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警方通报辅警执法直播中被撞飞:犯罪嫌疑人已投案

警方通报辅警执法直播中被撞飞:犯罪嫌疑人已投案

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当前速看:Stochastic Methods in Finance (1)

来源:博客园

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略去,详见测度论专栏中的文章


(资料图)

Expectations

令 \(X\) 为 \((\Omega, \mathcal{F}, P)\) 上的随机变量,\(\mathbb{E}[X]\) 为其期望。一些期望的特殊表示如下:

  • \(X: \Omega \rightarrow \mathbb{R}\) 为简单函数,即,\(X\) 在有限集 \(\left\{x_{1},\ldots, x_{n} \right\}\) 中取值,则:

    \[\mathbb{E}[X] := \sum\limits^{n}_{i=1} x_{i} P(X = x_{i})\]
  • \(X \geq 0\) almost surely,则:

    \[\mathbb{E}[X] := \sup \left\{ \mathbb{E}[Y]: ~ Y \mbox{ is simple, } ~ 0 \leq Y \leq X \mbox{ almost surely. } \right\}\]

    注意,非负随机变量的期望可能为 \(\infty\)。

  • \(\mathbb{E}[X^{+}]\) 或 \(\mathbb{E}[X^{-}]\) 其中之一是有限的,则:

    \[\mathbb{E}[X] := \mathbb{E}[X^{+}] - \mathbb{E}[X^{-}]\]
  • \(X\) 为一个向量,且 \(\mathbb{E}[|X|] < \infty\),则:

    \[\mathbb{E}\Big[\left(X_{1}, \ldots, X_{d}\right)\Big] := \Big( \mathbb{E}[X_{1}], \ldots, \mathbb{E}[X_{d}] \Big)\]

Jensen"s Inequality (琴生不等式)

令 \(X\) 为一个随机变量,\(g: \mathbb{R} \rightarrow \mathbb{R}\) 为一个凸函数。那么当 \(X\) 的期望存在时:

\[\mathbb{E}[g(X)] \geq g\left(\mathbb{E}[X] \right)\]

若 \(g\) 为严格凸函数,则以上不等式可随之写为严格大于的形式(除非 \(X\) 取常数值)。

  • 注(Convex function):

    函数 \(f: X \rightarrow \mathbb{R}\) 称作一个凸函数,如果:

    \[\forall ~ t \in [0, ~ 1]: ~ \forall ~ x_{1}, x_{2} \in X: ~ f\Big( tx_{1} + (1-t) x_{2} \Big) \leq t\cdot f(t x_{1}) + (1-t) \cdot f(x_{2})\]

Self-Financing Condition

A self-financing strategy is defined as a consumption stream \((c_{t})_{t\geq 0}\) which follows:

\[(c_{t} - c_{t+1})\cdot P_{t} = 0 \qquad \quad \mbox{for } \forall t \geq 0\]

Numeraire (计价单位)

  • \((\eta_t)_{t\geq 0}\) 为 previsible process.

  • \(\eta_{t} \cdot P_{t} > 0\) almost surely, i.e., \(P(\eta_t \cdot P_{t} > 0) = 1\).

  • \((\eta_{t})_{t\geq 0}\) 满足 self-financing condition, i.e.,

    \[(\eta_{t} - \eta_{t+1}) \cdot P_{t} = 0 \qquad \quad \mbox{for } \forall t\geq 0\]

    这实际上意味着:

    \[\eta_{t} \cdot P_{t} = \eta_{t+1} \cdot P_{t} \qquad \qquad \text{for } ~ \forall t \geq 0\]

    注意,以上式子中两侧的 \(P_{t}\) 不能随手约去,因为等式两边是两个向量的内积运算。

Numeraire Asset

  • A numeraire asset is an asset with strictly positive price.

