he "woke up 6 years ago and realized that he can no longer find happiness in the corporate world." He had already taken a one-year 1999 sabbatical. In 2001, he left full-time work again, though he remained an adviser. Friends say Mr. Muller felt he had already accomplished more than he expected, and the intense money focus and social-climbing side of New York left him wishing for a more balanced life. He also had broken up with a longtime girlfriend.

So over the next several years, Mr. Muller traveled to Bhutan, New Zealand and Hawaii, and kayaked in the Grand Canyon. He spent time in California and took up yoga. He began writing crossword puzzles, several of which appeared in the New York Times.

He also became more serious about his music. He had taken up the piano as a child and joined a jazz band in California. In 2002 and 2004 he recorded albums on his own label, Dog and Hammock Productions. During his time away from Morgan Stanley early this decade, he could be seen playing on the streets of Barcelona, Spain, and in New York City subways.

Mr. Maclin of Pragma Financial recalls seeing Mr. Muller playing on a subway platform: "People were dropping change in his [keyboard case] not realizing the guy is worth millions."

Late last year, Mr. Muller returned to Morgan Stanley full time, encouraged by Chief Executive John Mack's push toward more aggressive risk-taking at the investment bank. The trader also felt that in what was becoming an increasingly competitive quant field, PDT could benefit from more hands-on guidance.

Though secretive about their formulas, quants like him are often seen together at social gatherings. Poker is a favorite pastime. Mr. Muller is the ace of the group. While away from Morgan Stanley, he briefly joined the World Poker Tour and pocketed nearly $100,000 in a tournament in 2004.

In March 2006, at a charity event called Math for America held at New York's St. Regis Hotel, several quants squared off in "Wall Street Poker" night. Looking on, according to people who were there, was a murderers' row of hedge-fund managers: Citadel Investment Group's Kenneth Griffin, Renaissance Capital's Mr. Simons and David Einhorn of Greenlight Capital Inc. In the final round, Clifford Asness, who runs AQR Capital, faced off against Mr. Muller, who took the title with a pair of kings to his foe's ace and 10.

This summer was less fun. Mr. Muller had retaken the helm of PDT just in time for the biggest test of his career, as the subprime-mortgage meltdown broadened into a more-general credit squeeze. Among the unusual results was that stocks many investors considered low-quality -- and had bet against -- began to outperform the market, says Diane Garnick, investment strategist at Invesco PLC. She attributes much of this to "short covering": Investors who had borrowed shares and sold them had to buy them back when their brokers reined in their credit lines. In contrast to the "flight to quality" often seen during a crunch, she says, the anomalous result was a "flight to non-quality."

The phenomenon may have been magnified by the similarity of quants' portfolios. Their world is one of shared theories. "Everybody has read the same academic literature and knows what's in the air," says Richard Bookstaber, a portfolio manager at FrontPoint Partners. Mr. Asness, in a letter to his investors at AQR, wrote that the early-August jolt "is about a strategy getting too crowded."

Mr. Muller has been playing detective to avoid repeating past mistakes, peppering friends with questions about the performance of his peers, asking pointedly which funds got in trouble and which did better, says a person familiar with the situation. He has talked several times with Mr. Asness.

Though computers execute quant trades, real people are constantly at the switch during the trading day, monitoring portfolios to make sure the programs are operating according to plan. If a computer accumulates too much of a single stock, a trader may intervene. And quants are always tweaking their models. Mr. Muller has told friends that the August swoon presents opportunities for experienced managers like him.

Then there's his music. Songs on his first two albums reflected what was going on his life, including one song with the lyrics "I almost made my escape, I almost got away.... So hard to quit when you're good at the game."

But Mr. Muller's Wall Street career is getting in the way again. Since returning to PDT, he hasn't written a new song all year. As he recently wrote on his Web site, "one of my other passions, mathematical finance, has taken a lot of my time this year." 

第二篇

1. Bibliography on Levy processes in financial modeling

1

Barndorff-Nielsen, O., Mikosch T. & Resnick S. (Eds.) (2001) Lévy processes- theory and applications, Boston: Birkhauser.

2

Barndorff-Nielsen, O. (1977): Infinite divisibility of the hyperbolic and generalized inverse Gaussian distributions, Zeitschrift fur Wahrscheinlichkeitstheorie une verwandte Gebiete, 38, 309-312.

2

Barndorff-Nielsen, O. (1998): Processes of normal inverse gaussian type, Finance & Stochastics, 2, No. 1, 41-68.

3

Barndorff-Nielsen, O. (1997): Normal inverse gaussian distributions and stochastic volatility modeling, Scandinavian Journal of Statistics, 24, 1-13.

4

Barndorff-Nielsen, O. & Shephard, N. (2000) Modeling with Lévy processes for financial econometrics, MaPhySto Research Report No. 16, University of Aarhus.

5

Barndorff-Nielsen, O. & Shephard, N. (1998) Incorporation of a Leverage Effect in a Stochastic Volatility Model, MaPhySto Research Report No. 18, University of Aarhus.

6

Barndorff-Nielsen, O. & Shephard, N. (2000) Integrated Ornstein Uhlenbeck Processes, Research Report, Oxford University.

7

Bertoin, J. (1996) Lévy processes, Cambridge University Press.

8

Bretagnolle, J. (1973) Processus à incréments indépendants, Ecole d'Eté de Probabilités, Lecture Notes in Mathematics, Vol. 237, pp 1-26. Berlin: Springer.

9

Carr P., Madan D. (1998): Option valuation using the fast Fourier transform, Journal of Computational Finance, 2, 61-73.

9

Carr P., Geman H., Madan D. & Yor, M. (2000) The fine structure of asset returns: an empirical investigation, Working Paper.

10

Chan, K. (1999) Pricing contingent claims on stocks driven by Lévy processes, Annals of Applied Probability, 9, 504-528.

11

Cont R. (2001) Empirical properties of asset returns: stylized facts and statistical issues, Quantitative Finance, 1, No. 2.

12

Cont R., Bouchaud J.P. & Potters M. (1997): Scaling in financial data: stable laws and beyond, in: B Dubrulle, F Graner & D Sornette (Eds.): Scale invariance and beyond, Berlin: Springer.

13 Cont, R. and Tankov, P. (2003) Financial modelling with jump processes, CRC Press.

14 

Eberlein, E. & Jacod, J. (1997): On the range of options prices,Finance & Stochastics, 1, No. 1, 131-140.

15

Eberlein E., Keller U. & Prause, K. (1998) New insights into smile, mispricing and Value at Risk: the hyperbolic model, Journal of Business, 71, No. 3, 371-405.

16

Eberlein E., Raible S. (1999) Term structure models driven by general Levy processes, Mathematical Finance, 9, 31-53.

17

Geman H., Madan D. & Yor M. (2000) Time changes in subordinated Brownian motion, Preprint.

18

Geman H., Madan D. & Yor M. (1999) Time changes for Levy processes, Preprint.

19

Jacod, J. & Shiryaev, A.N. (1987) Limit theorems for stochastic processes, Berlin: Springer.

20

Lévy, P. (1937) Théorie de l'addition des variables aléatoires, Paris: Gauthier Villars.

21

Madan, D. & Milne, F. (1991) Option pricing with variance gamma martingale components, Mathematical finance, 1, 39-55.

22

Madan, D. B., P. P. Carr, and E. C. Chang (1998). The variance gamma process and option pricing. European Finance Review2, 79-105.

23

Madan, D. B. and E. Seneta (1990). The variance gamma model for share market returns. Journal of Business, 63, 511-524.

24

Mandelbrot, B. (1997) Fractals and scaling in finance, Berlin: Springer.

25

Protter, P. (1990) Stochastic integration and differential equations: a new approach, Berlin: Springer.

