introduction to financial time series analysis with R โดย อ.ดร.ชัยณรงค์ เกษามูล และ Data Science Thailand

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2. 1st September 2016
Introduction to
FinancialTime Series
NIDA Business Analytics and Data Sciences Conference
Chainarong Kesamoon, PhD
Thammasat University & Data Science Thailand Team
chainaron.kes@gmail.com

3. Outline:
✤ What is time series?
✤ How to model financial time series?
✤ Forecasting

4. What is time
series?
✤ Numeric data
✤ 1, 1, 2, 3, 5, 8,…
✤ 2.8, 1.9, -10, 25, -6.7,…
✤ Time series
✤ measured by time
✤ Time matters!!!

6. FinancialTime
Series
✤ Stock prices
✤ Market index
✤ Money Exchange rates
✤ gold, oil

7. Date
Typical data set
source: Google Finance

8. We would like to
know:
✤ Tomorrow prices!!!
✤ Chance of profit or loss
✤ Future value of our money
✤ That’s all about
“Return & Risk”

9. Return
✤ Today’s return=today’s price - yesterday’s price
✤ Percentage return = Today’s return/yesterday’s price
✤ Log return = log(today’s price) - log(yesterday’s price)
✤ know return then we also know price

10. Date
Better analyze returns than prices

11. Date
Let’s analyze!

12. Correlation
✤ Positive correlation :
✤ More experience, more salary
✤ Where there’s a will, there’s a way.
✤ Negative correlation:
✤ The higher the Doy, the lower the temperature.
✤ The more one works, the less free time one has.
✤ No correlation:
✤ The color of your shirt, the color of my shoes.

13. Coefficient of
Correlation
mpg Miles/(US) gallon
cyl Number of cylinders
disp Displacement (cu.in.)
hp Gross horsepower
drat Rear axle ratio
wt Weight (lb/1000)
qsec 1/4 mile time
vs V/S
am Transmission (0 = automatic, 1 = manual)
gear Number of forward gears
carb Number of carburetors

14. Date
Correlation between returns on different days

15. Can we forecast
the return?
✤ Imagine you have tossed a
normal coin ten times, last five
outcomes were all head.
✤ Do you expect that the next
outcome would be head?
✤ If there is no correlation, the
next outcome would be like
tossing a coin.
H T T H
T H H H
H H
1 2 3 4
5 6 7 8
9 10 11
?

16. Efficient Market Hypothesis
✤ A financial economist and passionate defender of the efficient markets hypothesis (EMH) was
walking down the street with a friend. The friend stops and says, "Look, there's a $20 bill on
the ground."
✤ The economist turns and says, "Boy, this must be our lucky day! Better pick that up quick
because the market is so efficient it won't be there for very long. Finding a $20 bill lying
around happens so infrequently that it would be foolish to spend our time searching for more
of them. Certainly, after assigning a value to the time spent in the effort, an 'investment' in
trying to find money lying on the street just waiting to be picked up would be a poor one. I am
also certainly not aware of lots of people, if any, getting rich mining beaches with metal
detectors."
✤ When he had finished they both look down and the $20 bill was gone!
source: http://www.etf.com/sections/features/123.html

17. Date
Let’s try another way

18. Why squared return?
✤ The variance of return is calculated from squared returns.
✤ Why variance? What is it?
✤ Variance is the degree of variation
✤ High variance => high volatility=> high risk
✤ Volatility is forecastable
✤ High risk, high return (but return can be either + or - )

19. Major Stylized Facts for Return
I. The distribution of returns is not normal, it has a high
peak and fat tails.
II. There is almost no correlation between returns for
different days.
III.There is positive correlation between squared returns
on nearby days, likewise for absolute returns.

20. Time series models
✤ General time series models:
✤ MA : moving average
✤ AR : autoregressive
✤ ARMA : autoregressive moving average
✤ ARIMA, ARFIMA,…
✤ Financial time series models:
✤ EWMA : exponentially weighted moving average
✤ (G)ARCH : (generalized) autoregressive conditional heteroskedastic
✤ SV : stochastic volatility
✤ Asset pricing model
✤ Black-Scholes model

21. Robert F. Engle Tim Bollerslev
Nobel Prize in
Economic Sciences
2003
GARCH model
Generalized AutoRegressive Conditional Heteroskedasticity Model
rt+1 = µ + t+1✏t+1
2
t+1 = ! + ↵(rt µ)2
+ 2
t
where ✏t+1 ⇠ N(0, 1)

22. Examples
✤ GARCH volatility
forecasting using data up to
5 Nov 2015
Date Return SD
6 Nov 0 0.384
7 Nov 0 0.390
8 Nov 0 0.396
9 Nov 0 0.401
10 Nov 0 0.407
11 Nov 0 0.412
12 Nov 0 0.416
13 Nov 0 0.421

23. "When Professors Scholes and
Merton and I invested in
warrants, Professor Merton lost
the most money.And I lost the
least”
– Fischer Black –
Nobel Prize in Economic Sciences1997:
Fischer Black, Myron Scholes, and Robert Merton

24. Challenging, isn’t it?
✤ Financial time series is challenging as it is quite difficult to
forecast.
✤ Multivariate time series is also of interest, but it is even more
difficult to model multiple time series together.
✤ Most financial models were created some years ago, at the time
that less data were available.
✤ Nowadays, we can access more and more data, that would be a
good opportunity to explore and create better models for
financial market.

25. “Moltes Gracies”
–Chainarong Kesamoon-