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Introduction to Financial Time Series Analysis with R
The First NIDA Business Analytics and Data Sciences Contest/Conferen...
1st September 2016
Introduction to
FinancialTime Series
NIDA Business Analytics and Data Sciences Conference
Chainarong Ke...
Outline:
✤ What is time series?
✤ How to model financial time series?
✤ Forecasting
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
✤ Ti...
FinancialTime
Series
✤ Stock prices
✤ Market index
✤ Money Exchange rates
✤ gold, oil
Date
Typical data set
source: Google Finance
We would like to
know:
✤ Tomorrow prices!!!
✤ Chance of profit or loss
✤ Future value of our money
✤ That’s all about 

“Re...
Return
✤ Today’s return=today’s price - yesterday’s price
✤ Percentage return = Today’s return/yesterday’s price
✤ Log ret...
Date
Better analyze returns than prices
Date
Let’s analyze!
Correlation
✤ Positive correlation :
✤ More experience, more salary
✤ Where there’s a will, there’s a way.
✤ Negative corr...
Coefficient of
Correlation
mpg Miles/(US) gallon
cyl Number of cylinders
disp Displacement (cu.in.)
hp Gross horsepower
dr...
Date
Correlation between returns on different days
Can we forecast
the return?
✤ Imagine you have tossed a
normal coin ten times, last five
outcomes were all head.
✤ Do you e...
Efficient Market Hypothesis
✤ A financial economist and passionate defender of the efficient markets hypothesis (EMH) was
wa...
Date
Let’s try another way
Why squared return?
✤ The variance of return is calculated from squared returns.
✤ Why variance? What is it?
✤ Variance is...
Major Stylized Facts for Return
I. The distribution of returns is not normal, it has a high
peak and fat tails.
II. There ...
Time series models
✤ General time series models:
✤ MA : moving average
✤ AR : autoregressive
✤ ARMA : autoregressive movin...
Robert F. Engle Tim Bollerslev
Nobel Prize in
Economic Sciences
2003
GARCH model
Generalized AutoRegressive Conditional He...
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 0.3...
"When Professors Scholes and
Merton and I invested in
warrants, Professor Merton lost
the most money.And I lost the
least”...
Challenging, isn’t it?
✤ Financial time series is challenging as it is quite difficult to
forecast.
✤ Multivariate time ser...
“Moltes Gracies”
–Chainarong Kesamoon-
introduction to financial time series analysis with R  โดย อ.ดร.ชัยณรงค์ เกษามูล และ Data Science Thailand ในงาน THE FIRST...
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introduction to financial time series analysis with R โดย อ.ดร.ชัยณรงค์ เกษามูล และ Data Science Thailand ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND

introduction to financial time series analysis with R โดย อ.ดร.ชัยณรงค์ เกษามูล และ Data Science Thailand ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND

introduction to financial time series analysis with R โดย อ.ดร.ชัยณรงค์ เกษามูล และ Data Science Thailand ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND

  1. 1. Introduction to Financial Time Series Analysis with R The First NIDA Business Analytics and Data Sciences Contest/Conference วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์ https://businessanalyticsnida.wordpress.com https://www.facebook.com/BusinessAnalyticsNIDA/ -ข้อมูลอนุกรมเวลาทางการเงิน เช่น SET index จากตลาดหลักทรัพย์แห่งประเทศไทย -การวิเคราะห์ข้อมูลอนุกรมเวลาทางการเงินขั้นสูง ทั้งที่ใช้ในการพยากรณ์และการประมาณค่า Volatility ได้แก่ -autoregressive moving average model (ARMA model) -Autoregressive conditional heteroskedasticity (ARCH) -Generalized autoregressive conditional heteroscedasticity(GARCH) อ.ดร.ชัยณรงค์ เกษามูล ภาควิชาคณิตศาสตร์และสถิติ คณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยธรรมศาสตร์ และ Data Science Thailand นวมินทราธิราช ห้อง 4001 เวลา 16.00-17.00.
  2. 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. 3. Outline: ✤ What is time series? ✤ How to model financial time series? ✤ Forecasting
  4. 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!!!
  5. 5. FinancialTime Series ✤ Stock prices ✤ Market index ✤ Money Exchange rates ✤ gold, oil
  6. 6. Date Typical data set source: Google Finance
  7. 7. We would like to know: ✤ Tomorrow prices!!! ✤ Chance of profit or loss ✤ Future value of our money ✤ That’s all about 
 “Return & Risk”
  8. 8. 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
  9. 9. Date Better analyze returns than prices
  10. 10. Date Let’s analyze!
  11. 11. 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.
  12. 12. 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
  13. 13. Date Correlation between returns on different days
  14. 14. 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 ?
  15. 15. 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
  16. 16. Date Let’s try another way
  17. 17. 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 - )
  18. 18. 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.
  19. 19. 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
  20. 20. 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)
  21. 21. 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
  22. 22. "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
  23. 23. 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.
  24. 24. “Moltes Gracies” –Chainarong Kesamoon-

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