26

Rydberg, T. H. (1997): The normal inverse Gaussian Lévy process: Simulation and approximation. Commun. Stat., Stochastic Models 13(4), 887-910.

27

Samorodnitsky, G. & Taqqu, M. (1994) Stable non-gaussian random processes, New York: Chapman and Hall.

28

Sato, K. (1999) Lévy processes and infinitely divisible distributions, Cambridge University Press.

最近这个月学了点econometrics和time series，特将一些看过的没看过的书总结一下：

 

一：Probability and Statistics：

"Introduction to Mathematical Statistics" by Craig et al

此书我看了近400来页吧。经典老书了。习题多，讲解思路清楚，但理论多而结合生活的实例太少。个人认为，无论是搞理论还是搞应用，都需要两者兼顾。搞理论的需要看点实际应用的书才能知道理论发展的根源在哪儿；搞应用的需要看点理论书籍才知道高屋建瓴真正理解指导应用的理论原则。这就是我为什么要看下面这本书的原因：

"mathematical statistics and data analysis" by Rice

书写得很好，主要是容易而且实际应用的例子多。

"probability theory and statistical inference" by Aris Spanos (1999)；

没看这本书的人估计都不太明白统计是怎么回事. 为了给自己扫盲，这几天正在看.感觉只能说对作者佩服的五体投地.

"Asymptotic theory for econometrics" by Halbert White（2000）;

"probability and measure" by Billingsley(1995),

"Introduction to Mathematical and Statistical Foundations of Econometrics" by Bierens(2004)

以上三本书都是用测度论的语言来描述的，比较数学化.我只看了Billingsley里的一些内容,自认为要完全理解里面的东西尚需时日.上次听测度论的课碰到了一个希腊小伙,很得意的告诉我他已经把Billingsley的书"read from front cover to back cover",佩服!  小伙子博士一毕业就把econometric领域内的三大top journal 都攻克了，厉害!

 "Statistical Foundation of Econometric Modelling" by Hendry and Spanos(1987)

 

二：Econometrics and Financial Econometrics

1. Entry Level：

"Basic Econometrics" by Gujaradi,

"Introductory Econometrics for Finance " by Brooks(2002);

以上三本是给本科生用的入门教材，数学够的人应该可以在一个星期内翻完。我只看了woodridge的一部分章节, 把计量的基本原理反复解释的很清楚。

"Econometric Models and Economic Forecaasting" by Pindyck and Rubinfield(1997);

pindyck写书的风格我一向喜欢。以前看过他写的investment under uncertainty，读的时候时而让人拍案叫好。这本计量书我也仔细通读了一遍，整体感觉比Woodridge要稍难，但是属于经济类本科生可以接受的范围内。

 

2.Intermediate Level：

"A guide to Modern Econometrics" by Verbeek (2nd edition  2004);

Bob指定要我读的书。介于硕士博士的水平。

"A guide to Econometrics" by Kennedy (5th edition, 2004);

其他书的好伴侣。风格类似于读书笔记或者literature review,无太多公式演算,主要讲述intuitive feeling和应用原则.

"Introduction to econometrics" by Maddala(3rd Edition, 2001),

"Econometric Methods" by Johnston and DiNardo(1997, 4th edition)

"Econometrics" by Hayashi(2000);

"Econometric Analysis" by Green(4th Edition, 2002);

"A course in Econometrics" by Goldberg,

一本类似于lecture notes的教材，分成small sections而不是chapters,每个section后面还有习题,特别适合于课堂教学和自学.

"Econometrics of Financial Markets" by Campell and Lo (1997),

financial econometrics的必读书。

3. Advanced Level:

"Estimation and Inference in Econometrics " by Davidson & McKinnon(1994);

"Dynamic Econometrics" by Hendry(1995);

"Advanced Econometrics" by Takeshi Amemiya  1985;

"Financial Econometrics: Problems, Models and Methods "by Gourieroux and Jasiak (2001),

"Handbook of Econometrics";

"Handbook of Applied Econometrics";

"Handbook of Economic Forecasting";

4. Selected Topics

bayesian Econometrics:

high frequency Econometrics:

 

三： Time Series and Financial Time Series

1.Entry Level：

"Time Series Analysis" by Chatfield(2006),

网上评价不错，但是我感觉此书不伦不类.看了一半就没继续看了。简而言之:理论少而废话多.不值得通读，偶尔翻翻应该还是有收获的。

"Applied Econometric Time Series" by Enders(2004),

A must reading beginning book. 我通读了一遍,收益非浅.

"Time Series Technqiues for Economists " by Mills(1990)

此书我基本看完, 感觉注重应用技巧,特别适用于增加 hand on experience.

"Economic Forecasting: An Introduction"

此书薄但是内容很全.适合forecasting的人快速上手。我看了相关的部分章节,感觉里面的实例挺不错。作者对于这门学科有着自己的理解。

"Forecasting: Methods and Applications"

Harvard, Insead and Monash 三个名校教授写的书，preface里面标明写书的目的是让马路上的老大妈也能看的懂. 个人认为此目的基本达到。

"Analysis of Financial Time Series" by Tsay,

侧重finance 领域内的东西，书里还有程序, 大而全，但内容既不深也不详细.适合industry里的人快速上手,gain some working knowledge。

 

2.Intermediate Level:

"Time Series Models: Forecasting and Control" by Box and Jenkinson(1994),

time series 领域内的BIBLE. 现代time series econometrics就因为此书而开始新的篇章。此书提出来的integrated process以及因此而产生的unit root test就足够写上一本专著。 这两个英国人没有拿nobel奖,真是让人奇怪。等有时间了一定仔细读几遍.

"Econometric Modelling of Financial Time Series " by Mills(),

"Time Series Models " by Harvey(),

"Econometric analysis of Time Series" Harvey(1989)

Harvey是time series的领路人之一,目前在剑桥. 但据隔壁的某院士告诉我,Harvey写的书如同他上课的风格一样，可以把人已经明白的变糊涂，糊涂的变的更加糊涂.此话吓的我没敢借这本书看.

"Forecasting Economic Time Series" by Granger and Newbold(1986),

两个time series econometrics 领域内的领路人合写的书,曾经试图作为我自学的起步书，结果发现基础不够，又还掉了。过段时间再借过来看看. 为此书作proof reading是我们商学院的一个老师，30年前估计是newbold的phd student.

"Time Series Models for Business and Economic Forecasting" by Philip Hans Franses and Dick van Dijk(2007)

风格类似于kennedy的书.目前是我手头的参考书,时而翻翻.

 

3.Advanced Level：

"Time Series Models" by Hamilton(1994),

 "Introduction to statistical time series"  by Fuller(1996 second edition)

 

"Time Series Analysis: Theory and Methods" by Brockwell and Davis(2003),

书我没看，但是看到Brockwell的最近的一篇paper 居然把levy process 跟 ARMA结合在一起，搞出了一个levy-driven continuous ARMA model, 实在是个good idea。数学界的人这五年都在风风火火的搞levy process和copula,没想到这个哥们居然也来凑这个热闹。

"Forecasting Economic Time Series " by Clements and Hendry

"Forecasting non-stationary time series" by Clements and Hendry

两位老大合作多年了。David Hendry是time series的老大之一, founding Edito-in-chief of "journal of applied econometrics". general-to-specific modelling 的思想是他提出来的. 而目前在warwick的Clements是Editor-in-chief  of "international journal of forecasting". 如果想发论文在这两个杂志上，赶紧乖乖的把这两本书看完吧.

