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FRM®一级风险管理基础备考公式（1）
文章来源：网络
发布时间：2021-12-30 08:36
阅读：549次
本期小编继续讲述FRM®一级风险管理基础备考公式的第二部分，一起来看吧。
Autoregressive (AR) Process
The first-order autoregressive process [AR(1)] is specified as a variable regressed against itself in lagged form. Mean is 0 and variance is constant. yt = d + Φyt–1 + εt
where:
d = intercept term
yt = the time series variable being estimated
yt–1 = one-period lagged observation of the variable being estimated
Φ = coefficient for the lagged observation of the variable being estimated
Trend Models
A linear trend is a time series pattern that can be graphed with a straight line:
yt = δ0 + δ1 t + εt
A nonlinear trend is a time series pattern that can be graphed with a curve. It can be modeled using either quadratic or log-linear functions, respectively:【点击免费下载>>>更多FRM学习相关资料】
yt = δ0 + δ1t + δ2t2 + εt
ln(yt) = δ0 + δ1t + εt
Seasonality
Seasonality in a time series is a pattern that tends to repeat from year to year. There are two approaches for modeling and forecasting a time series impacted by seasonality: (1) regression analysis with seasonal dummy variables and (2) seasonal differencing. Combining a trend with a pure seasonal dummy model produces the following model:
Spearman’s Rank Correlation
Step 1: Order the set pairs of variables X and Y with respect to set X.
Step 2: Determine the ranks of Xi and Yi for each time period i.
Step 3: Calculate the difference of the variable rankings and square the difference.
Where n is the number of observations for each variable and di is the difference between the ranking for period i.
Kendall’s Tau (τ)
Where the number of concordant pairs is represented as nc (pair rankings in agreement), and the number of discordant pairs is represented as nd (pair rankings not in agreement). Simulation Methods Monte Carlo simulations can model complex problems or estimate variables when there are small sample sizes. Basic steps are (1) specify datagenerating process, (2) estimate unknown variable, (3) repeat steps 1 and 2 N times, (4) estimate quantity of interest, and (5) assess accuracy of standard error and increase N until required accuracy is achieved.
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Ethan
讲师认证：擅长采用Matlab，Python和C++进行市场风险和信用风险建模和回测
FRM持证人，CFA持证人，CQF持证人，现任职于某对冲基金量化投资部门，从事风控管理以及交易策略制定，在风控方向有近10年工作经验。擅长采用Matlab，Python和C++进行市场风险和信用风险建模和回测，授课稳中有进，举例生动，专业干货多的同时又能顾及细节，积极与学员互动答疑。耐心十足。认真实干。成功带出十数名高分优秀学生。
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