  • 若 asset \(i\) 为一个 numeraire asset,那么对于 \(\forall t \geq 0\),定义 constant portfolio \(\eta\):

    \[\eta_{t}^{j} = \begin{cases}1 \qquad \text{if } j = i\\0 \qquad \text{otherwise}\end{cases}\]

    为一个 numeraire portfolio。

Investment-Consumption Strategy

\[\begin{align*}c_{0} & = x - H_{1} \cdot P_{0}\\c_{t} & = (H_{t} - H_{t+1}) \cdot P_{t} \qquad \qquad \mbox{for } t \geq 1\end{align*}\]

其中 \(x\) 为初始财富。

Terminal Consumption Strategy

\[\begin{align*}c_{0} & = -H_{1} \cdot P_{0} = 0\\c_{t} & = (H_{t} - H_{t+1}) \cdot P_{t} = 0 \qquad \qquad \mbox{for } 1 \leq t \leq T-1\\c_{T} & = H_{T} \cdot P_{T} \geq 0 \\\mbox{and} \qquad \qquad \\P( &c_{T} > 0) > 0\end{align*}\]

其中 \(H\) 为 previsible process,non-random \(T > 0\) 使得以上 holds almost surely。

Pure Investment Strategy

对于 \(\forall t \geq 0\),每一期持仓 \(H_{t}\),但将每一期的 consumption \(c_{t}\) 不用于消费,而是用于投资 numeraire portfolio \(\eta_{t}\)。

Theorem. 局部鞅 \(\rightarrow\) 鞅的充分条件 (local martingales to true martingales: sufficient condition)

令 \(X\) 为一个离散或连续的 local martingale,令过程 \((Y_{t})_{t\geq 0}\) 满足:

\[\mbox{for } ~ \forall ~ s,t, ~ 0 \leq s \leq t: ~ |X_{s}| \leq Y_{t} \mbox{ almost surely}\]

若 \(\mathbb{E}[Y_{t}] \leq \infty, ~ \mbox{ for } ~ \forall ~ t \geq 0\),那么 \(X\) 为一个 true martingale。

证明:

由于 \((X_{t})_{t\leq 0}\) 为一个 local martingale,根据定义存在一个 stopping time series (localizing sequence):\((\tau_{N})_{N\geq0}\),满足 \(\lim \limits_{N \rightarrow \infty} \tau_{N} = \infty\),使得对于 \(\forall ~ N \geq 0\),\(\Big(X^{\tau_{N}}_{t}\Big)_{t \geq 0} = \Big(X_{t \land \tau_{N}}\Big)_{t\geq 0}\) 为 true martingale。

首先证明 \((X_{t})_{t\geq 0}\) 可积。对于任意 \(t \geq 0\),取任意 \(T \geq t\),根据条件:\(|X_{t}| \leq Y_{T}\) almost surely。又因为:\(\forall ~ T \geq 0: ~ \mathbb{E}[Y_{T}] < \infty\),那么:

\[\mbox{for } ~ \forall ~ t \geq 0: ~ |X_{t}| \leq Y_{T} \quad \implies \quad \mathbb{E}[X_{t}] \leq \mathbb{E}[Y_{T}] < \infty\]

因此 \((X_{t})_{t\geq 0}\) integrable。

将 \(X_{t\land\tau_{N}}\) 视作一个下标为 \(N\) 的序列,即:

\[\Big\{ X_{t\land \tau_{N}} \Big\}_{N\geq 0} = X_{t\land \tau_{1}}, ~ X_{t\land \tau_{2}}, ~ X_{t\land \tau_{3}}, ~ \ldots\]

注意到 \(X_{t\land \tau_{N}} = X_{\min(t, \tau_{N})} \longrightarrow X_{t}\) almost surely with \(N \longrightarrow \infty\),即:

\[\mbox{for } ~ \forall ~ t \geq 0: ~ \forall ~ \varepsilon > 0: ~ P\left( \lim\limits_{N \rightarrow \infty} \left| X_{t\land \tau_{N}} - X_{t} \right| > \varepsilon \right) = 0\]

这是因为 \(\lim \limits_{N \rightarrow \infty} \tau_{N} = \infty\),\(t \land \tau_{N} = \min(t, \tau_{N})\) 自然随 \(N\) 增大而收敛于 \(t\)。

所以对于 \(\forall ~ 0 \leq s \leq t\):

\[\begin{align*}\mathbb{E}[X_{t} ~ | ~ \mathcal{F}_{s}] & = \mathbb{E}\Big[\lim\limits_{N\rightarrow \infty}X_{t\land \tau_{N}} ~ | ~ \mathcal{F}_{s}\Big]\\& = \lim\limits_{N \rightarrow \infty} \mathbb{E}\Big[ X_{t\land\tau_{N}} ~ | ~ \mathcal{F}_{s}\Big] \quad (\mbox{Dominated Convergence Theorem})\\& = \lim\limits_{N \rightarrow \infty} X_{s \land \tau_{N}} \quad (\mathbf{*})\\& = X_{s}\end{align*}\]

