chapter 1: market indexes, financial time series and their ...maling/msbd 5006msdm5053... ·...
TRANSCRIPT
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Chapter 1: Market Indexes, Financial Time
Series and their Characteristics
• What is time series (TS) analysis?
Observe the following two data sets:
Hang Seng 12877 12850 13023 · · ·IndexDate 30.8.04 31.8.04 01.9.04 · · ·
Student’s 130kg 200kg 45kg · · ·WeightsStudents A B C · · ·
What is the difference between these two data
sets?
• Definition:
A time series (TS) is a sequence of random
variables labeled by time t.
Time series data are observations of TS.
1
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TSA History
• Linear TSA: The beginning/babyhood 1927
• George Udny Yule (1871-1951), a British statis-tician.
• Eugen Slutsky (1880-1948), a Russian/Sovietmathematical statistician, economist and po-
litical economist.
• Herman Ole Andreas Wold ( 1908– 1992)
• Peter Whittle (1927-) (ARMA model)
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• Linear TSA in 1970’s
• George Edward Pelham Box (1919–2013), aBritish statistician (quality control, TSA, de-
sign of experiments, and Bayesian inference).
He has been called “one of the great statistical
minds of the 20th century”.
• Sir Ronald Aylmer Fisher (1890 –1962)
• Box & Jenkins (1976) Time Series Analysis:Forecasting and Control
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• Nonlinear TSA in 1950’s
• Patrick Alfred Pierce Moran (1917–1988), anAustralian statistician (probability theory, pop-
ulation and evolutionary genetics).
• Peter Whittle (1927-, New Zealand), stochas-tic nets, optimal control, time series analysis,
stochastic optimisation and stochastic dynam-
ics.
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• Nonlinear TSA in 1980’s
• Howell Tong (1944–, in Hong Kong) (TARmodel).
• Robert Fry Engle III (1942–) is an Americaneconomist and the winner of the 2003 Nobel
Memorial Prize.
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• What is financial time series (FTS)?
Examples
1. Daily log returns of Hang Sang Index .
2. Monthly log return of exchange rates of
Japan-USA.
3. China life daily stock data.
4. HSBC daily stock data.
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0 2000 4000 6000 8000
020
0040
00Nasdaq daily closing price
0 100 200 300 400
020
0040
00
Nasdaq monthly closing price
Nasdaq daily and monthly closing price from Sep
1, 1980 to Sep 1, 2016.
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Special features of FTS
1. Theory and practice of asset valuation over
time.
2. Added more uncertainty. For example, FTS
must deal with the changing business and eco-
nomic environment and the fact that volatility is
not directly observed.
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General objective of the course
to provide some basic knowledge of financial time
series data
to introduce some statistical tools and economet-
ric models useful for analyzing these series.
to gain empirical experience in analyzing FTS
to study methods for assessing market risk
to analyze high-dimensional asset returns.
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Special objective of the course
Past data =⇒TS r.v. Zt=⇒ future of TS.
(a) E(Zn+l|Z1, · · · , Zn
),
(b) P (a ≤ Zn+l ≤ b|Z1, · · · , Zn) for some a < b.
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1.1 Asset Returns
Let Pt be the price of an asset at time t, and
assume no dividend. One-period simple return or
simple net return:
Rt =Pt − Pt−1
Pt−1=
Pt
Pt−1− 1.
Gross return
1 +Rt =Pt
Pt−1or Pt = Pt−1(1 +Rt).
Multi-period simple return or the k−period simplenet return:
Rt(k) =Pt − Pt−k
Pt−k=
Pt
Pt−k− 1.
Gross return
1 +Rt(k) =Pt
Pt−k=
Pt
Pt−1×
Pt−1Pt−2
× · · · ×Pt−k+1Pt−k
= (1+Rt)(1 +Rt−1)× · · · × (1 +Rt−k+1)
=k−1∏j=0
(1 +Rt−j).
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Example: Suppose the daily closing prices of astock are
Day 1 2 3 4 5Price 37.84 38.49 37.12 37.60 36.30
1. What is the simple return from day 1 to day 2?
Ans: R2 =38.49−37.84
37.84 = 0.017.
2. What is the simple return from day 1 to day 5?
Ans: R5(4) =36.30−37.84
37.84 = −0.041.
3. Verify that
1 + R5(4) = (1+R2)(1 +R3)(1 +R4)(1 +R5).
