Actuarium

Formula sheets

Every formula on the syllabus, rendered natively, with the traps examiners set for each. Print one per exam and keep it by your desk.

MAS-II Modern Actuarial Statistics II

CAS

Bühlmann credibility Z=nn+k,k=EPVVHM=E[Var(XΘ)]Var(E[XΘ])Z=\frac{n}{n+k},\qquad k=\frac{EPV}{VHM}=\frac{E[\operatorname{Var}(X\mid\Theta)]}{\operatorname{Var}(E[X\mid\Theta])}

Bühlmann–Straub with exposures mim_i: Zi=mimi+kZ_i=\dfrac{m_i}{m_i+k}.

Limited fluctuation full credibility (frequency, Poisson): nF=(z(1+p)/2r)2n_F=\left(\dfrac{z_{(1+p)/2}}{r}\right)^2; partial credibility Z=n/nFZ=\sqrt{n/n_F}.

Gamma–Poisson conjugacy: prior Γ(α,θ)\Gamma(\alpha,\theta), observe x\sum x claims over nn periods \Rightarrow posterior Γ ⁣(α+x,  θ1+nθ)\Gamma\!\left(\alpha+\sum x,\;\dfrac{\theta}{1+n\theta}\right).

Metropolis–Hastings acceptance   α=min ⁣(1,π(y)q(xy)π(x)q(yx))\;\alpha=\min\!\left(1,\dfrac{\pi(y)q(x\mid y)}{\pi(x)q(y\mid x)}\right).

Ridge / lasso   minβyXβ2+λβ22\;\min_\beta \|y-X\beta\|^2+\lambda\|\beta\|_2^2 (ridge), +λβ1+\lambda\|\beta\|_1 (lasso).

Traps to remember

  • Using total observations instead of per-risk exposure in Bühlmann–Straub.

  • Forgetting that limited-fluctuation standards for aggregate losses add the severity CV² term.

  • Confusing lasso (sparse) with ridge (shrinks but keeps all).

  • Treating a random intercept model's variance components as fixed-effect coefficients.

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