因此:local martingale \((X_{t})_{t\geq 0}\) 在给定的条件下也为一个 true martingale。

  • 注意:

    以上带星号的那一步推导中,鞅 \(\Big(X_{t\land\tau_{N}}\Big)_{t\geq 0}\) 的下标依然是 \(t\),尽管现在复合为 \(t\land \tau_{N}\)。因此在这一步中我们只需将 \(t\) 替换为 \(s\) 即可。

Corollary.

假设 \(X\) 一个 离散时间 local martingale,使对于 \(\forall ~ t \geq 0: ~ \mathbb{E}[|X_{t}|] < \infty\),那么 \(X\) 是一个 true martingale。

证明:

令 \(Y_{t} = |X_{0}| + |X_{1}| + \cdots + |X_{t}|\)。Trivially:

\[Y_{t} = |X_{0}| + |X_{1}| + \cdots + |X_{t}| \geq |X_{s}| ~ \mbox{ for } ~ \forall s \in \left\{0, 1, \ldots, t \right\}\]

并且由于:\(\forall ~ t \geq 0: ~ \mathbb{E}[|X_{t}|] < \infty\),那么:

\[\begin{align*}\mathbb{E}[Y_{t}] & = \mathbb{E}\Big[ \left|X_{0}\right| + \left|X_{1}\right| + \cdots + \left|X_{t}\right| \Big]\\& = \sum\limits^{t}_{s=0}\mathbb{E}\big[ \left| X_{s} \right| \big] < \infty\end{align*}\]

所以 \((Y_{t})_{t\geq 0}\) 可积,并且此时 \((X_{t})_{t \leq 0}\) 和 \((Y_{t})_{t\geq 0}\) 恰满足上述 Sufficient Condition,因此 \((X_{t})_{t\geq 0}\) 为一个 true martingale。

Supermartingale and Submartingale (上鞅与下鞅)

上鞅(Supermartingale)

相关于 filtration \(\mathcal{\left\{ F_{t} \right\}}_{t\geq 0}\) 的一个 supermartingale(上鞅)是一个 adapted stochastic process \((U_{t})_{t\geq 0}\),满足以下性质:

  • (Integrability)

    \[\forall ~ t \geq 0: ~ \mathbb{E}\big[\left| U_{t} \right|\big] < \infty\]
  • (Decrease in average)

    \[\forall ~ 0 \leq s \leq t: ~ \mathbb{E}\big[U_{t} ~ | ~ \mathcal{F}_{s}\big] \leq U_{s}\]

下鞅(Submartingale)

相关于 filtration \(\mathcal{\left\{ F_{t} \right\}}_{t\geq 0}\) 的一个 submartingale(下鞅)是一个 adapted stochastic process \((V_{t})_{t\geq 0}\),满足以下性质:

  • (Integrability)

    \[\forall ~ t \geq 0: ~ \mathbb{E}\big[ | V_{t} | \big] < \infty\]
  • (Increase in average)

    \[\forall ~ 0 \leq s \leq t: ~ \mathbb{E}\big[V_{t} ~ | ~ \mathcal{F}_{s}\big] \geq V_{s}\]

鞅、上鞅、下鞅

A martingale is a stochastic process that is both a supermartingale and a submartingale.

Theorem.

假设 \(X\) 是一个连续或离散时间上的 local martingale。如果 \(X_{t} \geq 0\) 对于 \(\forall ~ t \geq 0\) 都成立,那么 \(X\) 是一个 supermartingale(上鞅)。

证明:

令 \((\tau_{N})_{N\geq 0}\) 为相关于 local martingale \((X_{t})_{t\geq 0}\) 的 localizing sequence,即:

\[\forall ~ N \geq 0: ~ \Big(X^{\tau_{N}}_{t} \Big)_{t\geq 0} ~ \mbox{ is a true martingale.}\]

