Quantitative Methods in Retail Finance Note 4

This notes follows materials from the slides for M3S17 at Imperial College London Mathematics Department by Dr. Tony Bellotti. Questions mentioned are referring to past exam papers of M3S17 at Imperial College London.

#Chapter 3: Markov Transition Model

Markov Transition Models

Why Markov Chain Model?

This model allow us to track model changes in the state of an account over time.
e.g. Tracking credit card use.

First-order Markov Transition Model

  • Let $(X_1, X_2, …, X_n)$ be a sequence of discrete random variables that take value from ${1,2,…,K}$ with K fixed.
  • first-order finite value Markov chain
    The sequence is said to be a first-order finite value Markov chain if
    P(X_{n+1}=j|X_0=x_0,X_1=x_1,X_2=x_2,…,X_{n-1}=x_{n-1},X_n = i) = P(X_{n+1}=j|X_n = i)
    for all $n, x_0,x_1, …, x_{n-1}$ and $i,j$ such that $1\leq i,j \leq K$.
  • transition probability
    Transition probability $p_n$ is defined as
  • transition matrix
    Transition matrix $P$ is defined as a $K \times K$ matrix such that
    P_n[i,j] = p_{n}(i,j)
  • structural zeroes
    If it is certain that no transition will be made from i to j in the $n$th period, then set $p_n(i,j)=0$, this is called a structural zero.

###Propagation of Markov Chain
If we know the transition matrix from period $n-r$ to $n$, i.e. $P_r,P_{r+1},…,P_{n-1}$ are known, then we also be able to know the state in the $n$th period:
Prob(X_{n}=j|X_{n-r}=i) = P_{n-r+1}P_{n-r+2}…P_{n}[i,j]
for any $1\leq r \leq n$

Forcasting from state 0:
Prob(X_{n}=j|X_{0}=i) = P_{1}P_{2}…P_{n}[i,j]
let $\pi_{i}$ be the marginal distribution for $X_n$:
$$\pi_n = (P(X_n = 1),P(X_n = 2),…,P(X_n = K))^T$$

$$\pi_n = \pi_0(P_1 P_2 … P_n)$$

Stationary Markov Chain

  • stationary
    A Markov chain is stationary if $p_n(i,j)=p(i,j)$ for all $n,\ and\ i,j\ where 1\leq i, j \leq K$, for some transition probability $p$.
  • stationary distribution
    A stationary distribution for transition matrix is a distribution $\pi^\star = \pi^\star P$.

    In practice, most Markov chains converge (with 𝑛) to a stationary
    (Markov chains which have a periodicity in state change do not necessarily converge, but we do not cover this material in this course).


The aim this section, given some data, is to estimate $\mathbf{\theta}=(\theta_{ij})^{K}{i,j=1}$ where $\theta{ij}=p(i,j)$.
Maximum likelihood estimator for $\mathbf{\theta}$:
\hat\theta_{ij}=\hat p(i,j)=\frac{n_{ij}}{\sum_{l=1}^{n}n_{il}}
where $n_{ij}=|{t:x_{t-1}=i,x_{t}=j,\ for \ t\in {1,2,…,n}}|$, i.e. total number of observations that move from state $i$ to state $j$ during period 1 to n.

(c) Derive the maximum likelihood estimator for the first order finite-valued Markov transition model, assuming stationarity.

  1. likelihood function

$=\prod_{t=1}^{n} P(X_{t}=x_t|X_{t-1}=x_{t-1},X_{t-2}=x_{t-2},…,X_{1}=x_{1})P(X_0=x_0)$
$=\prod_{t=1}^{n} P(X_{t}=x_t|X_{t-1}=x_{t-1})P(X_0=x_0)(Chain rule)$
$=P(X_0=x_0)\prod_{t=1}^{n} P(X_{t}=x_t|X_{t-1}=x_{t-1})$
where\ n_{ij}=|{t:x_t=x_t,X_{t-1}=x_{t-1},\ for\ t\in {1,…,n}}|

  1. log likelihood function
  2. differentiate and find MLE
  3. adjust to constraints

Testing First-order Assumptions

In order to test if first order markov chain is a suitable assumption in each occasion,
we use chi-square hypothesis test.

second order Markov chain

A sequence (x_1,..,x_n) is a second order Markov chain if

The transition probability
$p_n(k,i,j)=P(X_n=i, X_{n-1}=k)$

The MLE for a second-Markov chain can similarly be proved as:
$\hat p(k,i,j)=\hat\theta_{kij}=\frac{n_kij}{mki}$
where $n_{kij}=|{t:x_{t-2}=k,x_{t-1}=i,x_{t}=j\ for\ t \in {2,…,n}}|,
m_{ki}=|{t:x_{t-2}=k,x_{t-1}=i\ for\ t \in {2,…,n}}|=\sum_{l=i}^{n}n_{kil}$

  1. $H_{0}$
    Null hypothesis:
    where $P(k,i,j)$ is second-order Markov transition probability from state k to i then to j.

  2. $n_k$
    $n_k=|{t:x_t=k\ for\ t \in {1,..,n}}|$
    i.e. The observed number of times for each states

  3. $O_{kj}$
    The observed number of times state k follwed by i and then j
    $O_{kj}=n{kij}=m_{ki}\hat p(i,j)$

  4. $E_{kj}$
    The expected number of times state $k$ is followed by state $i$ and then $j$, given null hypothesis is:
    $E_{kj}=n_{k}\hat p(k,i)\hat p(i,j)\approx m_{ki}\hat p(i,j)$
    becuase $\hat p(k,i)=\frac{n_{ki}}{\sum_{l=1}^{K}n_{kl}}\approx \frac{m_{ki}}{n_k}$

  5. Pearson Chi_square test
    \chi^2 = \sum_{k\in S_1}\sum_{k\in S_2}\frac{(O_{kj}-E{kj})^2}{E_{kj}}

  • S1 is the set of states which do not have a structural zero moving to state $i$
  • S2 is the set of states which do not have a structural zero moving from state $i$
  1. Degree of freedom
    $df = (K-1-z_{1})(K-1-z_{2})$
  • $z_1$: the number of structural zeroes in the transition from $k$ to $i$.
  • $z_2$: the number of structural zeroes in the transition from $i$ to $j$.

Roll-rate Model

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