 

4. Research  Level：

Time Series Models: Nonstationary and Noninvertible  Distribution Theory by Tanaka 1996

5. Selected Topics：

＜Ａ＞．Structural Model and Kalman Filter:

"Forecasting, Structure Models and Kalman Filter " by Harvey(2003),

又是harvey, kalman filter的老大. 但既然院士说了他写的书难看懂,我不如去借control engineering 或者EE里的一些关于optimal estimation的书来看。那里关于kalman filter 的好的描写多的是。比如：

"Introduction to Random Signals and Applied Kalman Filtering" by Brown and Hwang(3rd edition, 2001)

"Kalman Filtering:Theory and Practice using matlab" by Grewal and Andrews(2nd edition, 2001)

"Optimal Filtering" by anderson and Moore(1979)

"Fundamentals of Kalman Filtering: A Practical Approach" by Zarchan and Musoff(2001)

"Random Processes:Filtering, Estimation and Detection" by Ludeman(2001)

"Optimal State Estimation:Kalman, H-infinity, and Nonlinear Approaches", by Dan Simon(2006)

"Applied Optimal Estimation"  by Arthur Gelb(1974)

 

＜Ｂ＞．Fourie Analysis:Bloomfield(2000)

＜Ｃ＞．Wavelet:

＜Ｄ＞．Cointegration:

＜Ｅ＞．Nonlinear: Philip Hans Franses and Dick van Dijk(2001)

  Long Memory Time Series: Theory and Methods by  Wilfredo Palma (2007)

Best econometric journal: Econometrica, Journal of Econometrics

Other top journal: Econometric theory, Journal of Applied Econometrics, Econometric Review, Journal of tTme Series Analysis, Journal of Financial Econometrics, Journal of Forecasting, International Journal of Forecasting,

Best statsitics journal: Biometrica, Annals of Statistics, Journal of Royal Statistical Society Series B, Journal of America Statistical Society,Statistical Science,

 

以上是个人看书的一点体会以及搜集的一些reading list. 对time series 真正有兴趣可参考权威版本的reading guide by peter philips. 连接如下: http://korora.econ.yale.edu/phillips/teach/553a-06syl.pdf  这个大牛还在LSE读硕士的时候就开始在econometrica上发文章了。一个真正的天才.现在在YALE的经济系立了个山头.Econometric Theory的主编,他也是到目前为止我看到的发econometrica最多的人,合计得超过30篇吧。

 

伦敦金融城周四（3月15日）公布的全球金融中心排名指数（GFCI）显示伦敦在全球金融中心城市中排名第一，纽约(华尔街)其次。香港和新加坡分别位于第三和第四。而两年前一项类似的调查中备受青睐的上海，这次排名第24。
为计算指数所作的调查还显示，金融界对构成金融中心竞争力主要因素的看法也在改变。两年前，人才和技术被认为是构成竞争力的首要因素，今年受访者关注的重点是政策监管和税收环境。

据BBC报道，全球金融中心排名指数今年首次公布，由英国的Z/Yen调查公司为伦敦金融城统计制作，今后每两年公布一次，对全球46个城市作为金融中心所具备的竞争力加以比较和排名。全球金融中心排名指数是根据对全球金融服务业决策层人士进行的网上问卷调查结果，结合47个有关竞争力的不同指数，综合计算分析得出。

排名目的

推出这个指数的目的是展示金融中心之间相对竞争力的变化，以助了解金融中心之间力量对比的变化，从零开始建设一个世界主要金融中心（比如沙特的迪拜）是否可能，以及随着区域经济实力的增强，亚洲是否可能出现一个全球金融中心。

伦敦金融城政府政策与资源委员会主席斯耐德说："全球金融中心排名指数凸显了两个亚洲金融中心 - 香港和新加坡 - 在竞争力上远远超过伦敦以外的其他任何一个欧洲城市。"

伦敦金融城2005年的金融中心竞争力调查报告显示，当时亚洲几个金融市场之间没有明确的高低之分，而今年的调查则清楚表明，香港被认为正在成为世界金融中心，而日本东京仅排名第9。两年前的调查中倍受青睐的上海和北京，今年名列第24和第36。

根据这次调查的结果，受访者认为香港最有可能成为全球金融中心，因为它的监管良好，高质量的专业人力资源充足，而且已经是重要的区域金融中心。从各项指标来看，上海都无法跟香港比。

根据调查报告，成功的金融中心扮演了五个角色中至少一个:

全球金融中心，目前只有伦敦和纽约够这个资格；

国际金融中心，比如香港，承担了大量跨国交易活动；

特色金融中心，在某个领域独霸鳌头，比如苏黎世的私营银行业是世 界第一;

全国金融中心，作为一个国家的主要金融服务中心，比如上海；

区域金融中心，承担了国内一个地区的主要金融业务，比如芝加哥既是一个国际金融中心，又是一个地区金融中心。
伦敦和纽约扮演了全部五种角色，但伦敦在五项竞争力指标上都超过了纽约。

竞争力要素改变

今年的调查结果表明，金融界要人对构成金融中心竞争力的要素的看法跟两年前不同。2005年，在《伦敦作为全球金融中心的相对竞争力调查报告》中，大多数受访者认为人才和技术是整体竞争力的首要因素，而这次的调查显示，人们的关注焦点已经转移到政府监管政策和税务环境。

计算指数中采纳的其他竞争力因素指数还包括人力（高质量劳动力供应、劳动力市场灵活程度、商业教育和人力资本开发情况）、商业环境（监管、税率、腐败程度、经济自由度和经商的难易程度）、市场状况和基础设施条件，以及居住生活条件等因素。

关于亚洲是否可能出现一个可以跟伦敦和纽约平起平坐的全球金融中心，今年的调查报告没有给出定论，但排名指数显示，香港和新加坡正在成为国际金融中心，而东京和上海正在稳固它们作为国内金融中心的地位。

报告还指出，如果有更充足的数据，圣保罗和约翰内斯堡两年后也有可能被纳入全球金融中心排名指数。

 

London and New York emerge as clear leaders in new ranking of global financial centres

Today sees the release of a new report, The Competitive Position of London as a Global Financial Centre, which gives a fascinating insight into global financial centres and how London ranks among them. 

Commissioned by the Corporation of London and conducted by Z/Yen Limited, the research is based on a survey of opinion of professionals in the financial services industry in over 20 countries.

Senior decision makers were asked about the key components of competitive advantage, and how the world’s major financial centres ranked against these criteria. 

Michael Snyder, Chairman, Policy and Resources Committee, Corporation of London said “I believe the key findings of this report are clear: London and New York have emerged as the clear leaders in the ranking of global financial centres.”

Average Scores of the Financial Centres

 

• London and New York are the only two genuinely global financial centres at present.  Respondents believe that other financial centres such as Frankfurt, Paris and most of the Asian centres will remain as national financial centres and will not challenge London and New York as global financial centres in the future.
• The difference between London and New York is not statistically significant.  What is clear, however, is that these two financial centres have extended their lead over Frankfurt and Paris since 2003.¹

Other findings of the report include:

• The five most important competitive factors for financial centres in descending order are: the availability of skilled personnel; the regulatory environment; access to international financial markets; availability of business infrastructure; and access to customers.