Time interval is important! Default is one year.
Annualized (average) return:
Annualized[Rt(k)] =
k−1∏j=0
(1 +Rt−j)
1/k − 1.An approximation:
Annualized[Rt(k)] ≈1
k
k−1∑j=0
Rt−j.
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Continuous compounding
Assume that the interest rate of a bank deposit is
10% per annum and the initial deposit is $1.00.
If the bank pays interest m times a year, then the
interest rate for each payment is 10%/m, and the
net value of the deposit become
$1×(1+
0.1
m
)m.
Illustration of the power of compounding (int. rate
10% per annum):
Type m(payment) Int. NetAnnual 1 0.1 $1.10000Semi-Annual 2 0.05 $1.10250Quarterly 4 0.025 $1.10381Monthly 12 0.0083 $1.10471Weekly 52 0.1/52 $1.10506Daily 365 0.1/365 $1.10516Continuously ∞ $1.10517
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In general, the net asset value A of the continuous
compounding is
A = C exp(r × n),
r is the interest rate per annum, C is the initial
capital, n is the number of years, and and exp is
the exponential function.
Present value:
C = A exp[−r × n].
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Continuously compounded (or log) return
rt = ln(1 +Rt) = lnPt
Pt−1= pt − pt−1,
where pt = ln(Pt)
Multi-period log return:
rt(k) = ln[1 +Rt(k)]
= ln[(1 +Rt)(1 +Rt−1)(1 +Rt−k+1)]
= ln(1 +Rt) + ln(1 +Rt−1)
+ · · ·+ ln(1 +Rt−k+1)= rt + rt−1 + · · ·+ rt−k+1.
Example (continued). Use the previous daily prices.
1. What is the log return from day 1 to day 2?
A: r2 = ln(38.49)− ln(37.84) = 0.017.
2. What is the log return from day 1 to day 5?
A: r5(4) = ln(36.3)− ln(37.84) = −0.042.
3. It is easy to verify r5(4) = r2 + · · ·+ r5.
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Portfolio return:
Suppose that we have N assets with the i − thasset price is Pit at time t.
Then price of Portfolio at (t− 1)time is
Pp,t−1 =N∑
i=1
Pi,t−1,
and the proportion of the i−asset in the wholePortfolio is
wi =Pi,t−1Pp,t−1
andN∑
i=1
wi = 1.
At time t, the price of Portfolio is
Pp,t =N∑
i=1
Pi,t.
Thus, the simple return of this Portfolio is
Rpt =Pp,t − Pp,t−1
Pp,t−1=
N∑i=1
Pit − Pit−1Pp,t−1
=N∑
i=1
Pi,t−1Pp,t−1
Pit − Pit−1Pit−1
=N∑
i=1
wiRit.
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Example: An investor holds stocks of IBM, Mi-
crosoft and Citi- Group. Assume that her capital
allocation is 30%, 30% and 40%. The monthly
simple returns of these three stocks are 1.42%,
3.37% and 2.20%, respectively. What is the mean
simple return of her stock portfolio in percentage?
Answer:
E(Rt) = 0.3×1.42+0.3×3.37+0.4×2.20 = 2.32.
The continuously compounded returns of a port-
folio do not have the previous convenient property.
When Rit is small in absolute value, we have
rp,t ≈N∑
i=1
wirit,
where rit is the log-return of asset i.
Dividend payment: let Dt be the dividend payment
of an asset between dates t − 1 and t and Pt bethe price of the asset at the end of period t.
Rt =Pt +DtPt−1
− 1, rt = ln(Pt +Dt)− ln(Pt−1).
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Excess return: (adjusting for risk)
Zt = Rt −R0t, zt = rt − r0t,
where r0t denotes the log return of a reference
asset (e.g. risk-free interest rate) such as short-
term U.S. Treasury bill return, etc..
Relationship:
rt = ln(1 +Rt), Rt = ert − 1.
If the returns are in percentage, then
rt = 100×ln(1+Rt
100), Rt = [exp(rt/100)−1]×100.
Temporal aggregation of the returns produces
1 +Rt(k) = (1+Rt)(1 +Rt−1) · · · (1 +Rt−k+1),
rt(k) = rt + rt−1 + · · ·+ rt−k+1.
These two relations are important in practice, e.g.
obtain annual returns from monthly returns.
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Example: If the monthly log returns of an asset
are 4.46%, -7.34% and 10.77%, then what is the
corresponding quarterly log return?
