金融风险管理 (Financial Risk Management) 对编程的要求有多高?

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Emily   2018-10-16 00:01   10263   6
本题已加入知乎圆桌  欢迎来到风控时代,更多「风险控制」讨论欢迎关注。
之后想打算在投行或基金公司做market risk, credit risk analysis这一块,但现在不大明白这种工作编程运用量有多少。在linkedin上搜索伦敦的职位,有的需要编程经验,有的不需要,很不解。在国内从事相关工作的也欢迎发表意见~
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6#
Belinda917  2级吧友 | 2018-10-16 00:01:23 发帖IP地址来自
现在应用比较多的蒙特卡洛模拟、时间序列分析、回归分析等金融建模这些可以通过EXCEL、R等很快实现,倒不是很大的问题。纽约对冲基金做alpha投资的业内人士说他们现在用的最多的Python和SQL。个人感觉编程是对风控来说是必不可少的辅助工具吧。
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我们项目就叫金融风险管理,GARP关于合作的。有节关于金融风险编程的Course Discriptions是这样的:
This course focuses on the use of MATLAB, R, and SAS for financial programming and modeling. Students pick up materials such as programming basics, SQL, database operations, file operations, graphical user interface design, object-oriented programming, XML, Component Object Model (COM) client and server, and application programming interface (API). Fundamental concepts are reviewed. Students learn modeling techniques such as Monte-Carlo simulation, binomial and trinomial trees, Black-Scholes, finite difference methods, constrained and unconstrained optimization, linear and non-linear programming, heuristic optimization, mean-variance, Value at Risk, data envelopment analysis (DEA), and data mining techniques applied in risk management, and apply these in financial contexts. More specifically, students construct various applications, for example portfolio optimization with live data from the internet using various methods, option pricing using Monte-Carlo, binomial trees, Black-Scholes, asset pricing models, capital budgeting, efficiency evaluation, finding betas of stocks, risk evaluation using data mining techniques, etc., across several programming languages.


这已经偏量化了,平时应该用不到那么多。还有,现在2019年的CFA把Fintech加入了考试范围,在量化那个部分。CFA官方reading的内容:
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6.3. Risk Analysis
As mandated by regulators worldwide, the global investment industry has undertaken major steps in stress testing and risk assessment that involve the analysis of vast amounts of quantitative and qualitative risk data. Required data include information on the liquidity of the firm and its trading partners, balance sheet positions, credit exposures, risk-weighted assets, and risk parameters. Stress tests may also take qualitative information into consideration, such as capital planning procedures, expected business plan changes, business model sustainability, and operational risk.
There is increasing interest in monitoring risk in real time. To do so, relevant data must be taken by a firm, mapped to known risks, and identified as it moves within the firm. Data may be aggregated for reporting purposes or used as inputs to risk models. Big Data may provide insights into real-time and changing market circumstances to help identify weakening market conditions and adverse trends in advance, allowing managers to employ risk management techniques and hedging practices sooner to help preserve asset value. For example, evaluation of alternative data using ML techniques may help foreshadow declining company earnings and future stock performance. Furthermore, analysis of real-time market data and trading patterns may help analysts detect buying or selling pressure in the stock.
ML techniques may be used to help assess data quality. To help ensure accurate and reliable data that may originate from numerous alternative data sources, ML techniques can help validate data quality by identifying questionable data, potential errors, and data outliers before integration with traditional data for use in risk models and in risk management applications.
Portfolio risk management often makes use of scenario analysis—analyzing the likely performance of the portfolio and liquidation costs under a hypothetical stress scenario or the repeat of a historical stress event. For example, to understand the implications of holding or liquidating positions during adverse or extreme market periods, such as the financial crisis, fund managers may perform “what-if” scenario analysis and portfolio backtesting using point-in-time data to understand liquidation costs and portfolio consequences under differing market conditions. These backtesting simulations are often computationally intense and may be facilitated through the use of advanced AI-based techniques.

5#
李毅  4级常客 | 2018-10-16 00:01:18 发帖IP地址来自
中国最大的风险是政策风险,搞个模型你搞得过党和政府?
4#
omegaif  2级吧友 | 2018-10-16 00:01:17 发帖IP地址来自
不请自来,知乎首答

risk analysis可以分为quantitative和qualitative两个方面,一般而言,前者需要编程基础,后者不需要。

quantitative顾名思义是对内外部数据进行分析,从而计算资本金等数字,而计算的方法就是编程建模。

qualitative则是对进模型的数据进行内容质量上的分析,比如是否有遗漏或错误的数据,某些分类是否符合巴塞尔指引等等。

建议研究一下jd所指是哪个方面。
以上
3#
海恋空  1级新秀 | 2018-10-16 00:01:16 发帖IP地址来自
在知乎上看到很多回答感觉很多关于Risk Management的回答都是基于Pricing的。其实自己读书的时候也一直以为计算那些数值是最为重要的事情。但是实际工作找到了市场风控这个岗位,区别于风险建模组,也就是传统意义上的风控经理,才发现重要的skill set并不是数理编程那一套。
首先作为一个Junior, 好的数理背景和编程意味着你可以为组里优化更多现有的流程,更快的理解各个模型的含义,从而很快进入角色。但是真的做到管理层的,大多数是trading半路转risk的老板。这些人会编程吗?一点也不会。但他们对产品,交易都有很好的理解。
卖方市场风控经理,权力还是比较大的。Trader 一旦出现恶意敞口超过limit 是可以直接叫他走人的。可以交易的产品类别也由风控决定。具体工作包括每天看风险报告,了解市场变化,了解自己的desk 主要risk集中在哪里,设置Risk Limit Framework, 设计如何measure 产品风险,有新产品了怎么算risk metrics,作为模型的使用人,也会看模型用的合不合适。Risk sensitivities, VaR, Stress是主要的metric. 应该说主要skill set 跟trader更加接近,接触最多的也是trading组。Market sense 和对产品的了解更加重要,最后还有最重要的就是interpersonal skills.
2#
Zhang Alex  3级会员 | 2018-10-16 00:01:15 发帖IP地址来自
分两类金融机构,自建派和购买第三方派。自建派得找一个完备的团队去开发,都是上百号人,有外包开发的有测试的。购买第三方的就是得有自己的验证团队,这个对编程要求比较高。目前,大部分风险指标都是eod的,但是也有个公司颠覆行业可以做到real time的,满满的都是技术。
1#
匿名用户   | 2018-10-16 00:01:14 发帖IP地址来自
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