Skip to article frontmatterSkip to article content
Contents
and

11. Present Values

11.1Overview

This lecture describes the present value model that is a starting point of much asset pricing theory.

Asset pricing theory is a component of theories about many economic decisions including

  • consumption
  • labor supply
  • education choice
  • demand for money

In asset pricing theory, and in economic dynamics more generally, a basic topic is the relationship among different time series.

A time series is a sequence indexed by time.

In this lecture, we’ll represent a sequence as a vector.

So our analysis will typically boil down to studying relationships among vectors.

Our main tools in this lecture will be

  • matrix multiplication, and
  • matrix inversion.

We’ll use the calculations described here in subsequent lectures, including consumption smoothing, equalizing difference model, and monetarist theory of price levels.

Let’s dive in.

11.2Analysis

Let

  • {dt}t=0T\{d_t\}_{t=0}^T be a sequence of dividends or “payouts”
  • {pt}t=0T\{p_t\}_{t=0}^T be a sequence of prices of a claim on the continuation of the asset’s payout stream from date tt on, namely, {ds}s=tT\{d_s\}_{s=t}^T
  • δ(0,1) \delta \in (0,1) be a one-period “discount factor”
  • pT+1p_{T+1}^* be a terminal price of the asset at time T+1T+1

We assume that the dividend stream {dt}t=0T\{d_t\}_{t=0}^T and the terminal price pT+1p_{T+1}^* are both exogenous.

This means that they are determined outside the model.

Assume the sequence of asset pricing equations

pt=dt+δpt+1,t=0,1,,Tp_t = d_t + \delta p_{t+1}, \quad t = 0, 1, \ldots , T

We say equations, plural, because there are T+1T+1 equations, one for each t=0,1,,Tt =0, 1, \ldots, T.

Equations (1) assert that price paid to purchase the asset at time tt equals the payout dtd_t plus the price at time t+1t+1 multiplied by a time discount factor δ.

Discounting tomorrow’s price by multiplying it by δ accounts for the “value of waiting one period”.

We want to solve the system of T+1T+1 equations (1) for the asset price sequence {pt}t=0T\{p_t\}_{t=0}^T as a function of the dividend sequence {dt}t=0T\{d_t\}_{t=0}^T and the exogenous terminal price pT+1p_{T+1}^*.

A system of equations like (1) is an example of a linear difference equation.

There are powerful mathematical methods available for solving such systems and they are well worth studying in their own right, being the foundation for the analysis of many interesting economic models.

For an example, see Samuelson multiplier-accelerator

In this lecture, we’ll solve system (1) using matrix multiplication and matrix inversion, basic tools from linear algebra introduced in linear equations and matrix algebra.

We will use the following imports

import numpy as np
import matplotlib.pyplot as plt

11.3Representing sequences as vectors

The equations in system (1) can be arranged as follows:

p0=d0+δp1p1=d1+δp2pT1=dT1+δpTpT=dT+δpT+1\begin{aligned} p_0 & = d_0 + \delta p_1 \\ p_1 & = d_1 + \delta p_2 \\ \vdots \\ p_{T-1} & = d_{T-1} + \delta p_T \\ p_T & = d_T + \delta p^*_{T+1} \end{aligned}

Write the system (2) of T+1T+1 asset pricing equations as the single matrix equation

[1δ000001δ000001δ000000001δ000001][p0p1p2pT1pT]=[d0d1d2dT1dT]+[0000δpT+1]\begin{bmatrix} 1 & -\delta & 0 & 0 & \cdots & 0 & 0 \cr 0 & 1 & -\delta & 0 & \cdots & 0 & 0 \cr 0 & 0 & 1 & -\delta & \cdots & 0 & 0 \cr \vdots & \vdots & \vdots & \vdots & \vdots & 0 & 0 \cr 0 & 0 & 0 & 0 & \cdots & 1 & -\delta \cr 0 & 0 & 0 & 0 & \cdots & 0 & 1 \end{bmatrix} \begin{bmatrix} p_0 \cr p_1 \cr p_2 \cr \vdots \cr p_{T-1} \cr p_T \end{bmatrix} = \begin{bmatrix} d_0 \cr d_1 \cr d_2 \cr \vdots \cr d_{T-1} \cr d_T \end{bmatrix} + \begin{bmatrix} 0 \cr 0 \cr 0 \cr \vdots \cr 0 \cr \delta p_{T+1}^* \end{bmatrix}