Answer: (4.46 - 7.34 + 10.77)% = 7.89%.
Example: If the monthly simple returns of an asset
are 4.46%, -7.34% and 10.77%, then what is the
corresponding quarterly simple return?
Answer: R = (1+0.0446)(1−0.0734)(1+0.1077)−1 = 1.0721− 1 = 0.0721 = 7.21%.
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1.2 Distributional properties of returns
Is rt a data or random variable?
What is the difference?
Key: What is the distribution of
(rt : t = 1, · · · , T )?
Review of theoretical statistics:
X is a random variable, {X ≤ x} is an event and
FX(x) = P ({X ≤ x}) = P (X ≤ x),
is called its cumulative distribution function (CDF).
The CDF is nondecreasing (i.e., FX(x1) ≤ FX(x2)if x1 ≤ x2) and satisfies
FX(−∞) = 0 and FX(∞) = 1.
f(x) = F ′(x) is called the density function of X.
FX(x) = P (X ≤ x) =∫ x−∞
f(x)dx.
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Example:(Normal Distribution). Let X ∼ N(µ, σ2),
f(x) =1√2πσ2
exp
(−
(x− µ)2
2σ2
), −∞ ≤ x ≤ ∞,
The density function of Normal distribution.
Quantile: For a given probability p,
xp = inf{x|FX(x) ≥ p}
is called the pth quantile of the random variableX. If f(x) exists,
FX(xp) =∫ xp−∞
f(x)dx = p and P (X ≥ x1−p) = p.
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Moments of a random variable X:
Mean and variance:
µx = E(X) and σ2x = Var(X) = E(X − µx)2
Skewness (symmetry) and kurtosis (fat-tails)
S(x) = E(X − µx)3
σ3x, K(x) = E
(X − µx)4
σ4x.
K(x)− 3: Excess kurtosis.
The l−th moment and l−th central moment:
m′l = E(Xl) =
∫ ∞−∞
xlf(x)dx.
ml = E[(X − µx)l] =∫ ∞−∞
(x− µx)lf(x)dx,
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Why are mean and variance of returns important?
They are concerned with long-term return and
risk, respectively.
Why is symmetry of interest in financial study?
Symmetry has important implications in holding
short or long financial positions and in risk man-
agement.
Why is kurtosis important?
Related to volatility forecasting, efficiency in esti-
mation and tests, etc.
High kurtosis implies heavy (or long) tails in dis-
tribution.
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Example:(Normal Distribution). Let X ∼ N(µ, σ2),
f(x) =1√2πσ2
exp
(−
(x− µ)2
2σ2
), −∞ ≤ x ≤ ∞,
The density function of Normal distribution.
E(X) = µ
Var(X) = σ2
S(X) = 0
K(X) = 3
ml = 0, for l is odd.
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Example:(Student’s-t distribution).
Let X follow Students t distribution with v degrees
of freedom.
f(x) =Γ(v+12 )√vπΓ(v2)
(1 +x2
v)−
v+12 ,
where Γ(t) =∫∞0 x
t−1e−xds is a gamma function.
The density function of Student’s t-distribution.
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Then
E(X) = 0, v > 1
Var(X) =v
v − 1, v > 2
S(X) = 0, v > 3
K(X) =6
v − 4, v > 4.
• Existence of moments depends on degrees offreedom (df) parameter v.
• Cauchy = Students-t with 1 df. Only densityexists.
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Example:(Chi-squared distribution).
Let X follow Chi-squared distribution with k de-
grees of freedom.
f(x) =x(k/2−1)e−x/2
2k/2Γ(k2), x > 0.
The density function of Chi-squared distribution.
E(X) = k, Var(X) = 2k
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Joint Distribution: The following is a joint dis-tribution function of two variables:X and Y ,
FX,Y (x, y) = P (X ≤ x, Y ≤ y),where x ∈ R, y ∈ R.
If the joint probability density function fx,y(x, y) ofX and Y exists, then
FX,Y (x, y) =∫ x−∞
∫ y−∞
fx,y(w, z)dzdw.
Marginal Distribution: The marginal distributionof X is given by
FX(x) = FX,Y (x,∞).Thus, the marginal distribution of X is obtainedby integrating out Y . A similar definition appliesto the marginal distribution of Y .
