1 Econ 240C Power 17. 2 Outline The Law of One Price.

Slides:



Advertisements
Similar presentations
Cointegration and Error Correction Models
Advertisements

Autocorrelation Functions and ARIMA Modelling
1 Some Pitfalls in Testing … Japan Imports of Wheat US Pacific and Gulf Export Ports.
Chapter 12 Simple Linear Regression
Using SAS for Time Series Data
Nonstationary Time Series Data and Cointegration Prepared by Vera Tabakova, East Carolina University.
COINTEGRATION 1 The next topic is cointegration. Suppose that you have two nonstationary series X and Y and you hypothesize that Y is a linear function.
Analysis of Sales of Food Services & Drinking Places Julianne Shan Ho-Jung Hsiao Christian Treubig Lindsey Aspel Brooks Allen Edmund Becdach.
Financial Econometrics
Slide 1 DSCI 5340: Predictive Modeling and Business Forecasting Spring 2013 – Dr. Nick Evangelopoulos Lecture 7: Box-Jenkins Models – Part II (Ch. 9) Material.
Chapter 11 Autocorrelation.
Vector Error Correction and Vector Autoregressive Models
Chapter 12 Simple Linear Regression
Price of Gold and US Dollar Index Dwarakamayi Polakam Jennifer Griffeth Ashley Arlotti Rui Feng Ying Fan Qi He Qi Li Group C Presentation.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both.
1 Econ 240 C Lecture 3. 2 Part I Modeling Economic Time Series.
1 Econ 240 C Lecture White noise inputoutput 1/(1 – z) White noise input output Random walkSynthesis 1/(1 – bz) White noise input output.
Modeling Cycles By ARMA
1 Econ 240 C Lecture Time Series Concepts Analysis and Synthesis.
1 Takehome One month treasury bill rate.
1 Lecture Eleven Econ 240C. 2 Outline Review Stochastic Time Series –White noise –Random walk –ARONE: –ARTWO –ARTHREE –ARMA(2,2) –MAONE*SMATWELVE.
Econ 240C Lecture Review 2002 Final Ideas that are transcending p. 15 Economic Models of Time Series Symbolic Summary.
1 Power 2 Econ 240C. 2 Lab 1 Retrospective Exercise: –GDP_CAN = a +b*GDP_CAN(-1) + e –GDP_FRA = a +b*GDP_FRA(-1) + e.
1 Econ 240 C Lecture 6. 2 Part I: Box-Jenkins Magic ARMA models of time series all built from one source, white noise ARMA models of time series all built.
1 Identifying ARIMA Models What you need to know.
1 Lab Five. 2 Dark Northern Spring Wheat Import price in dollars in Japan: dnsj Import price in dollars in Japan: dnsj.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both.
1 Econ 240C Lecture Five Outline w Box-Jenkins Models w Time Series Components Model w Autoregressive of order one.
1 Econ 240 C Lecture 3. 2 Time Series Concepts Analysis and Synthesis.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both.
1 Econ 240C Lecture Five. 2 Outline w Box-Jenkins Models: the grand design w What do you need to learn? w Preview of partial autocorrelation function.
1 ECON 240C Lecture 8. 2 Outline: 2 nd Order AR Roots of the quadratic Roots of the quadratic Example: capumfg Example: capumfg Polar form Polar form.
1 ECON 240C Lecture 8. 2 Part I. Economic Forecast Project Santa Barbara County Seminar –April 17, 2003 URL:
Econ 240C Lecture Part I. VAR Does the Federal Funds Rate Affect Capacity Utilization?
1 ECON 240C Lecture 8. 2 Outline: 2 nd Order AR Roots of the quadratic Roots of the quadratic Example: change in housing starts Example: change in housing.
1 Lecture One Econ 240C. 2 Einstein’s blackboard, Theory of relativity, Oxford, 1931.
1 Econ 240C Lecture Five Outline w Box-Jenkins Models w Time Series Components Model w Autoregressive of order one.
Introduction At the start of most beginning economics courses we learn the economics is a science aimed toward answering the following questions: 1.What.
1 ECON 240C Lecture 8. 2 Part I. Economic Forecast Project Santa Barbara County Seminar Santa Barbara County Seminar  April 22, 2004 (April 17, 2003)
1 Some Pitfalls … II. 2 #2 Dark Northern Spring, DNS, 14% Table 4: Without Freight Rates DNSJ, DNSPJ DNSJ-DNSPJ lnDNSJ, lnDNSPJ Unit roots, no cointegration.
1 Econ 240C Power Outline The Law of One Price Moving averages as a smoothing technique: Power 11_06: slides Intervention models: Power 13_06:
1 Lab Four Postscript Econ 240 C. 2 Airline Passengers.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail sales –Add a quadratic term –Use both models to.
1 ECON 240C Lecture 8. 2 Outline: 2 nd Order AR Roots of the quadratic Roots of the quadratic Example: change in housing starts Example: change in housing.
What affects MFP in the long-run? Evidence from Canadian industries Danny Leung and Yi Zheng Bank of Canada, Research Department Structural Studies May.
Oil and the Macroeconomy of Kazakhstan Prepared for the 30 th USAEE North American Conference, Washington DC By Ferhat Bilgin, Ph.D. Student and Fred Joutz.
#1 EC 485: Time Series Analysis in a Nut Shell. #2 Data Preparation: 1)Plot data and examine for stationarity 2)Examine ACF for stationarity 3)If not.
Purchasing Power Parity A Survey on East European Countries ( ) Ioana Ceanga.
Module 4 Forecasting Multiple Variables from their own Histories EC 827.
Cointegration in Single Equations: Lecture 5
1 Econ 240C Lecture Five. 2 Part I: Time Series Components Model w The conceptual framework for inertial (mechanical) time series models: w Time series.
1 Lecture Plan : Statistical trading models for energy futures.: Stochastic Processes and Market Efficiency Trading Models Long/Short one.
Review of Unit Root Testing D. A. Dickey North Carolina State University (Previously presented at Purdue Econ Dept.)
Lecture 11: Simple Linear Regression
Financial Econometrics Lecture Notes 4
ECO 400 Vector Error Correction Models (VECM)
REGRESSION DIAGNOSTIC III: AUTOCORRELATION
Nonstationary Time Series Data and Cointegration
An Introduction to Macroeconometrics: VEC and VAR Models
Econ 240 C Lecture 4.
Econ 240C Lecture 18.
ECON 240C Lecture 7.
Applied Econometric Time-Series Data Analysis
CHAPTER 16 ECONOMIC FORECASTING Damodar Gujarati
Unit Roots 31/12/2018.
Vector AutoRegression models (VARs)
Chou, Mei-Ling Assistant Professor Nanya Institute of Technology
BOX JENKINS (ARIMA) METHODOLOGY
Correlation and Simple Linear Regression
Presentation transcript:

1 Econ 240C Power 17

2 Outline The Law of One Price

3 Law of One Price: Outline Definition: slides 5-6 Applied to wheat trade: slides: slides 7-9 Time Series Notation: slides Data and Traces: slides Show that Import Price, DNSPJ, is Evolutionary: slides Show that Import Price Minus Sum of Export Price (-1) + Freight Rate (-2) is stationary: slides

4 Outline Cont. Show that logs of import & export prices are evolutionary of order one: slides Show that log of price ratio is stationary: slides –Speed of convergence: slides Cointegration: slides 55 - –Long run equilibrium relationship between log of import price and log of export price (with freight and lags): slides –VAR speed of adjustment model: slide 58-60

5 The Law of One Price The New Palgrave Dictionary of Money and Finance –Next slide

6 The Law of One Price This law is an immediate consequence of the absence of arbitrage and, like the absence of arbitrage, follows from individual rationality. Departures from the no arbitrage condition imply that there are profit opportunities. These arise because it would be profitable for arbitrageurs to buy good i in the country in which it is cheaper and transport it to the country in which it is more expensive, and in doing so, profit in trade.

7 Commodity Trade Issues Well defined product: World Wheat Statistics –# 2 Dark Northern Spring 14% –Western White –Hard Winter Transport costs –US: export Pacific Ports Gulf Ports

8 Prices in $/metric ton Import price notation: DNSJ is Japanese import price in $/metric ton for Dark Northern Spring wheat Export price notation: DNSG is export price for Dark Northern Spring from a Gulf Port; DNSP is export price for Dark Northern Spring from a Pacific Port –Lagged one month because commodity arbitrage takes time

9 Transport Cost in $/Metric Ton Freight rates are forward prices and are lagged two months

10 Time Series Import Price: DNSJ Export Price (lagged one) Plus Freight (lagged two): DNSGT Logarithm of Price Ratio: ln [DNSJ/DNSGT] = lnDNSJ – lnDNSGT denoted lnratiodnsgjt = ln[1 + ∆/DNSGT] ~ ∆/DNSGT, the fractional price differential, where ∆ = DNSJ – DNSGT, and can be positive or negative

11 Time Series Is the log of the export price evolutionary, of order one? –Ln DNSJ = lndnsj Is the log of the import price evolutionary, of order one? –Ln DNSGT = lndnsgt Is their difference stationary, of order zero, ie. are they cointegrated? i.e. Is the log price ratio ( the fractional price differential) stationary? –Ln{DNSJ/DNSGT] =lnratiodnsjgt