首先证明 \((X_{t})_{t \geq 0 }\) 可积。由 Fatou"s Lemma

\[\begin{align*}\mathbb{E}\big[|X_{t}|\big] & = \mathbb{E}[X_{t}] \\& = \mathbb{E}\Big[\lim\limits_{N \rightarrow \infty} X_{t \land \tau_{N}}\Big] \\& = \mathbb{E}\Big[\liminf\limits_{N \rightarrow \infty} X_{t \land \tau_{N}}\Big] \\& \leq \liminf\limits_{N \rightarrow \infty} \mathbb{E}\Big[X_{t\land \tau_{N}}\Big] \\& = \liminf\limits_{N \rightarrow \infty} \mathbb{E}\Big[X_{t\land \tau_{N}} ~ \Big| ~ \mathcal{F}_{0} \Big] \\& = X_{0} < \infty\end{align*}\]

在条件期望上运用 Fatou"s Lemma,对于 \(\forall ~ 0 \leq s \leq t:\)

\[\begin{align*}\mathbb{E}\big[X_{t} ~ | ~ \mathcal{F}_{s}\big] & = \mathbb{E}\Big[ \lim\limits_{N \rightarrow \infty} X_{t\land \tau_{N}} ~ \Big| ~ \mathcal{F}_{s} \Big] \\& = \mathbb{E}\Big[ \liminf\limits_{N \rightarrow \infty} X_{t\land \tau_{N}} ~ \Big| ~ \mathcal{F}_{s} \Big] \\& \leq \liminf_{N \rightarrow \infty} \mathbb{E}\Big[ X_{t\land \tau_{N}} ~ \Big| ~ \mathcal{F}_{s} \Big] \\& = \liminf_{N \rightarrow \infty} X_{s \land \tau_{N}} \\& = X_{s}\end{align*}\]

因此 \((X_{t})_{t\geq 0}\) 为一个 supermartingale(上鞅)。

Corollary.

如果 \((X_{t})_{t\geq 0}\) 是一个离散时间 local martingale,且对于任意 $ t \geq 0$,有 \(X_{t} \geq 0\) almost surely,那么 \((X_{t})_{t\geq 0}\) 是一个 true martingale。

证明:

通过上述 Theorem,我们有:

\[\mathbb{E}\big[|X_{t}|\big] = \mathbb{E}[X_{t}] \leq X_{0} < \infty\]

由于 \(X\) 是可积的,通过上一条 Corollary可以得出 \((X_{t})_{t\geq 0}\) 是一个 martingale 的结论。

Theorem.

假设:

\[X_{t} = X_{0} + \sum\limits^{t}_{s=1} K_{s} (M_{s} - M_{s-1})\]

其中,\(K\) 是一个 previsible process,\(M\) 是一个 local martingale,\(X_{0}\) 是一个常数。

如果对于某些非随机的 \(T > 0\),有:\(X_{T} \geq 0\) almost surely,那么 \((X_{t})_{0\leq t \leq T}\) 是一个 true martingale。

证明:

略。(太长了,以后有机会补上。)

随机贴现因子(Stochastic Discount Factor / Pricing Kernel / State Price Density)

在一个没有股息的市场中,在时刻 \(s\) 和 \(t\) 间(\(0 \leq s < t\))的随机贴现因子是一个 adapted positive \(\mathcal{F}_{t}-\) measurable random variable \(\rho_{s,t}\), 使得:

\[P_{s} = \mathbb{E}\big[\rho_{s,t}P_{t} ~ | ~ \mathcal{F}_{s}\big]\]
  • 令 \(Y\) 为一个 martingale deflator(i.e. \(\forall 0 \leq s < t: ~ \mathbb{E}[Y_{t}P_{t} ~ | ~ \mathcal{F}_{s}] = Y_{s}P_{s}\)),令 \(\rho_{s,t} = \frac{Y_{t}}{Y_{s}}\),若 \(\rho_{s,t}P_{t}\) 可积,那么 \(\rho_{s,t}\) 为时间 \(s\) 与 \(t\) 间的 pricing kernel。

    • 证明:

      对于 positivity,由于 \(Y\) 为 martingale deflator,则 \(\forall t \geq 0: ~ Y_{t} > 0\),所以 \(\rho_{s,t} = \frac{Y_{t}}{Y_{s}} > 0\),并且:

      \[\begin{align*}\mathbb{E} \big[ \rho_{s,t} P_{t} ~ | ~ \mathcal{F}_{s} \big] & = \mathbb{E} \Big[ \frac{Y_{t}}{Y_{s}} P_{t} ~ | ~ \mathcal{F}_{s} \Big] \\& = \frac{1}{Y_{s}} \mathbb{E} \big[ Y_{t}P_{t} ~ | ~ \mathcal{F}_{s} \big] \\& = \frac{1}{Y_{s}} \cdot Y_{s} P_{s} \\& = P_{s}\end{align*}\]

      因此 \(\rho_{s,t}\) 为一个 pricing kernel。

  • 相反地,对于 \(s\geq 0\),假设 \(\rho_{s, s+1}\) 为 时间 \(s\) 与 \(s+1\) 间的 pricing kernel,令 \(Y_{t} = \rho_{0,1} \rho_{1,2} \ldots \rho_{t-1, t}\),且 \(YP\) 可积,那么 \(Y\) 为一个 martingale deflator。

    • 证明:

      对于 \(\forall t \geq 0\),由于 pricing kernel 为正随机变量,则 \(Y_{t} = \rho_{0,1} \rho_{1,2} \ldots \rho_{t-1, t} > 0\),并且:

      \[\begin{align*}\mathbb{E} \big[Y_{t+1}P_{t+1} ~ \big| ~ \mathcal{F}_{t} \big] & = \mathbb{E} \big[\rho_{0,1} \rho_{1,2} \ldots \rho_{t-1, t} \rho_{t, t+1} \cdot P_{t+1} ~ \big| ~ \mathcal{F}_{t} \big] \\& = \rho_{0,1} \rho_{1,2} \ldots \rho_{t-1, t} \cdot \mathbb{E} \big[\rho_{t, t+1} \cdot P_{t+1} ~ \big| ~ \mathcal{F}_{t} \big] \qquad \text{(adaptness)}\\& = Y_{t} \cdot P_{t} \qquad \text{(by definition)}\end{align*}\]

      因此,\((Y_{t})_{t\geq 0}\) 为一个 martingale deflator。

Proposition.

考虑存在一个 numeraire \(\eta\) 的市场,且令:\(N_{t} = \eta_{t} \cdot P_{t} \quad \forall t \geq 0\)。令 \(H\) 为一个 investment-consumption strategy,即,\(H\) 的 consumption stream 定义为:

\[\begin{align*}c_{0} & = x - H_{1} \cdot P_{0}\\c_{t} & = (H_{t} - H_{t+1}) \cdot P_{t}\end{align*}\]

其中 \(x\) 为初始财富。令:

\[K_{t} = H_{t} + \eta_{t} \sum\limits_{s=0}^{t-1} \frac{c_{s}}{N_{s}}\]

那么,\(K\) 为一个 pure-investment strategy from the same initial wealth \(x\)。

特殊地,当且仅当 \(K\) 为一个 terminal-consumption arbitrage 时,\(H\) 为一个 arbitrage。

证明:

\[\begin{align*}(K_{t} - K_{t+1}) \cdot P_{t} & = \Big( H_{t} + \eta_{t}\sum\limits_{s=0}^{t-1}\frac{c_{s}}{N_{s}} - H_{t+1} - \eta_{t+1}\sum\limits_{s=0}^{t}\frac{c_{s}}{N_{s}} \Big) \cdot P_{t} \\& = (H_{t} - H_{t+1}) \cdot P_{t} + \Big( \eta_{t}\sum\limits_{s=0}^{t-1}\frac{c_{s}}{N_{s}} - \eta_{t+1}\sum\limits_{s=0}^{t}\frac{c_{s}}{N_{s}} \Big) \cdot P_{t} \\& = (H_{t} - H_{t+1}) \cdot P_{t} + \Big( \eta_{t}\sum\limits_{s=0}^{t}\frac{c_{s}}{N_{s}} - \eta_{t+1}\sum\limits_{s=0}^{t}\frac{c_{s}}{N_{s}} - \eta_{t} \frac{c_{t}}{N_{t}} \Big) \cdot P_{t} \\& = (H_{t} - H_{t+1}) \cdot P_{t} + \Big( \big( \eta_{t} - \eta_{t+1} \big) \sum\limits_{s=0}^{t} \frac{c_{s}}{N_{s}} - \eta_{t} \frac{c_{t}}{N_{t}} \Big) \cdot P_{t} \\& = (H_{t} - H_{t+1}) \cdot P_{t} - \eta_{t} \cdot P_{t} \frac{c_{t}}{N_{t}} + \big( \eta_{t} - \eta_{t+1} \big) \cdot P_{t} \sum\limits_{s=0}^{t}\frac{c_{s}}{N_{s}} \\& = (H_{t} - H_{t+1}) \cdot P_{t} - \eta_{t} \cdot P_{t}\frac{c_{t}}{N_{t}} \qquad \text{(Investment-consumption strategy)} \\& = c_{t} \cdot P_{t} - N_{t} \cdot \frac{c_{t}}{N_{t}} \qquad \text{(By definition)} \\& = 0\end{align*}\]

