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Berkeley MFE 一面面经 + 一些关于mfe的总结

[日期:2016-06-30] 来源:ChaseDream论坛 作者:y658zhan [字体: ]

大家好,两天前刚刚面了伯克利的MFE。乘着还没忘记内容,来一发面经。

背景: 加本 + 168、158、4.5 + 无托福 + 数学专业 + gpa 90%~ + Quantnet c++。 推荐信一个是实习老板出的强推,买方基金。 一个是统计教授出的,估计是模板推荐信,没啥花头。

整个申请过程从五月底开始准备,六月十四号递交,六月二十一号被通知面试,时间是 六月二十三号下午(ET)。

准备比较仓促,翘课两天刷了一遍heard on street。根据自己比较弱的 BS model + derivative 这一块,又做了绿皮。需要指出的是,heard on street 中derivative 没做,计算太过复杂,感觉不会考。何况他的root 是在 stochastic 里面,本科生也没学过,所以没必要细究。

面试很长 1小时长面。 两个current students。 先walk through resume,然后对方根据我的简历,问了其中一个 linear regression的project。结果越挖越深,最后问到了linear model 的 assumption。我也没想到,它提示 stock price 的 assumption 我才意识到他要的答案是geometric brownnian motion。但是我的model和这个没啥鸟关系。不过也算是答对了。

这之后就开始正式做题了。现实数学,definition of continuity, differentiability, etc. they are easy. then its linear algebra, they tested cholesky decomposition. I indicated that it has been years since I last used linear algebra, but with their hint, I was able to do that question. 还有问了i^i 是多少,我刚想了两秒,对方说你有没有学过complex analysis 我说没有,他说那skip。

之后是概率。问了几个简单的contional probability。然后是finance 问了black schole input variables,assumptinos。然后问了theta定义,还有theta 符号。在之后是american call option exercise time. and why always exercise at end of the term.

这个时候我基本上已经exhausted了,对方说还要问c++。我心理一万匹草泥马。。。问了virtual function & call by reference vs call by value.这些都比较简单了。最后一个trick, python is call by value or call by reference or call by pointer. 我说了 reference 对方说,答案是call by object。

至此所有问题结束。然后我问了你们为什么选择berkeley。一个说了one year program 另一个说主要因为west coast buy side 比较多。

最后我问我表现如何,对方表示,you have the skillsets as indicated by the resume, the interivew is particularly long because we wanted to test if you are really as good as your resume suggest 之类的。

之后就挂断了。

regarding mfe, I think there are a few important sections. 1. Math, 2. CS, 3. Internship/work exp 4. finance knowledge.

let me go through the list one by one.

  1. Regarding math, it should include most of the first year/second year courses. calc 1.2.3 Linear algebra 1. 2. ODE PDE

  2. regarding CS, C++ is a must nowadays, (if your target is tier 1.) Schools such as Baruch Berkeley CMU explicitly requires that. second tier schools may have a lower requirement on this seciton. Such as MIT. Also, you will need python matlab and R for sure. It is a plus if you have applied your cs skills in real work. Berkeley has now put machine learning as a requirement, but other schools haven't (as far as I know).

  3. internship, you need to find a relevant internship in finance. It is best if related to quantitative side. For myself, I got a internship in citi, and another well-known buy side firm in canada. Nothing is quant, but it is still a big plus because grads from Berkeley go to these 2 firms a lot. and I already have future internship & connection in the buy side firm. So it is like a reassurance for berkeley that I will be able to get a good job after graduation. which makes them more happy to recruit me.

  4. finance. Regarding CFA level 1, I heard it is not very necessary. but definitely doesn't hurt to have it. but you should know the quantitative part of finance, black sholes option priceing methods, the greeks and so on. You should have a general knowledge of these things to maximize your chances of being admitted.

Let me know if you have any questions, I will post answers and keep editing this post to get it updated..

懒得打中文,大家就这么看吧~

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原文引自:
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