Conditional density function is
fx|y(x) ≡ fx,y(x, y)/fy(y) or
fx,y(x, y) = fx|y(x)× fy(y)
X and Y are independent random vectors if andonly if fx|y(x) = fx(x). In this case,
fx,y(x, y) = fx(x)× fy(y).
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Estimation:
Data: {x1, · · · , xT}.
sample mean:
µ̂x =1
T
T∑t=1
xt,
sample variance:
σ̂2x =1
T − 1
T∑t=1
(xt − µ̂x)2,
sample skewness:
Ŝ(x) =1
(T − 1)σ̂3x
T∑t=1
(xt − µ̂x)3,
sample kurtosis:
K̂(x) =1
(T − 1)σ̂4x
T∑t=1
(xt − µ̂x)4.
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Random sample: {x1, · · · , xT}.
µ̂x, σ̂2x, Ŝ(x) and K̂(x) are random.
If Xi’s are iid N(µ, σ2), we can show that
Ŝ(x) ∼ N(0,6
T), K̂(x)− 3 ∼ N(0,
24
T).
Some simple tests for normality (for large T ).
1. Test for H0: symmetry v.s. H1: asymmetry:
S∗ =Ŝ(x)√6/T
∼ N(0,1)
if normality holds.
Decision rule: Reject H0 of a symmetric distribu-tion if |S∗| > Zα/2 or p-value is less than α.
2. Test for
H0 : K(x) = 3(thick tail) v.s. H1 : K(x) ̸= 3.
K∗ =K̂(x)− 3√
24/T∼ N(0,1)
if normality holds.
Decision rule: Reject H0 of normal tails if |K∗| >Zα/2 or p-value is less than α
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3. A joint test (Jarque-Bera test):
JB = (K∗)2 + (S∗)2 ∼ χ22,
if normality holds, where χ22 denotes a chi-squared
distribution with 2 degrees of freedom.
H0 : X ∼ N(µ, σ2) v.s.H1 : X ̸∼ N(µ, σ2).
Decision rule: Reject H0 of normality if JB >
χ22(α) or p-value is less than α.
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Goodness-of-Fit Tests in SAS
Assume r1, · · · , rn are i.i.d with a common dis-tribution function F(x). Order {r1, · · · , rn} fromsmallest to largest as r(1), ..., r(n). The empirical
distribution function, Fn(x), is defined as
Fn(x) =1
n
n∑t=1
I{rt ≤ x} =1
n
n∑i=1
I{r(i) ≤ x}
=
0 if x < r(1)in if r(i) ≤ x < r(i+1), i = 1, · · · , n− 11 if x ≥ r(n).
Fn(x) is an estimator of F (x) and Fn(x) ≈ F (x).
PROC UNIVARIATE provides three EDF tests:
Kolmogorov-Smirnov (D)
Anderson-Darling (A− sq)
Cramr-von Mises (W − sq)
Kolmogorov D Statistic:
Dn = supx
|Fn(x)− F (x)|.
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2.3 Empirical Properties of Returns
Data: Nasdaq daily and monthly closing price fromSep 1, 1980 to Sep 1, 2016.
Descriptive Statistics of Nasdaq daily index withn = 9081
Mean SD Skewness Kurtosis Minimum MaximumS. return 0.05 1.34 -0.05 8.77 -11.35 14.17Log return 0.04 1.34 -0.26 8.59 -12.04 13.25
Descriptive Statistics of Nasdaq monthly indexn = 433
Monthly Mean SD Skewness Kurtosis Minimum MaximumS. return 0.97 6.18 -0.52 2.00 -27.23 21.98Log return 0.77 6.27 -0.89 3.09 -31.79 19.87
0 2000 4000 6000 8000
−10
05
10
Nasdaq daily simple return
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0 2000 4000 6000 8000
−10
05
10
Nasdaq daily log return
Nasdaq daily and monthly closing price from Sep
1, 1980 to Sep 1, 2016.
0 100 200 300 400
−20
010
20
Nasdaq monthly simple return
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0 100 200 300 400
−30
−10
10Nasdaq monthly log return
Nasdaq daily and monthly closing price from Sep
1, 1980 to Sep 1, 2016.
−0.4 −0.2 0.0 0.2 0.4
02
46
8
Den
sity
S return
Normal
−0.4 −0.2 0.0 0.2 0.4
02
46
8
Den
sity
Log return
Normal
Comparison of empirical and normal densities for
monthly simple and log returns of Nasdaq index.