In vector-matrix notation, we can write system (3) as

Ap=d+bA p = d + b

Here AA is the matrix on the left side of equation (3), while

p=[p0p1pT],d=[d0d1dT],andb=[00δpT+1]p = \begin{bmatrix} p_0 \\ p_1 \\ \vdots \\ p_T \end{bmatrix}, \quad d = \begin{bmatrix} d_0 \\ d_1 \\ \vdots \\ d_T \end{bmatrix}, \quad \text{and} \quad b = \begin{bmatrix} 0 \\ 0 \\ \vdots \\ \delta p^*_{T+1} \end{bmatrix}

The solution for the vector of prices is

p=A1(d+b)p = A^{-1}(d + b)

For example, suppose that the dividend stream is

dt+1=1.05dt,t=0,1,,T1.d_{t+1} = 1.05 d_t, \quad t = 0, 1, \ldots , T-1.

Let’s write Python code to compute and plot the dividend stream.

T = 6
current_d = 1.0
d = []
for t in range(T+1):
    d.append(current_d)
    current_d = current_d * 1.05 

fig, ax = plt.subplots()
ax.plot(d, 'o', label='dividends')
ax.legend()
ax.set_xlabel('time')
plt.show()
<Figure size 640x480 with 1 Axes>

Now let’s compute and plot the asset price.

We set δ and pT+1p_{T+1}^* to

δ = 0.99
p_star = 10.0

Let’s build the matrix AA

A = np.zeros((T+1, T+1))
for i in range(T+1):
    for j in range(T+1):
        if i == j:
            A[i, j] = 1
            if j < T:
                A[i, j+1] = -δ

Let’s inspect AA

A
array([[ 1. , -0.99, 0. , 0. , 0. , 0. , 0. ], [ 0. , 1. , -0.99, 0. , 0. , 0. , 0. ], [ 0. , 0. , 1. , -0.99, 0. , 0. , 0. ], [ 0. , 0. , 0. , 1. , -0.99, 0. , 0. ], [ 0. , 0. , 0. , 0. , 1. , -0.99, 0. ], [ 0. , 0. , 0. , 0. , 0. , 1. , -0.99], [ 0. , 0. , 0. , 0. , 0. , 0. , 1. ]])

Now let’s solve for prices using (6).

b = np.zeros(T+1)
b[-1] = δ * p_star
p = np.linalg.solve(A, d + b)
fig, ax = plt.subplots()
ax.plot(p, 'o', label='asset price')
ax.legend()
ax.set_xlabel('time')
plt.show()
<Figure size 640x480 with 1 Axes>

Now let’s consider a cyclically growing dividend sequence:

dt+1=1.01dt+0.1sint,t=0,1,,T1.d_{t+1} = 1.01 d_t + 0.1 \sin t, \quad t = 0, 1, \ldots , T-1.
T = 100
current_d = 1.0
d = []
for t in range(T+1):
    d.append(current_d)
    current_d = current_d * 1.01 + 0.1 * np.sin(t)

fig, ax = plt.subplots()
ax.plot(d, 'o-', ms=4, alpha=0.8, label='dividends')
ax.legend()
ax.set_xlabel('time')
plt.show()
<Figure size 640x480 with 1 Axes>
Solution to Exercise 2

We proceed as above after modifying parameters and consequently the matrix AA.

δ = 0.98
p_star = 0.0
A = np.zeros((T+1, T+1))
for i in range(T+1):
    for j in range(T+1):
        if i == j:
            A[i, j] = 1
            if j < T:
                A[i, j+1] = -δ

b = np.zeros(T+1)
b[-1] = δ * p_star
p = np.linalg.solve(A, d + b)
fig, ax = plt.subplots()
ax.plot(p, 'o-', ms=4, alpha=0.8, label='asset price')
ax.legend()
ax.set_xlabel('time')
plt.show()
<Figure size 640x480 with 1 Axes>

The weighted averaging associated with the present value calculation largely eliminates the cycles.

11.4Analytical expressions

By the inverse matrix theorem, a matrix BB is the inverse of AA whenever ABA B is the identity.