Competitive Factors Ranked

Factor of Competitiveness	Rank	Average Score
Availability of Skilled Personnel	1	5.37
Regulatory Environment	2	5.16
Access to International Financial Markets	3	5.08
Availability of Business Infrastructure	4	5.01
Access to Customers	5	4.90
A Fair and Just Business Environment	6	4.67
Government Responsiveness	7	4.61
Corporate Tax Regime	8	4.47
Operational Costs
9	4.38
Access to Suppliers of Professional Services	10	4.33
Quality of Life	11	4.30
Cultural & Language	12	4.28
Quality / Availability of Commercial Property	13	4.04
Personal Tax Regime	14	3.89

• The availability of skilled personnel is clearly judged as the most important factor with over 90% of respondents judging it to be either ‘Very Important’ or ‘Critically Important’.  
• The regulatory environment is also seen as a crucial component of competitiveness.  These two factors also ranked first and second in the 2003 study.  London and New York scored highly for both these factors and this contributed to their overall rankings. 
• None of the recipients believes that London or New York will lose their positions as global financial centres within the next ten years.  
• Part of the continuing appeal of London to foreign companies is its cosmopolitan status.  Frankfurt and Tokyo, for example, are primarily market places for domestic participants to which foreign players are granted access.  London, and to a lesser extent, New York are characterised by foreigners trading with each other.  In an increasingly international economy, London seems to have a solid future as a global financial centre.
• An argument has been made that financial centres will be weakened by technology, outsourcing and off-shoring.  The research indicates that financial centres may lose certain types of commoditised activities to low cost cities although important parts of the industry, companies’ headquarters and their most skilled employees, will continue to cluster in financial centres.  ‘Value-added’ activities will remain in the business clusters that financial centres offer.  These value added activities include senior ‘strategic’ management and front office, product innovation, client facing and deal-making activities.   
• Views on a third global financial centre are split.  Most people agree that if a third global financial centre develops it is most likely to be in China and probably in Shanghai.  It is unlikely that Hong Kong, Singapore or Tokyo will ever become more than regional financial centres.  
• The main competitive factors that keep London and New York so strong are the availability of skilled people and the regulatory environment.  However, the reason that these cities became global financial centres in the first place has something to do with their long trading history.  These two cities have critical mass and benefit from having an existing business cluster. 

Mark Yeandle of Z/Yen Limited commented:

“Global financial centres have emerged where market liquidity is.  Market liquidity is very hard to move and so once a centre such as London has been established it will take a number of significant factors, acting over a long period to alter the status quo”.

1. In June 2003 the Corporation of London published Sizing up the City – London’s Ranking as a Financial Centre, a report written by the Centre for the Study of Financial Innovation based on a survey of City opinion of London’s competitive position as an international financial services centre.

 

Also see the 2005 report "the competitive position of london as a global financial centre" (70 pages long)

http://www.cityoflondon.gov.uk/NR/rdonlyres/131B4294-698B-4FAF-9758-080CCE86A36C/0/BC_RS_compposition_FR.pdf

Banks are reportedly licking wounds on junk rated indices, but there’s no less demand for credit derivatives expertise.

 

BNP Paribas is the latest to join the credit derivatives fray. Financial News reports that the French bank has bold plans to continue expanding its structured credit business, and may hire as many as 350 front office professionals this year (not all into credit derivatives, we assume).

Separately, Derivatives Week says Bank of America is upping its credit derivatives sales force in Europe by 25% in the coming months. And Royal Bank of Scotland, HSBC, West LB and Citigroup are also in the market for credit derivatives talent, according to headhunters.

But what of claims that the opaque credit derivatives market, and much of the rest of the financial system, are liable to go belly-up in the event of a major default?

It’s all scaremongering, according to Terri Duhon, managing director of B&B Structured Finance, a derivatives advisory firm. “Credit derivatives will not exacerbate a market downturn,” says Duhon. “If anything, the credit derivatives market has mitigated the contagion risk by distributing losses to a much larger number of investors.”

In the meantime, Duhon says credit derivatives traders are well placed to profit from growing risk aversion: “As credit risk is perceived to be increasing, credit derivatives will increasingly be used as a hedging tool.”

Little surprise, therefore, that structured credit desks have been doing well out of the current market, with trading volumes soaring on the back of market volatility. “People have only been losing money in crossover and high yield,” says Alex Tracey, MD at Clifton Partners. “On the whole, most desks have done quite well out of widening spreads.”

It’s also little surprise, then, that banks continue to pile into the market. “It’s a bit like property,” reflects Tracey. “At this stage of the cycle, it may not look like a good time to be going in, but you don’t want to be left on the sidelines.”

Fairytale New Year in structured credit sales?

30 Nov 2006

Mirror, mirror on the wall, who is the most highly desired of them all? Structured credit sales specialists, if some headhunters’ claims are anything to go by.

“There will be at least 150 vacancies in the structured credit sales market next year,” says one derivatives-focused headhunter. “In 2005 and 2006 a lot of banks built up their structuring and origination capabilities, 2007 will be the year for increasing the distribution side,” he adds.

Barclays Capital, BNP Paribas, Calyon, Dresdner Kleinwort, HSBC, Royal Bank of Scotland, and Wachovia are all said to be rustling their chequebooks. So too, apparently, are established players like JPMorgan and Deutsche Bank.

“There are aggressive expansion plans outside the big four or five,” says the headhunter. “The big players are acutely aware of this and bonus pools will be 10 per cent to 20 per cent up on last year.”

Shaun Springer, chief executive of Napier Scott, said the 150 additions were ‘feasible’ given institutions’ hiring plans: “Everyone is going through expansion so clearly there will not be enough talent to go around. There may be a need to import people from outside the UK as well perhaps the buyside and the agencies.”

As in every fairytale, however, there are obstacles en-route to fame and fortune. Two headhunters told us the notion that 150 sales staff would be added in structured credit was too good to be true. “We do have clients asking us for a lot of people, but 150 is way too high,” said one, while the other claimed total ignorance of the predicted hiring boom.

Corporate finance salaries spiralling higher

 

Markets may be sliding but for now at least, M&A and corporate finance salaries could still be rising

Recruiter Robert Walters says corporate finance salaries are up 40% year on year, with senior positions in the sector reputedly now commanding basic pay of £200k. And the outlook looks promising – Robert Walters says one US bank is looking to double its corporate finance headcount in London this year.

The company also says most corporate finance recruitment has been at the senior end of the market, at managing director level, with strong demand for people who can originate business or bring clients with them.

But £200k salaries are still, by and large, the exception rather than the rule, cautions Andrew Lynch, a consultant at Akamai Financial Markets. Lynch also questions whether salaries have risen quite as dramatically as Robert Walters claims.

“Some of the really top performers may have gone up something like 40%, but that’s not representative of the market as a whole,” he says.

Other claims from Robert Walters:

• 20% more people are unhappy with their bonuses this year than last year.

• High salaries are luring finance directors to Asia (a finance director earning £250k in London can earn £300k to £500k in the Far East).

• Taxes are troubling – 30% more people this year than last year said high taxes were a concern

 

 

“You’re going to see banks bringing in people from Hong Kong and Singapore this year,” predicts Andrew Chancellor, London-based head of the banking and finance team at recruitment firm Robert Walters. “Most will be Europeans who might have been an accountant over here before moving into a banking role in Hong Kong. There’s definitely a lot more flexibility about hiring that kind of person.”

In the past, Chancellor says financiers who quit London in search of fame and fortune in Hong Kong were known by the acronym FILTH – ‘Failed In London Try Hong Kong.’ Not any more it seems – banks’ failure to hire and train sufficient numbers of corporate financiers during the downturn of 2002 and 2003 means they’re keen to hire wherever they can.

Robert Thesiger, chief executive of recruiter Morgan McKinley, confirms the talent pool is becoming more and more global. “It’s no longer just about finding candidates in London and continental Europe – there’s much more transference from Hong Kong, Singapore and India.”

Corporate financiers who make the move to the City of London from Asia are likely to see significant upside in their pay packets.

The 2006 salary survey from international search firm Options Group suggested a first year associate in corporate finance/M&A earned a base salary of £50k to £65k in London, plus a bonus of £100k to £150k. The comparable package in Hong Kong and Singapore was a base salary of £30k to £40k, plus a £70k to £90k bonus.

