public:courses:finance:computational_finance:probability_review

# Week2 - Probability Review

• A random variable (rv) X can take value on a sample space $S_X$.
• It is distributed following a probability distribution function (pdf).
• Can only take a finite number of values.
• $\forall x \in S_X : 0 \le p(x) \le 1$
• $\forall x \notin S_X: p(x) = 0$
• $\sum\limits_{x \in S_X} p(x) = 1$

#### Bernoulli distribution

• We note X=1 on success and X=0 on failure.
• $Pr(X=1)=\pi$ and $Pr(X=0)=1-\pi$
• Then we have the pdf: $p(x)=Pr(X=x)=\pi^x(1-\pi)^x$ for $x \in {0,1}$
• In that case we have a probability curve $f(x)$
• And we can measure probability on intervals A: $Pr(X \in A) = \int_A f(x) dx$
• $\forall x: f(x) \ge 0$ and $\int_{-\infty}^\infty f(x) dx = 1$

#### Uniform distribution over [a,b]

• assuming b>a here.
• We note: $X \sim U[a,b]$ and we have the pdf: $f(x) = \begin{cases} \frac{1}{b-a} & \text{for } a \le x \le b \\ 0 & \text{otherwise}\end{cases}$
• The CDF function F for a rv X is: $F(x) = Pr(X \le x)$
• $x_1 \lt x_2 \Rightarrow F(x_1) \le F(x_2)$
• $F(-\infty) = 0$ and $F(\infty) = 1$
• $Pr(X \ge x) = 1 - F(x)$
• $Pr(x_1 \le x \le x_2) = F(x_2) - F(x_1)$
• $\frac{d}{dx} F(x) = f(x)$ if X is a continuous rv.
• Also note that for a continuous rv: $Pr(X\le x) = Pr(X\lt x)$ and $Pr(X=x)=0$
• Given an X rv with continuous CDF $F_X(x) = Pr(X \lt x)$: The $\alpha$* 100% quantile of $F_X$ for $\alpha \in [0,1]$ is the value $q_\alpha$ such that $F_X(q_\alpha) = Pr(X \lt q_\alpha) = \alpha$.
• The area to the left of $q_\alpha$ is $\alpha$ under the probability curve.
• If the inverse CDF function exists, then: $q_\alpha = F_X^{-1}(\alpha)$
• The 50% quantile is also called the median
• For a dist U[0,1] for instance we have $F(x)=x \Rightarrow q_\alpha=\alpha$
• If X is a rv such as $X \sim N(0,1)$, then: $f(x) = \phi(x) = \frac{1}{\sqrt{2\pi}} exp\left( - \frac12 x^2 \right)$ for $-\infty \le x \le \infty$.

$\Phi(x) = Pr(X \le x) = \int_{-\infty}^x \phi(z)dz$

• We have the important ranges:

$Pr(-1 \le x \le 1) \approx 0.67$ $Pr(-2 \le x \le 2) \approx 0.95$ $Pr(-3 \le x \le 3) \approx 0.99$

• In Excel:
• we can use the function NORMSDIST to get the $\Phi(z)$ or the $\phi(z)$ values.
• we can use the function NORMSINV to get the $\Phi^{-1}(\alpha)$ value.
• In R:
• We use pnorm to compute $\Phi(z)$
• We use qnorm to compute $\Phi^{-1}(z)$
• We use dnorm to compute $\phi(z)$
• Other noticeable relations on the std distribution:

$Pr(X\le z) = 1 - Pr(X \ge z)$ $Pr(X\ge z) = Pr(X \le -z)$ $Pr(X\ge 0) = Pr(X \le 0) = 0.5$