It can be verified that the inverse of the matrix AA in (3) is

A1=[1δδ2δT1δT01δδT2δT10001δ00001]A^{-1} = \begin{bmatrix} 1 & \delta & \delta^2 & \cdots & \delta^{T-1} & \delta^T \cr 0 & 1 & \delta & \cdots & \delta^{T-2} & \delta^{T-1} \cr \vdots & \vdots & \vdots & \cdots & \vdots & \vdots \cr 0 & 0 & 0 & \cdots & 1 & \delta \cr 0 & 0 & 0 & \cdots & 0 & 1 \cr \end{bmatrix}

If we use the expression (9) in (6) and perform the indicated matrix multiplication, we shall find that

pt=s=tTδstds+δT+1tpT+1p_t = \sum_{s=t}^T \delta^{s-t} d_s + \delta^{T+1-t} p_{T+1}^*

Pricing formula (10) asserts that two components sum to the asset price ptp_t:

  • a fundamental component s=tTδstds\sum_{s=t}^T \delta^{s-t} d_s that equals the discounted present value of prospective dividends

  • a bubble component δT+1tpT+1\delta^{T+1-t} p_{T+1}^*

The fundamental component is pinned down by the discount factor δ and the payout of the asset (in this case, dividends).

The bubble component is the part of the price that is not pinned down by fundamentals.

It is sometimes convenient to rewrite the bubble component as

cδtc \delta^{-t}

where

cδT+1pT+1c \equiv \delta^{T+1}p_{T+1}^*

11.5More about bubbles

For a few moments, let’s focus on the special case of an asset that never pays dividends, in which case

[d0d1d2dT1dT]=[00000]\begin{bmatrix} d_0 \cr d_1 \cr d_2 \cr \vdots \cr d_{T-1} \cr d_T \end{bmatrix} = \begin{bmatrix} 0 \cr 0 \cr 0 \cr \vdots \cr 0 \cr 0 \end{bmatrix}

In this case system (1) of our T+1T+1 asset pricing equations takes the form of the single matrix equation

[1δ000001δ000001δ000000001δ000001][p0p1p2pT1pT]=[0000δpT+1]\begin{bmatrix} 1 & -\delta & 0 & 0 & \cdots & 0 & 0 \cr 0 & 1 & -\delta & 0 & \cdots & 0 & 0 \cr 0 & 0 & 1 & -\delta & \cdots & 0 & 0 \cr \vdots & \vdots & \vdots & \vdots & \vdots & 0 & 0 \cr 0 & 0 & 0 & 0 & \cdots & 1 & -\delta \cr 0 & 0 & 0 & 0 & \cdots & 0 & 1 \end{bmatrix} \begin{bmatrix} p_0 \cr p_1 \cr p_2 \cr \vdots \cr p_{T-1} \cr p_T \end{bmatrix} = \begin{bmatrix} 0 \cr 0 \cr 0 \cr \vdots \cr 0 \cr \delta p_{T+1}^* \end{bmatrix}

Evidently, if pT+1=0p_{T+1}^* = 0, a price vector pp of all entries zero solves this equation and the only the fundamental component of our pricing formula (10) is present.

But let’s activate the bubble component by setting

pT+1=cδ(T+1)p_{T+1}^* = c \delta^{-(T+1)}

for some positive constant cc.

In this case, when we multiply both sides of (14) by the matrix A1A^{-1} presented in equation (9), we find that

pt=cδtp_t = c \delta^{-t}

11.6Gross rate of return

Define the gross rate of return on holding the asset from period tt to period t+1t+1 as

Rt=pt+1ptR_t = \frac{p_{t+1}}{p_t}

Substituting equation (16) into equation (17) confirms that an asset whose sole source of value is a bubble earns a gross rate of return

Rt=δ1>1,t=0,1,,TR_t = \delta^{-1} > 1 , t = 0, 1, \ldots, T

11.7Exercises

Solution to Exercise 4

Plugging each of the above pT+1,dtp_{T+1}^*, d_t pairs into Equation (10) yields:

  1. pt=s=tTδstgsd0=dt1(δg)T+1t1δg p_t = \sum^T_{s=t} \delta^{s-t} g^s d_0 = d_t \frac{1 - (\delta g)^{T+1-t}}{1 - \delta g}

  2. pt=s=tTδstgsd0+δT+1tgT+1d01δg=dt1δgp_t = \sum^T_{s=t} \delta^{s-t} g^s d_0 + \frac{\delta^{T+1-t} g^{T+1} d_0}{1 - \delta g} = \frac{d_t}{1 - \delta g}

  3. pt=0p_t = 0

  4. pt=cδtp_t = c \delta^{-t}

CC-BY-SA-4.0

Creative Commons License – This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International.