Rumour has it that algorithmic traders are none too happy with the recent bonus round.

“Algorithmic traders in banks and hedge funds have had a stinking year,” says one London headhunter. “Profits haven’t been as high as anticipated and pay has been squeezed.” Another (equally anonymous) headhunter agrees. “Although some investment banks are making huge returns from algorithmic trading, there are quite a few that aren’t. Where businesses are immature, people haven’t been paid very well – I know some junior quants who’ve been paid a £60k salary plus a £60k bonus, while similarly experienced algo traders have made bonuses of just £15k.” Is pay in the sector really that bad? Not according to Luke Williams, a consultant at search firm Kinsey Allen. But Williams agrees the quants who build algorithmic trading models are inevitably better remunerated than the traders who do little more than execute trades that the models suggest: “The supply of guys who write the algorithms is a lot more finite than the traders who execute them.” Quants vs. traders While a quant with two to three years’ experience can expect to earn a base salary of around £70k plus a bonus in the region of £140k, Williams says a comparable algo trader will earn a bonus that’s a fraction rather than a multiple of a similar base – 50% to 100% is standard. Which banks pay their algorithmic traders the most? Rumour has it Goldman and Lehman Brothers rank among the more generous. By comparison, Credit Suisse is said (by headhunters) to pay less well. David Korn, European managing partner at search firm Options Group, says there’s a trend in favour of algorithmic traders who can combine equity and fixed income products, but says it hasn’t had an adverse impact on everyone else in the market: “Houses are still looking for strong algo guys – we haven’t seen any impact on pay.”

The algorithms are coming
6 Dec 2006

Algorithmic traders still on the menu
14 Jul 2006

Program traders look to algorithms in 2005
By Sarah Butcher
4 Jan 2005

作者：小凋     
   
 　最近，某些人突然醒了，对占据故宫6、7年的星巴克同仇敌忾，誓死将它赶出故宫。理由是：星巴克“是美国不登大雅之堂的文化”，开在故宫里会糟蹋中国文化。

　　可是纵观中华举国上下，除了不知道“星巴克”为何物者，其他对星巴克稍有一知半解的人，哪一个不视它为“雅堂之雅”、只恐巴结不上？

我们从小资说起。所谓“小资”，要是不以中产为起点，说它矫情不为过吧。在小资当中，一句很经典的流行话就是：我不在办公室，就在星巴克；我不在星巴克，就在去星巴克的路上。看看多自豪！泡星巴克，已经是小资们生活中不可或缺的节目。毫无疑问，这杯名叫星巴克的咖啡，是小资的标志之一。

　　而另一例拥有星巴克的自豪，则多少有些让人心酸。那位来自农村的孩子，终于有一天可以在城市的空间喘口气宣称：我奋斗了18年才和你坐在一起喝星巴克！这时候，和城里人一起喝星巴克，标志着一种身份的认可，地位的平等。虽然我不能为这种自豪面露一丝微笑。

　　我不知道星巴克这种在美国不登大雅之堂的文化，怎么会从1999年开始，就以大雅之态大摇大摆地登陆中国北京、上海等大都市，就能与中国大商家联手，落户高档商务区，就能让小资或者伪小资的有钱人青睐，让奋斗不足卡包不丰的人向往。但一个不得不承认的事实就是，星巴克始终摆放在贵族的休闲席上。

　　同学出国10年回来，发现北京和上海开了很多的星巴克，由此感叹国内的生活真是很进步了。北京的小资们评价，最早的星巴克可以说“达到了谈笑皆鸿儒，往来无白丁的水准”。那时候，星巴克里老外居多，身着高级西装的洋行买办也多，美女更多，一半以上人的顾客都不是操中文或者国语在交谈。我一位特别爱见外国男士的女友，每天下午都要抱着笔记本去星巴克，纤细的中指食指间夹一只ESSE，在假壁炉边的沙发上占据一个位置，然后煞有介事地用笔记本进行写作，用小杯的咖啡打发整个下午的时光，体验那种很洋很派的感觉。

　　如今，北京、上海之后，星巴克已在全国14个二级城市开设分店，其中包括天津、杭州、宁波、南京、苏州、无锡、昆山、常州，以及青岛——中国第一个由星巴克公司独立经营的市场。这个分布让排除在外的其他人不舒服了。

　　于是有人提出“中部谁将最先拥有星巴克”的荣耀猜想，湖南人赶紧证明：“长沙去年就有了”而一句“星巴克绕开武汉，挺进成都”让许多武汉人心里酸溜溜的。怎么说武汉也是个大武汉，可以没有狗不理，可以没有热干面，怎么能没有星巴克呢？当然也有人反驳：“为什么非得有呢？有就可以超过上海了？自己发展起来了他自然会来。”可见，星巴克还是和发展连在一起。

　　这就是事实，你不得不承认。从一个地区到一个城市到一个人，国人的认知就是：星巴克与高档、情调、经济发展、有钱有闲连在一起了，就像牛头村不可能有连锁店，芙蓉镇不可能有麦当劳，经济基础不雄厚就不可能有星巴克！鄙视也没用。

French quants have long ruled the roost in London, but they could soon be knocked of their perch by an influx of talent from Eastern Europe.

Hemendra Rai, quantitative trading and analytics practice leader at recruitment firm Huxley Associates, says banks are increasingly interested in hiring quants from Eastern Europe and China. Former (and current) communist countries have a strong science pedigree, says Rai. The best students in those countries have typically competed in region-wide ‘olympiads’ – science and maths focused competitions not dissimilar to US spelling-bees. Rai says olympiad winners are highly prized by banks. “I’ve placed mathematicians who’ve won first place in the Romanian Olympiad and gone on to study in the UK for their PhD,” he tells us. “Some banks have even begun specifically asking for olympiad winners to fill entry-level quant positions.” The prospect of a flood of highly skilled Romanian and Bulgarian quants could therefore come as bad news to students at the likes of France’s Ecole Polytechnique, traditionally London banks’ preferred source of quantitative talent. It could also put a dampener on efforts to increase the flow of quants from the UK education system. Rai says UK universities are already catching up. Annabel Biddle, head of European graduate recruitment at Bank of America, agrees. The French schooling system is more specialized and vocational than in the UK, says Biddle: “Similar talent is available in the UK, however it is more challenging to identify and target those candidates with these particular quant skills." Oxford, Cambridge, Imperial and University College London are apparently good places to look.

How much will senior structured credit professionals earn this year? Lots, according to a survey of leading structured credit search firms.

