<p>It is well known that Japanese stock price and exchange rate(USD/JPY) is correlation, So I built a VAR model in python about stock data(Tokyo stock price index) and exchange rate(USD/JPY) and I fitted it by AIC.</p>

<p>As a result, a certain year(2017) was calculated that order is zero, 
only constant value is displayed.</p>

<p>That means current stock price can explain only constant value,
not need past price or exchange rate.</p>

<p>That calculation result is some times occurred when economic data analysis
or my calculation is wrong?</p>



<pre><code>data17['TOPIX'] = np.log['TOPIX'].diff()
data17['EXCH']  = np.log['EXCH'].diff()

result = VAR(data17).fit(maxlags=20,ic='aic')
</code></pre>

<pre><code>Results for equation TOPIX
========================================================================
           coefficient       std. error           t-stat            prob
------------------------------------------------------------------------
const         0.000937         0.000568            1.650           0.099
========================================================================

Results for equation EXCH
========================================================================
           coefficient       std. error           t-stat            prob
------------------------------------------------------------------------
const        -0.000159         0.000409           -0.388           0.698
========================================================================
<span class="math-container">```</span>
</code></pre>

CC BY-SA 4.0
2019-12-01T11:06:41.457Z
Well without seeing your code and model specification it’s very hard to say if that’s due to some mistake in code or just simply because using AIC constant model has the best fit. If you are using classic VAR you should also remember that variables should stationarity (and your variables are definitely not) otherwise you will get some roots that are on the unit circle and model will be biased (although that’s not necessary what drives this result). There are so many things that can go wrong here that without seeing model diagnostics and specification you can’t tell...
1muflon1
CC BY-SA 4.0
2019-12-08T07:28:27.623Z
I fixed my question.
I took TOPIX data from Wall Street Journal and exchange rate data from investing.com.
TOPIX data is Japanese Standard Time, and exchange rate is american Eastern Standard Time I think, so i took a data below and match the standard time.
 1)TOPIX is open time data.
 2)Exchange rate is close time data.
(There are 3 or 4 hours time lag)
Eiji
CC BY-SA 4.0
2019-12-08T07:34:57.207Z
https://www.investing.com/currencies/usd-jpy
Eiji
CC BY-SA 4.0
2019-12-08T07:35:03.020Z
https://quotes.wsj.com/index/JP/TOKYO%20EXCHANGE%20(TOPIX)/180460?mod=searchresults_companyquotes
Eiji
<stock-market><time-series>
Stock price and exchange rate is correlation, but VAR model order is zero
Eiji
# Stock price and exchange rate is correlation, but VAR model order is zero

(Asked by: username_0 on 2019-12-01)

It is well known that Japanese stock price and exchange rate(USD/JPY) is correlation, So I built a VAR model in python about stock data(Tokyo stock price index) and exchange rate(USD/JPY) and I fitted it by AIC.
As a result, a certain year(2017) was calculated that order is zero, 
only constant value is displayed.
That means current stock price can explain only constant value,
not need past price or exchange rate.
That calculation result is some times occurred when economic data analysis
or my calculation is wrong?
data17['TOPIX'] = np.log['TOPIX'].diff()
data17['EXCH']  = np.log['EXCH'].diff()

result = VAR(data17).fit(maxlags=20,ic='aic')

Results for equation TOPIX
========================================================================
           coefficient       std. error           t-stat            prob
------------------------------------------------------------------------
const         0.000937         0.000568            1.650           0.099
========================================================================

Results for equation EXCH
========================================================================
           coefficient       std. error           t-stat            prob
------------------------------------------------------------------------
const        -0.000159         0.000409           -0.388           0.698
========================================================================
```

**Tags:** stock-market, time-series

> **Comments:**
> **username_1**: Well without seeing your code and model specification it’s very hard to say if that’s due to some mistake in code or just simply because using AIC constant model has the best fit. If you are using classic VAR you should also remember that variables should stationarity (and your variables are definitely not) otherwise you will get some roots that are on the unit circle and model will be biased (although that’s not necessary what drives this result). There are so many things that can go wrong here that without seeing model diagnostics and specification you can’t tell...  
> **username_0**: I fixed my question. I took TOPIX data from Wall Street Journal and exchange rate data from investing.com. TOPIX data is Japanese Standard Time, and exchange rate is american Eastern Standard Time I think, so i took a data below and match the standard time.  1)TOPIX is open time data.  2)Exchange rate is close time data. (There are 3 or 4 hours time lag)  
> **username_0**: https://www.investing.com/currencies/usd-jpy  
> **username_0**: https://quotes.wsj.com/index/JP/TOKYO%20EXCHANGE%20(TOPIX)/180460?mod=searchresults_companyquotes  

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## 0 answers