• Expected Value or Mean: Center of mass
• Kurtosis: Tail thickness
• For discrete rv: $E[X] = \mu_X = \sum\limits_{x \in S_X} x \cdot p(x)$
• For continuous rv: $E[X] = \mu_X = \int_{-\infty}^\infty x \cdot f(x) dx$
• If $X \sim N(0,1)$ then $\mu_X = \int_{-\infty}^\infty x \cdot \frac{1}{\sqrt{2\pi}} e^{-\frac 12 x^2} dx = 0$
• Let g(X) be some function of the rv X. Then
• For discrete rv: $E[g(X)] = \sum\limits_{x \in S_X} g(x) \cdot p(x)$
• For continuous rv: $E[g(X)] = \int_{-\infty}^\infty g(x) \cdot f(x) dx$
• $g(X) = (X - E[X])^2 = (X - \mu_X)^2$
• $Var(x) = \sigma_X^2 = E[g(X)] = E[(X-\mu_X)^2] = E[X^2] - \mu_X^2$
• $SD(X) = \sigma_X = \sqrt{Var(X)}$
• Note that Var(X) is in squared units of X, whereas SD(X) is in the same unit as X.
• Concretely:
• For discrete rv: $\sigma_X^2 = \sum\limits_{x \in S_X} (x - \mu_X)^2 \cdot p(x)$
• For continuous rv: $\sigma_X^2 = \int_{-\infty}^\infty (x - \mu_X)^2 \cdot f(x) dx$
• If $X \sim N(\mu_X,\sigma_X^2)$, then:

$f(x) = \frac{1}{\sqrt{2\pi \sigma_X^2}} exp\left( - \frac 12 \left(\frac{x-\mu_X}{\sigma_X} \right)^2\right)$

• Note that we still have 67% of probability in the range $[\mu_X - \sigma_X, \mu_X + \sigma_X]$.
• For this general normal distribution, we also have the relation with the standard normal distribution quantile function: $q_\alpha = \mu_X + \sigma_X \cdot \Phi^{-1}(\alpha) = \mu_X + \sigma_X \cdot z_\alpha$
• In Excel:
• NORMDIST(x,mu_X,sigma_X,cummulative): if commulative==true, computes $Pr(X \le x)$, otherwise compute $f(x) = \frac{1}{\sqrt{2\pi \sigma_X^2}} exp\left( - \frac 12 \left(\frac{x-\mu_X}{\sigma_X} \right)^2\right)$
• NORMINV(alpha, mu, sigma) computes $q_\alpha = \mu_X + \sigma_X \cdot z_\alpha$
• In R:
• simulate data: rnorm(n,mean,sd)
• compute CDF: pnorm(q, mean, sd)
• compute quantiles: qnorm(p,mean,sd)
• compute density: dnorm(x,mean, sd)
• Typically for return rate computation, if we consider: $R_A \sim N(\mu_A,\sigma_A^2)$ and $R_B \sim N(\mu_B,\sigma_B^2)$, then typically, if $\mu_A > \mu_B$, then we will also find that $\sigma_A > \sigma_B$.
• If we model a return $R_t \sim N(0.05,(0.50)^2)$. Then even if we know that $R_t \ge -1$, we will compute that: $Pr(R_t < -1) = 0.018$ (which is wrong!).
• normal distribution is more appropriate for cc returns:
• $r_t = ln(1+R_t)$
• $r_t$ can take on values less than -1.
• $X \sim N(\mu_X,\sigma_X^2), -\infty \lt X \lt \infty$
• Then we can define $Y = exp(X) \sim lognormal(\mu_X,\sigma_X^2), 0 \lt Y \lt \infty$
• $E[Y] = \mu_Y = exp(\mu_X + \frac{\sigma_X^2}{2})$
• $Var[Y] = \sigma_Y^2 = exp(2\mu_X + \sigma_X^2)(exp(\sigma_X^2)-1)$
• positive skew is when we have a long “right tail”, eg. the main “blob” is on the left.
• in R we have : rlnorm, plnorm, qlnorm and dlnorm.
• $g(X) = ((X - \mu_X)/\sigma_X)^3$
• $Skew(X) = E\left[ \left(\frac{X - \mu_X}{\sigma_X} \right)^3 \right]$
• Skew(X)>0 is when we have a long “right tail”, eg. the main “blob” is on the left.
• Skew(X)<0 is when we have a long “left tail”, eg. the main “blob” is on the right.
• For symmetry distributions Skew(X)=0
• For log normal distribution: $Y \sim lognormal(\mu_X,\sigma_X^2)$ we have:

$Skew(Y) = (exp(\sigma_X^2) +2) \sqrt{exp(\sigma_X^2) -1} \gt 0$

• $g(X) = ((X-\mu_X)/\sigma_X)^4$
• $Kurt(X) = E\left[ \left( \frac{X-\mu_X}{\sigma_X}\right)^4 \right]$
• For a general normal distribution $X \sim N(\mu_X,\sigma_X^2)$ we get $Kurt(X)=3$
• We then define the Excess kurtosis = Kurt(X) - 3.
• If Excess kurtosis(X) > 0 ⇒ X has fatter tails than normal distribution
• If Excess kurtosis(X) < 0 ⇒ X has thinner tails than normal distribution
• Similar to normal distribution but with fatter tails (eg. larger kurtosis).
• It has an additional parameter called the **degree of freedom“ “v”.
• We note $X \sim t_v$, and the pdf is:

$f(x) = \frac{\Gamma(\frac{v+1}{2})}{\sqrt{2\pi}\Gamma(\frac v2)} \left( 1 + \frac{x^2}{v}\right)^{- \frac{v+1}{2}}, ~~ -\infty \lt x \lt \infty, ~~ v > 0$

• With $\Gamma(z) = \int_0^\infty t^{z-1}e^{-t}dt$ denoting the gamma function.
• When $v \rightarrow \infty$ then the Student-t distribution is exactly the normal distribution.
• The smaller the degree of freedom parameter, the fatter are the tails of the distribution.
• Properties of this distribution are:
• $E[X] = 0, ~~ v>1$
• $Var(X) = \frac{v}{v-2}, ~~ v > 2$
• $Skew(X) = 0, ~~ v > 3$
• $excess kurt(X) = \frac{6}{v-4} - 3, ~~ v > 4$
• in R we have the functions: rt, pt, qt and dt related to this distribution.
• In practice if v=60 then we can already consider that we have the normal distribution.
• Let X be a discrete or continuous rc with $\mu_X = E[X]$ and $\sigma_X^2 = Var(X)$
• We define a new rv Y, such as: $Y = g(X) = a \cdot X + b$
• Then we have: $\mu_Y = a \cdot \mu_X + b$ and $\sigma_Y = a \cdot \sigma_X$
• Let $X \sim N(\mu_X,\sigma_X^2)$ and define $Y = a \cdot X + b$. Then: $Y \sim N(\mu_Y,\sigma_Y^2)$ with:

$\mu_Y = a \cdot \mu_X + b$ $\sigma_Y^2 = a^2 \cdot \sigma_X^2$

• Let $X \sim N(\mu_X,\sigma_X^2)$. The standardized rv Z is created using:

\begin{align} Z & = \frac{X - \mu_X}{\sigma_X} = \frac{1}{\sigma_X} \cdot X - \frac{\mu_X}{\sigma_X} \\ & = a \cdot X + b \\ a & = \frac{1}{\sigma_X}, ~ b = -\frac{\mu_X}{\sigma_X} \end{align}

• Thus we get: $Z \sim N(0,1)$.
• Eg. compute how much money we could loose with a specified probability $\alpha$.
• Assume R = simple monthly return. $R \sim N(0.05, (0.10)^2)$
• $\alpha$ is usually 5% or 1%.
• End of month wealth $W_1 = 10000 \cdot (1+R)$
• What is $Pr(W_1 \lt 9000$
• What value of R produces $W_1 = 9000$
• In general, the $\alpha \times 100%$ Value-at-Risk $(VaR_\alpha)$ for an initial investment of $W_0$ is computed as: $VaR_\alpha = W_0 \times q_\alpha$ where $q_\alpha$ is the quantile of the simple return distribution.
• Note that the Var is often reported as a positive number instead of a negative value.
• r =ln(1+R)
• We assume $r \sim N(\mu_r,\sigma_r^2)$
• We then:
• Compute the alpha quantile of the normal dist for r: $q_\alpha^r = \mu_r + \sigma_r z_\alpha$
• Convert the alpha quantile for r into an alpha quantile for R: $q_\alpha^R = e^{q_\alpha^r} - 1$
• We compute the $VaR_\alpha$ using $q_\alpha^R$: $VaR_\alpha = W_0 \cdot q_\alpha^R$
• public/courses/finance/computational_finance/probability_review.txt