The survey, by Structured Credit Investor, suggests managing directors in structured credit will receive an average salary in excess of £125,000 this year, plus a bonus of more than £2m. The best paid structured credit pros are working in sales positions. Managing directors in structured credit sales receive average salaries of £127,721 and bonuses of £2,162,204. By comparison, structured credit traders at managing director level command an average salary of £133,000 plus a bonus of £1,781,906 (to be precise). Unsurprisingly, pay for structured credit expertise exceeds that for standard credit products: structured credit pros now earn an average of 30% more than their credit flow colleagues. The differential is highest at executive director level, where pay is a more impressive 35% more. Shaun Springer, chief executive of search firm Napier Scott – one of those consulted for the survey – said structured credit professionals have had a good year: “At some banks credit derivatives people haven’t done as well they’d expect, but generally speaking it’s been a fantastic year for the industry – lots of new products, far more players, far more liquidity and the spreads have been very favourable.” Average salaries and bonuses, structured credit pros Executive director, structured trading Salary: £107,193. Bonus: £1,076,796 Executive director, structured sales Salary: £114,440. Bonus: £916,130 Director, structured trading Salary £90,206. Bonus: £632,581 Director, structured sales Salary £96,070. Bonus: £708,730   Bonus cheer for favoured few 15 Nov 2006 Corporate financiers and structured product specialists will overflow with bonhomie. Interest rate sales teams will contain more than their fair share of scrooges. And there will be disappointed faces all round. These are among the predictions of the Armstrong International bonus report. The latest of its ilk, Armstrong’s report is based on conversations with employees at 30 banks in London. It predicts the total bonus pool will be up anywhere from 10 per cent to 30 per cent on 2005. But Matthew Osborne, a partner at Armstrong, says plenty of people will be surprised to find they’re being paid the same or less. “A lot of people have made the same money they made in 2005 and are expecting to be up 10 per cent to 30 per cent. They could be paid flat or down.” With no one knowing where markets will go next, Osborne says more effort than ever will be made to focus rewards on top performers: “Banks are determined to secure their best talent now – if there’s a slowdown towards the end of next year they know they need to have the best guys on board.” Corporate financiers and structured product specialists are likely to do best, simply because of the strength of those markets, says Osborne. For example, top performing structured credit traders are forecast to take home $2m (£1.1m) to $3m, particularly in high margin products like constant proportion portfolio insurance (CPPI), hybrid products and collateralized debt obligations of asset backed securities. If tears are to be shed, Armstrong’s study suggests they’ll flow among mid-ranking people in teams selling interest rate products to hedge funds. Although associates in the area are likely to be paid up, it predicts VP level staff in interest rate teams could see pay down 20 per cent. And if things don’t buck up next year they may be reassigned elsewhere. Armstrong International’s 2006 Bonus Report: Structured Credit Correlation credit traders: $1m+ Junior structurers:: $700,000 Debt Capital Markets Director-level originators: $500,000 to $1m Managing directors (without significant management responsibility) $1.2-$1.5m Interest Rate Derivative Sales to Hedge Funds Managing directors: $2-$3m Vice presidents Equity Research: Top managing directors$1m+ Directors: $700,000 Vice presidents:: $500,000 Corporate Finance Managing directors: $1m-$3m Directors: $1.5m for outperformers, $1.2-$1.3m for the rest Source: Armstrong International

H1B申请数据, 透露出高盛年终奖人均六十二万美元的秘密

年关将近, 又到了年终奖成为热门话题的时候. 但这个星期, 搅得美国上下最心神不安
, 议论纷纷的, 不是自己或者同事的红包, 而是高盛的.

我星期三清晨在跑步机上第一次听到了这个消息, 大约是说今年华尔街年终奖的总额是
三百八十亿美元, 高盛占去了一百六十多亿. 虽说高盛全世界有两万五千多员工, 平均
下来, 人均六十二万. 美元!

晚上回家, CBS的晚间新闻, 又在大幅报道这消息. 那些镜头上美国人民熟悉的名记者
们大概心里也不好受, 在采访中不断问人家怎么看, 这样公平不公平. 一个人力资源公
司的总裁说, 只能这样, 否则人才就跑道其他行业去了. 如果你不爽, 就让你的孩子成
为一个投资银行家.

"Or marry one." CBS的女主播加上了这么一句, 结束了报道.

公平不公平姑且不论. 最优秀的人才如此集中在投资银行业, 无疑可以让以资本和利润
为衡量标准的现代经济以最高的效率运转,. 虽说我觉得这是人才资源的极大浪费, 这
不是我今天要讨论的. 我想和大家讨论讨论的, 是从h1b申请数据中透露出的高盛成功
秘密.

并不是所有的投资银行都这么成功, 有那么多年终奖金可发的. 如果我在跑步机上气喘
嘘嘘时候的记忆是正确的, 高盛一家就占去了华尔街所有年终奖金的大约四成. 其他那
么多投资银行分剩下的六成, 差距可想而知了.

高盛的经营有什么特别之处呢? 我并不是研究这行业的, 但偶然看到的一个贴子让我嗅
到了一点东西: 根据www.myvisajobs.com的h1b visa sponsor排名, 在数学和物理专业
, 最大的雇主不是哈佛或者普林斯顿, 也不是橡树岭或者洛斯阿拉莫斯, 是高盛. 我到
myvisajobs.com的排名, 报告和数据库上研究搜索了半天, 发现了下面这些有趣的数据
, 有些数据是现成的排名数据，　有些是我自己从上面的数据库里搜出来整理的:

1. 数学和物理专业, 2005年h1b visa申请的十大雇主,其他九个都是高校或者研究
机构, 唯有第一名是家投资银行, 也就是高盛,
2. 高盛在2005年一共申请了477的h1b visa, 在所有的top h1b visa sponsors中排
26位. 这477的申请分布如下, 注意，　数学专业125个, Business专业只有49个:

Occupation_Group Goldman Sachs & Co.
IT 175
Math 125
Economics 120
Business 49
Manager 7
Others 1

3. 其他的投资银行都在2005年申请了数以百计的h1b visa, 但职业分布上有微妙的
不同, 数学专业非常少, business比例大大提高. 以Morgan Stanley和Citigroup(数
据应该包括非投资银行部分)为例:

Occupation_Group Morgan Stanley Citigroup
Economics 216 144
IT 148 404
Business 12 168
Manager 3 167
Others 2 35
Math 1 39

　
好了, 以上这些数据, 已经充分说明了问题. 学好数理化, 走遍天下都不怕,直到今天,
到了美国, 对个人还是颠簸不破的整理. 对企业, 用好数理化, 是在数字时代的竞争
中立于不败之地的手段. 虽然, 我觉得这数理化的理化二字, 可以改成信统, 信息技术
的信，统计的统, 包括了对信息的编程和统计分析. 这才是真正意义上的数字化生存.
数字化生存，就是数学的生存．过去这二十年, 数理化的基础研究专业在中国成了最被
人看不起, 最没人上, 录取分数最底的专业, 大家都一窝风的去读财经MBA等热门专
业, 会迟早让国家和个人付出代价.

最后，再来看看myvisajobs.com上的Ｈ１Ｂ专业工资排名, 也挺有意思的. 收入最高的
h1b职业，　排第一的，　是模特，年收入中位数$624,000!　正好是高盛今年的平均年
终奖．收入最底的，是牧师，$29, 000, 真好是人家年终奖的零头． 主啊，马克思啊
，　这是怎么回事? 种种巧合，　有时候真让人怀疑，　这一切是不是＂智能设计＂
的结果．在收入最高的二十五的h1B职业中，　几乎都是理工专业，　收入最底的25个
h1b职业, 几乎都是文科专业, 生命科学（＄42,175）除外!!! 医生排第三($120,000)
，　牙医排第四($100,400)， 数学，　化学和物理分别排在十四($65,000)，　十五($
64,525)和十八位($60,000).　考虑到这三个职业的h1b很多是在学校和研究机构的博士
后，这个工资水平应该说还是很不错的．

 

今年行情大好，街上各公司老板也笑逐颜开，发起奖金来也毫不吝啬，这不，摩根斯坦利的CEO John Mark刚刚以4020万美元的奖金创下华尔街首席执行官的薪酬新纪录，高盛就马上公布其首席执行官Lloyd Blankfein今年可以拿到5340万美元，其实华尔街五大投行高盛、摩根斯坦利、美林、雷曼兄弟和贝尔斯登的薪酬都不会相差太大，不然马上会给别人挖走，五大的总奖金据现在情况估计，起码比去年高了三成。

几千万美元就是金融界的顶级薪水嘛？NO。对冲基金经理还没有发言呢。

听一个圈子里面的朋友说，今年收入超过十亿美元现金的对冲基金经理人数据他所知就超过了2位数。

高盛２.6万雇员平均每人分得的６６万美元年终奖虽为金融业年终红利的新记录，但相对于对冲基金经理和私人股权基金经理的收入就真算是毛毛雨了。

这是city里一个著名的猎头公司合伙人 Dominic给出的为什么他们要招phd的理由:
Why PhDs are good:
It proves you are at least mildly smart.
You have a wider range of techniques for problem solving and analysis than a MSc or BSc
You can finish something without a teacher hassling you.
You can work without supervision.