12 Data

13 Table Three: Pacific DNS Log of Ratio of Import Price to Pacific Export Price (lag 1) Plus Pacific Freight (lag2) –Stationary No unit root AR (1) Model: root 0.54, normal residual Log of Import Price and Log of Pacific Export Price (lag 1) Plus Pacific Freight (lag 2) –Cointegrated VEC: one lag Rank: 1, 1, 1, 1, 2 Data: no trend; Integrating Equation: Intercept, no trend, rank one, 1%

14

15 Correlogram of Import Price, DNSJ

16 Unit Root Test

17 Dark Northern Spring Japan Export Price is Evolutionary The Import Price Minus the Sum of the Export Price (-1) + Freight Rate (-2) is Stationary So the Import Price and the Export Price Never Wander Off from each other, i.e. they are cointegrated

18

19Histogram

20Correlogram

21 Unit Root Test

22 ARONE Model

23Diagnostics

24 Correlogram of Residuals

25 Correlogram of Residuals Squared

26 ARCH-LM Test

27

28 Show that Logs of Prices Are Evolutionary

29

30

31

32 Conclude Log of Import Price, lndnsj, is evolutionary

33

34

35

36 Conclude Both the log of the import price and the log of the Pacific export price (lagged one) plus the Pacific Freight Rate (lagged two) are evolutionary, of order one. –To be of order one, not higher, their differences should be stationary, i.e. of order zero. –Unit root tests show this is the case

37

38 Log of Price Ratio

39

40

41

42

43

44

45

46

47

48

49 Conclusions Log of ratio of import price to the export price (lagged one) plus freight rate (lagged two) is stationary and is modeled as an autoregressive process of the first order with mean zero and root 0.54

50

51 How fast does any price differential get arbitraged to zero? Arone(t) = b*arone(t-1) + wn(t) Arone(t-1) = b* arone(t-2) + wn(t-1) Arone(t) = b[b* arone(t-2) + wn(t-1)] + wn(t) Arone(t) = b 2 *arone(t-2) + wn(t) + b*wn(t-1) Arone(t+2) = b 2 *arone(t) + wn(t+2) + b*wn(t+1) Arone(t+u) = b u *arone(t) + wn(t+u) + b*wn(t+u- 1) + …

52 Half Life 1.1 month ~ 5 weeks Arone(t+u) = b u *arone(t) + wn(t+u) + b*wn(t+u-1) + …. E t {Arone(t+u) = b u *arone(t) + wn(t+u) + b*wn(t+u-1) + …} E t Arone(t+u) = b u *arone(t) E t Arone(t+u) /arone(t) = ½ = b u Ln [E t Arone(t+u) /arone(t)] = ln(1/2) = u*lnb /lnb= /ln 0.54 = 1.1 = u

53

54 Half Life: root =0.54, Arone(t) =100 Time =uArone(t+u)bubu

55 Cointegration Logs of export price and import price (lagged with freight lagged) are of order one. Their difference is of order zero The long run relationship: –Lndnsj = c + b*lndnspjt + e –Where the residual is an estimate of price differential over time

56

57

58 Error Correction VAR dlndnsj(t)= a M *e(t-1) + wn M (t) + b 11 dlndnsj(t-1) + c 12 dlndnspjt(t-1) dlndnspjt(t)= -a x *e(t-1) + wn x (t) + b 21 dlndnsj(t-1) + c 22 dlndnspjt(t-1) a M and a x are speed of adjustment parameters of fractional change in import and export prices to the fractional price differential, i.e. e(t-1)

59

60 Significant speed of adjustment Parameter. If lndnsj is greater than the fitted value, i.e. Greater than c + b*lndnspjt, the Residual e(t) is positive, and next Period, lndnspjt will increase to Close the gap.

61 Johansen Cointegration Test

62 Johansen Table Summary

63 Diebold, Ch.4 p. 87

64

65

66 Null: No Cointegrating Equation Reject null at 1% level

67 Impulse response functions

68 Impulse response functions

69 Variance decomposition

70 Error Correction Model: All the way in one play

71

72 Error Correction VAR, One Lag

73 VEC Cont.

74 Johansen Summary Table

75 Johansen Test: No Data Trend, Intercept

76 Impulse Response Functions Order: lndnsj, lndnspjt

77 Impulse Response Functions Order: lndnspjt, lndnsj

78 Variance Decomposition Order: lndnsj, lndnspjt

79 Table 3: log ratio of Import Price/ [Export Price (lag 1) + Freight (lag 2)] DNSGJTDNSPJTWWPJTHWGJT AR(1) MA(1)0000 Model Resnormal VEC1 lag 5 Mod. rank0,0,1,0,21,1,1,1,2 1,1,1,1,1 Rank 15%1% (3/4) 1% all

80 Table 4: log ratio of Import Price/ [Export Price (lag 1)] DNSGJDNSPJWWPJHWGJ AR(1) MA(1)0000 Model Resnormal VEC1 lag 5 Mod. rank0,0,0,0,20,0,0,0,0 0,0,1,0,2 Rank 15%