因此,对于 \(\forall t \geq 0\),有:

\[(K_{t} - K_{t+1}) \cdot P_{t} = 0\]

由假设:\((\eta_{t})_{t\geq 0}\) 为 pure-investment strategy,则 \((K_{t})_{t\geq 0}\) 亦为 pure-investment strategy。

假设对于 non-random \(T\),有:\(c_{T} = H_{T}\cdot P_{T}\),那么:

\[\begin{align*}K_{T} \cdot P_{T} & = \Big( H_{T} + \eta_{T}\sum\limits_{s=0}^{T-1}\frac{c_{s}}{N_{s}} \Big) \cdot P_{T} \\& = H_{T} \cdot P_{T} + \eta_{T} \cdot P_{T} \sum\limits_{s=0}^{T-1}\frac{c_{s}}{N_{s}} \\& = c_{T} + N_{T} \sum\limits_{s=0}^{T-1}\frac{c_{s}}{N_{s}} \\& = N_{T} \frac{c_{T}}{N_{T}} + N_{T} \sum\limits_{s=0}^{T-1}\frac{c_{s}}{N_{s}} \\& = N_{T} \sum\limits_{s=0}^{T}\frac{c_{s}}{N_{s}} \\\end{align*}\]\[\implies K_{T} \cdot P_{T} = N_{T} \sum\limits_{s=0}^{T}\frac{c_{s}}{N_{s}}\]

则:当且仅当 某些 \(c_{t} ~ (0 \leq t \leq T)\) 取值为 strictly positive 时, 等式左侧 \(K_{T} \cdot P_{T}\) 为 strictly positive。

Lemma. (Bayes formula; from homework 5.)

令 \(\mathbb{P}\) 和 \(\mathbb{Q}\) 为定义在 \((\Omega, ~ \mathcal{F})\) 上的 equivalent probability measures,令 Radon - Nikodym derivative: \(Z = \frac{d\mathbb{Q}}{d\mathbb{P}}\),令 \(\mathcal{G} \subset \mathcal{F}\) 为一个 \(\sigma-\)field。那么:

\[\mathbb{E}^{\mathbb{Q}}\big[ X ~ \big| ~ \mathcal{G} \big] = \frac{\mathbb{E}^{\mathbb{P}}[ZX ~ | ~ \mathcal{G}]}{\mathbb{E}^{\mathbb{P}}[Z ~ | ~ \mathcal{G}]}\]

证明:

令 \(Y = \frac{\mathbb{E}^{\mathbb{P}}[ZX ~ | ~ \mathcal{G}]}{\mathbb{E}^{\mathbb{P}}[Z ~ | ~ \mathcal{G}]}\),欲证:\(\mathbb{E}^{\mathbb{Q}}\big[ X ~ \big| ~ \mathcal{G} \big] = Y\),这等价于:

对于 \(\forall G \in \mathcal{G}\):

\[\begin{align*}& \mathbb{E}^{\mathbb{Q}}\big[ X ~ \big| ~ \mathcal{G} \big] \cdot \mathbb{I}_{G} = Y \cdot \mathbb{I}_{G} \\\iff \quad & \mathbb{E}^{\mathbb{Q}}\Big[ \mathbb{E}^{\mathbb{Q}}\big[ X ~ \big| ~ \mathcal{G} \big] \cdot \mathbb{I}_{G} \Big] = \mathbb{E}^{\mathbb{Q}} \Big[ Y \cdot \mathbb{I}_{G} \Big] \\\iff \quad & \mathbb{E}^{\mathbb{Q}}\Big[ \mathbb{E}^{\mathbb{Q}}\big[ X \cdot \mathbb{I}_{G} ~ \big| ~ \mathcal{G} \big] \Big] = \mathbb{E}^{\mathbb{Q}} \Big[ Y \cdot \mathbb{I}_{G} \Big] \\\iff \quad & \mathbb{E}^{\mathbb{Q}} \big[ X \cdot \mathbb{I}_{G} \big] = \mathbb{E}^{\mathbb{Q}} \Big[ Y \cdot \mathbb{I}_{G} \Big] \\\iff \quad & \int_{G} ~ X ~ d\mathbb{Q} = \int_{G} ~ Y ~ d\mathbb{Q}\end{align*}\]

由 Radon-Nikodym derivative \(Z = \frac{d\mathbb{Q}}{d\mathbb{P}} \implies d\mathbb{Q} = Z \cdot d\mathbb{P}\):

\[\begin{align*}& \int_{G} ~ X ~ d\mathbb{Q} = \int_{G} ~ Y ~ d\mathbb{Q} \\\iff \quad & \int_{G} ~ X Z ~ d\mathbb{P} = \int_{G} ~ YZ ~ d\mathbb{P} \\\iff \quad & \mathbb{E}^{\mathbb{P}}\big[ XZ \cdot \mathbb{I}_{G} \big] = \mathbb{E}^{\mathbb{P}}\big[ YZ \cdot \mathbb{I}_{G} \big]\end{align*}\]

因此,目标等价于证明:对于 \(\forall G \in \mathcal{G}\),有:

\[\mathbb{E}^{\mathbb{P}}\big[ XZ \cdot \mathbb{I}_{G} \big] = \mathbb{E}^{\mathbb{P}}\big[ YZ \cdot \mathbb{I}_{G} \big]\]

注意到 \(Y = \mathbb{E}^{\mathbb{Q}}\big[ X ~ \big| ~ \mathcal{G} \big]\) 为 \(\mathcal{G}-\)measurable,那么RHS:

\[\begin{align*}\mathbb{E}^{\mathbb{P}}\big[ YZ \cdot \mathbb{I}_{G} \big] & = \mathbb{E}^{\mathbb{P}} \Big[ \mathbb{E}^{\mathbb{P}}\big[ YZ \cdot \mathbb{I}_{G} ~ \big| ~ \mathcal{G} \big] \Big] \qquad \text{(Tower property)} \\& = \mathbb{E}^{\mathbb{P}} \Big[ \mathbb{I}_{G}Y \cdot \mathbb{E}^{\mathbb{P}}\big[ Z ~ \big| ~ \mathcal{G} \big] \Big] \qquad \text{($\mathbb{I}_{G}Y$ is $\mathcal{G}-$measurable)} \\& = \mathbb{E}^{\mathbb{P}} \Big[ \mathbb{I}_{G} \cdot \frac{\mathbb{E}^{\mathbb{P}}[ZX ~ | ~ \mathcal{G}]}{\mathbb{E}^{\mathbb{P}}[Z ~ | ~ \mathcal{G}]} \cdot \mathbb{E}^{\mathbb{P}}\big[ Z ~ \big| ~ \mathcal{G} \big] \Big] \\& = \mathbb{E}^{\mathbb{P}} \Big[ \mathbb{I}_{G} \cdot \mathbb{E}^{\mathbb{P}} \big[ZX ~ \big| ~ \mathcal{G} \big] \Big] \\& = \mathbb{E}^{\mathbb{P}} \Big[ \mathbb{E}^{\mathbb{P}} \big[ZX \cdot \mathbb{I}_{G} ~ \big| ~ \mathcal{G} \big] \Big] \qquad \text{($\mathbb{I}_{G}$ is $\mathcal{G}-$measurable)} \\& = \mathbb{E}^{\mathbb{P}} \big[ ZX \cdot \mathbb{I}_{G} \big] \qquad \text{(Tower property)}\end{align*}\]

证毕。

关键词: 随机变量 贴现因子 充分条件