 

国基姆一家四口两周前在俄勒冈州偏僻山区失去踪迹。救援人员凭借手机短信，终于在4日成功解救母亲凯蒂和两个女儿，但是父亲詹姆斯始终下落不明。6日，詹姆斯的尸体在一处山沟被发现。
　　
　　失踪者是30岁的凯蒂·基姆、35岁的詹姆斯·基姆和他们的两个女儿，4岁的佩内洛普和7个月大的萨宾。詹姆斯·基姆的尸体被发现时，离他停在俄勒冈克拉马斯山麓一条偏僻小道上的汽车有7英里远，两天前，他的妻子和两个女儿被人从抛锚的车里解救出来。
　　
　　11月26日，住在旧金山的基姆一家结束感恩节旅行，驾车返程途中在俄勒冈州沿海附近山区迷路，与外界失去联系。在基姆一家人失踪的9天里，由于天气寒冷，他们用汽车加热器取暖，直至车中汽油耗尽，夜里一家人只好抱成一团取暖。由于大雪封山，缺少食物和饮水，凯蒂用自己的乳汁延续两个女儿的生命。
　　
　　在获救3天前，詹姆斯离开母女3人，独自下车寻求救援，自此失去联系。詹姆斯离开车时，身上只穿了毛衣、厚夹克、牛仔裤和网球鞋。天气预报显示该地区温度在零下7摄氏度到零下1摄氏度之间。
　　
　　基姆一家失踪后，两名工程师富卡和帕格斯利通过查询手机通讯记录，发现当地时间26日凌晨1时30分左右，有人给基姆一家的一部手机发过2条短信。两人由此追踪到传输手机信号的基站，缩小基姆一家搜寻范围。
　　
　　此后，富卡和帕格斯利运用计算机软件制作了一张山区搜救图，运用他们丰富的技术知识和旅行经验，对基姆一家行车路线做出合理推断，终于成功寻找到被困在车中的凯蒂和两个女儿。
　　
　　现在，凯蒂母子三人身体及精神状态良好，正在医院接受恢复治疗。

日记日记，天天要记.没有时间，只能放弃。

(顺便崇拜一下诗坛芙蓉姐姐赵丽华)

浅析佩帅阵容：贝尔萨的防守+罗米的进攻

(5月15号初稿,5月18号终稿)

我们首先来看看阿根廷队从贝尔萨到佩克尔曼的演变过程。

一．贝尔萨时代

贝尔萨口口声声说最好的防守就是进攻。但其实因为进攻手段单一，其所宣扬的进攻一直效率低下。反倒是其防守从来不差。究其原因主要是他讲究控球和丢球以后的就地反抢。到了后贝尔萨时代，他的进攻思路已经转变，尤其讲究领先后的倒脚，而不是一直疯狂的进攻了。于是在奥运会和预选赛上出现了一支新的阿根廷国家队，其防守体系是以海因策+阿亚拉+科洛奇尼的三中卫加上以索林+小马+路岗的三后腰。萨穆埃尔被抛弃从这个时候就打下了伏笔。这支国家队的阵型仍然是3313：进攻是1+3，防守是3+3。实践证明：这支阿根廷队的防守体系相当成型，比赛效果也出奇的好。

1。奥运会上0失球

2。预选赛上防守纪录也特别好。

3。美洲杯主力三中卫还是这三个人，结果是一路表现出色，最后决赛中点球惜败。

从预选赛到奥运结束，整个后防体系的磨合是相当成功的。

预选赛唯一一次例外是对巴西，当时3中卫的主力是奎罗加、萨穆埃尔、海因策；三后腰是索林+小马+诘兀把犯?lt;STRONG>萨穆埃尔负责领导整个后防线。结果罗纳尔多上演了帽子戏法，而路岗表现不佳于61分钟被爱马尔提前换下。

1。罗纳尔多接队友传球后，带球突入禁区，在禁区内被阿根廷球员海因茨绊倒，主裁判判罚了点球。

2。第67分钟，罗纳尔多的突破再次造成了阿根廷球员犯规，马斯切拉诺将其放倒在禁区内，主裁判判罚点球。

3。伤停补时阶段，罗纳尔多在禁区被被卡巴列罗扑到，主裁判判罚了本场比赛的第三粒点球，罗纳尔多第三次将点球罚进，上演了点球帽子戏法。

从以上可以看出没有了阿亚拉，整个后防线多么的混乱！！！！！

三中卫的时候萨穆埃尔可堪阿亚拉第一替补吗？？？答案是NO。萨穆埃尔那闷头闷脑的气质在骨子里重视防守的意大利教练手下没有问题，但是不适合作为风格类似西甲崇尚进攻的阿根廷队和皇马的后防线领袖！！！他个人能力可能没有问题，但是不具有指挥能力。

萨姆埃尔能打左中卫吗？答案也是NO.左中卫要求速度快，转身快，启动也快.萨穆埃尔中后卫出身,在拖后中卫上成名,这些条件都比不上中卫出身边卫成名的海因策和库福雷.在国米他可以有速度快转身灵活的科尔多巴替他补位,但是阿根廷却没有这样的人才.

那么阿亚拉的第二替补是谁？答案其实很清楚：天生领袖米利托！西甲最佳中卫之一的米粒托！巴萨和利物浦苦求而不得的米粒托！！

二．佩克尔曼时代

佩帅接过帅印以后，首先想到的是以罗米为中心，恢复阿根廷的传统打法，好好的改造一下阿根廷以前那种只开花不结果的局面。既然进攻是以罗米为主，防守应该怎么办呢？选择有两个：

1。继承贝尔萨时代磨合成型的3中卫打法，阵型为3412；

这种打法有两个好处，首先就是磨合时间长而且防守纪录好；其次是4中场对罗米的保护能够确保其舒舒服服的控球，创造更多的进攻机会，也增加中场厚度。毕竟得中场者的天下。

2。模仿潜水艇的防守体系，打4312。

这个体系表面上看起来是最适合阿根廷的，主要是两点原因：第一，可以让四个顶级后卫一起上（海因策+萨穆埃尔+阿亚拉+萨内蒂），中场的三后腰可以是诸多后腰的各种组合。第二，打法会让罗米习惯，因为中场和潜水艇完全一致，甚至还有跟罗密配合了两年的左后腰索林。

于是在一系列预选赛和热身赛上佩帅开始测验这两种阵型：

二队对玻利维亚3412（2：1）：（布迪索+米利托+库弗雷）/（马克西+杜舍尔+坎比亚索+斯卡罗尼）

一队对哥伦比亚4312（1：0）：（萨内蒂+海因策+阿亚拉+索林）/（坎比亚索+马斯切拉诺+冈萨雷斯）

二队对厄瓜多尔3412（0：2）：（科洛奇尼+米利托+萨穆埃尔）/萨内蒂+杜舍尔+坎比亚索+基利。萨穆埃尔领衔的后防线再次失利！！！其缺乏领导力的缺点再次暴露无遗。

一队对巴西3412（3：1）：（海因策+阿亚拉+科洛奇尼）/（基利+索林+小马+路岗）

对秘鲁3412（2：0）：科洛奇尼+阿亚拉+加布里埃尔-米利托/路冈+巴塔格利亚+索林+基利

对乌拉圭3412（0:1）: 邦奇奥+阿亚拉+萨穆埃尔/路冈+巴塔格利亚+索林+基利

索林在连续三场比赛都成功的演绎了后腰这个角色，既奠定了自己主力后腰的位置，又证明自己对得起队长这个袖标。值得一提的是，索林并不是第一次担任队长。早在95年4月份，佩克尔曼就把索林任命为阿根廷20岁以下青年队队长，带队出征卡塔尔世青赛，索林也第一次同恩师一起站在世界最高领奖台上。

