Practical Examples using Eviews

Presented by 顏廣杰 2013/10/24

2.5 Estimation of an optimal hedge ratio   1.

1.

This section shows how to run a bivariate regression using Eviews.

We focus on the relationship between SPOT and FUTURES: Level regression (long run relationship) 𝑆 𝑡 𝛼 + 𝑡 Return regression (short run relationship)   𝑟 𝑆,𝑡 𝛼 + 𝐹,𝑡 The appropriate hedge ratio will be the slope estimate, and the independent variable is the futures return.

𝛽 , in a regression where the dependent variable is the spot returns Test whether  𝛽 = 1 or not, we can View  Coeff. Restrictions. Type C(2)=1.

Coeff. Tests

Input Data

Descriptive Statistics  

Genr

 type rfutures=100*dlog(futures)

rspot=100*dlog(spot)

Do not forget to Save the workfile.

Run Regression   If you want to save the summary statistics, you must name them by clicking Name and then choose a name, e.g. Descstats. We can now proceed to estimate the regression.

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Name

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returnreg

 In the same way, we also obtain levelreg

Test Coefficients of Regression  Suppose now that we wanted to test the null hypothesis that 𝐻 0 : 𝛽 = 1 rather than 𝐻 0 : 𝛽 = 0 .

Example for CAPM

Generate New Variables    

RSANDP=100*DLOG(SANDP) RFORD=100*DLOG(FORD) USTB3M=USTB3M/12 ERSANDP=RSANDP-USTB3M

CAPM test   To estimate the CAPM equation, click on Equation 𝑟 𝐹𝑜𝑟𝑑 − 𝑟 𝑓 𝑡 = 𝛼 + 𝛽 𝑟 Type in the equation window 𝑚 − 𝑟 𝑓 𝑡 + 𝑢 𝑡

ERFORD C ERSANDP

Or

DLOG(FORD)-USTB3M C DLOG(SANDP)-USTB3M

APT-style Model   In the spirit of APT, the following example will examine regressions that seek to determine whether the monthly returns on Microsoft stock an be explained by reference to unexpected changes in a set of macroeconomic and financial variables.

Press Genr

dspread = baa_aaa_spread – baa_aaa_spread(-1) inflation = 100*dlog(cpi) term = ustb10y – ustb3m

ermsoft = rmsoft – mustb3m (excess return of Microsoft)  Stepwise regression

Stepwise regression

Testing for heteroscedasticity  If the residuals of the regression have systematically changing variability over the sample, that is a sign of heteroscedasticity.

30 20 10 0 -10 -20 -30 -40 -50 -60 86 88 90 92 94 96 98 00 02 04 06 ERMSOFT Residuals

 To test for heteroscedasticity using White’s test.

Using White’s modified standard error estimates in EViews The heteroscedasticity-consistent s.d. errors are smaller than OLS Durbin-Watson (DW) is a test for first order autocorrelation.

Detecting autocorrelation  𝑢 𝑡 Breusch-Godfrey test: = 𝜌 1 𝑢 𝑡−1 + 𝜌𝑢 𝑡−2 𝐻 0 : 𝜌 1 𝐻 1 : 𝜌 1 + ⋯ + 𝜌 = 𝜌 2 𝑟 𝑢 𝑡−𝑟 = ⋯ = 𝜌 ≠ 0 𝑜𝑟 ⋯ 𝑜𝑟 𝜌 𝑟 𝑟 + 𝑣 𝑡 , 𝑣 𝑡 ~𝑁(0, 𝜎 𝑣 2 = 0 ) ≠ 0

Testing for non-normality   The Bera-Jarque normality tests

View

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Test Residual Tests

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Histogram

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Normality

Multicollinearity  

Quick/Group

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Statistics/Correlations

In the dialog box that appears:

Ersandp dprod dcredit dinflation dmoney dspread rterm

RESET tests 

View

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Stability tests

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Ramsey RESET test

Stability tests 

View

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Stability Tests

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Chow Breakpoint Test

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View

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Only) Stability Tests

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Recursive Estimates (OLS