重点看看联合会杯：

对突尼斯3412（2：1）:科洛奇尼+冈萨洛+海因策/马克西+桑塔纳+贝尔纳迪+索林。卢克斯连续犯错被判点球。

对澳大利亚3412（4：2）：科洛奇尼+萨穆埃尔+海因策/萨内蒂+桑塔纳+贝尔纳迪+索林

对德国3412（2：2）：科洛奇尼+萨穆埃尔+海因策/扎内蒂+坎比亚索+贝纳尔迪+索林

对墨西哥3412（1：1）：科洛奇尼+米利托+海因策 /萨内蒂+桑塔纳+坎比亚索+索林

前面四场很明显在试验谁是拖后中卫第一替补，因为科洛奇尼和海因策都被固定下来作为左右两边主力中卫了。这是给萨姆埃尔的最后机会了，但是萨姆埃尔没有把握住。他负责的两场比赛对德国和澳大利亚，都是欧洲球队讲究头球，但也恰好也是失球最多的比赛。这说明萨姆埃尔在对付欧洲球队的时候也没有好的表现。

对巴西4312（1：4）：萨内蒂+科洛奇尼+海因茨+普拉森特/坎比亚索+贝尔纳迪+索林

在对付巴西人的时候佩帅决心换下萨姆埃尔改打4后卫。结果大家也知道了，由于普拉森特太差了，所失四球完全来自右路进攻。希希尼奥上演了助攻帽子戏法，罗比尼奥野在右侧助攻卡卡进球。而且中场极其失控，整场比赛阿根廷队没有创造出太多的机会，罗米这次比赛表现还是很活跃的，但是很明显得到的支援不够。最终导致球队惨败！

对英格兰4312（2：3）：萨内蒂+阿亚拉(76'科洛奇尼)+萨穆埃尔+索林。 这条后防线是目前来说除了左后卫海因策之外的最强组合了。但是事实证明，在换下罗米以后，中场失去控制。没有了罗米，三后腰也失去了对后防线的保护能力。于是这条后防线也变的不堪一击。最后三分钟被英格兰连入两球就是一个很好地证明。

对克罗地亚（2：3）：媒体报道是4222，实则为3322，因为邦齐奥总是在右中场位置。布迪索+萨穆埃尔+科洛奇尼/坎比亚索+德米凯利斯+庞齐奥（因为庞齐奥一直靠中场，所以大家才看到科洛奇尼总是在右边单挑普尔韶。甚至打到后来，克罗地亚人干脆就总是简单的右路起球吊给普尔韶。）

潜水艇打阿森纳4312（0：1）：这场比赛罗米被彻底冻结。狭小的场地再加上阿森纳强大的5人中场让佩克尔曼看到了3后腰的局限性。

从以上比赛可以看出来两点：

1．打4后卫国家队的三人中场受到两个局限：第一，罗米下场以后的中场控制能力不够；第二，当碰到强大对手的时候罗米很容易被冻结（对阿森纳的5人中场）或者孤立（对巴西）。但当中场是4人的时候甚至连强大的巴西中场组合也不是我们的对手。

2．打四后卫的时候球队的四个后卫之间磨合时间不够，而且效果并不好。面对死亡之组的威胁，我们没有太多时间。

放弃萨内蒂有一个过程。其实从最开始佩帅让他作为副队长证明了他在佩帅心中的地位，因为4后卫中他是当仁不让的右后卫首选。但是从这次入选大名单可以看出，通过一系列的比赛佩克尔曼渐渐倾向于三后卫。或者说，因为索林的位置特殊，类似于左路一条通的自由人，导致必须要有左中卫替他补位，形成了事实中的三中卫+一个自由人。（详细分析请参考我的一个老帖：http://bbs.argstorm.com/dispbbs.asp?boardID=2&ID=200550&page=1）

这时候萨内蒂的地位变得很微妙了。他需要跟能够打右前卫的斯卡洛尼进行PK.很显然，因为中后卫出生的德米组织能力欠缺而且跑动范围不足以给三后卫足够的保护，从而促使在德米和帕拉希奥两人之间佩帅选择了帕拉希奥以增强边路进攻，那么这时候佩帅心中可以打后腰的多面手斯卡洛尼已经变得更加重要。而且当我们真的需要突破能力的时候我们已经有了速度更快进球能力更强突破能力也不差的梅西和马克西。最终佩帅只能艰难的放弃了只具有边路突破能力的阿根廷副队长萨内地。

(换句话说，因为德米不太符合佩帅的选人要求：有一定组织能力，有一定防守能力，跑动范围大。而中后卫出身的德米只是一对一能力强，头球好。在拜仁他可以跑动少一点，因为德国人无他，都只是一些战术意识超强体力充沛跑动范围极大的家伙，所以他少一点跑动无所谓。

斯卡洛尼从这点上来说，作为路岗的替补（当然他没有路岗的创造力，组织能力也不如），是基本符合佩帅选人要求。他有过一段时间稳定在右后腰位置，也在右前卫和右后卫之间经常轮换。而萨内蒂这个时候则有点显得多余了。他没有组织能力，打后腰横向拦截也不如斯卡洛尼。他的确偶尔客串过后腰，但是哪能跟斯卡洛尼这样稳定的打过后腰的人选相比呢。萨内蒂的突击能力也基本没什么意义，因为马克西和梅西都是速度快突破好得分好的选手，而且帕拉希奥据说突破也非常犀利，得分能力也强。所以徒具有突破能力的萨内蒂落选理由自然渐渐变得充分起来了！！！

此外，其实我们的这个3412完全不同于2002年夺冠的巴西人的3412。那时候他们的两个边路卡福卡洛斯是能上能下纯粹的突击手。而我们的两个边路索林和路岗都不是速度快的突击手，而是具有一定防守能力和组织能力的后腰。进一步说白了我们其实在中路堆积了四个后腰，因为我们的思路是要在中场多控球，这样才能发挥里克尔梅的威力。而不像02年巴西，他们打得是防守反击，特别强调边路快速突击手。这样萨内蒂也自然没有入选的必要了。

最后总结名单人选：

后防领袖：阿亚拉，替补米利托。两个人都是天生的后防领袖，西甲最好的两个中卫。

左中卫：海因策，替补库福雷。两个人都打过左后卫和左中卫。

右中卫：科洛奇尼，替补布迪索。两人都打过右中卫和右后卫

主力后腰：索林，坎比，小马，路岗

索林可以是坎比和小马的替补。

路岗的替补是斯卡洛尼，两者都是具有一定组织能力和防守能力能打右后腰和右前卫的人。

马克西和梅西类似，两者都是速度快能打前锋和前腰而且突破能力也很强的人。

特维斯和萨维奥拉互为替补，他们都是能打中锋能打二前锋的人。

埃玛尔是罗米的另一种风格的替补。

克雷斯波的替补是克鲁斯。

帕拉希奥风格独特，是个佩帅的一个奇兵。用得好很有可能是真正理解里克尔梅传球的另一个风之子！！！甚至可以认为这个人才是真正顶掉萨内蒂入选名额的人。

在罗米开始脱衣服的时候，齐达内也已经开始脱，这是两个人的默契,

紧紧拥抱，相互祝福,

没有多余的话，两个内向的人就此分开,

绿茵场上仅存的两位古典艺术大师诀别，从此以后只有罗米独舞..........