<p>Writing as an academic modeller who would use the outputs of such models, what's required (and what I look for as a reviewer) is:</p>

<ul>
<li>reproducibility: the algorithm is described with sufficient detail to enable someone else to reimplement it;</li>
<li>validation, using data that is completely distinct from the calibration dataset; and</li>
<li>that the output is a <em>distribution</em> of forecasts, not just point values.</li>
</ul>

<p>Such a distribution can then be fed into ensemble modelling. So instead of using a single forecast of the future value of a currency pair, I could feed in the distribution to a monte carlo simulator or similar.</p>

<p>It doesn't need to have the right expected value, nor a correct value for volatility (or any other moment).</p>

<p>All that makes it essentially useless for trading. And that's a good thing, because no one of sound mind would give away a profitable forex forecaster: such a forecaster would immediately render itself obsolete as it entered common use.</p>

CC BY-SA 3.0
2014-12-21T05:43:21.780Z
I agree with the fact that forecasters only work if no one know about them, but I'm surprised to hear that there isn't a standard forecasting model (like Black-Scholes for derivatives). That's helpful though. Thank You!
Jason Nichols
CC BY-SA 3.0
2014-12-20T08:06:40.177Z
2014-12-20T08:06:40.177Z
410 gone
<p>Are there any <em>de facto</em> standards when publishing, or is there substantial latitude in deriving one's own methodology?</p>

<p>I understand in practice, even the <a href="http://research.stlouisfed.org/wp/2011/2011-001.pdf" rel="nofollow">St. Louis Fed</a> is gently skeptical of the validity of most technical methods, but I was wondering how academia viewed them. </p>

<p>While Dooley and Shafer (1976, 1984) were largely dismissed, Neely and Weller (1999) seemed to fare fairly well using genetic algorithms, and Markov and ARIMA methods are reviewed more or less favorably in terms of predictive value.</p>

<p>Is accuracy of prediction (and consequently profitability) even necessarily a factor in modelling for academia, or is the simplicity and explanatory value more heavily favored?$^1$
<br><br>$^1$*Note: Not sure if this merits a separate question, or can be answered as an elaboration of the primary*</p>

CC BY-SA 3.0
2014-12-20T11:05:52.007Z
Maybe too general, but try this: http://scholar.harvard.edu/files/rogoff/files/51_jie1983.pdf
Anton Tarasenko
CC BY-SA 3.0
2014-12-21T05:45:16.620Z
Certainly helpful, @Anton. Thank you. Do you know if there are any updates to this paper? Though I suppose I could run it through a citation engine to see who built off it.
Jason Nichols
CC BY-SA 3.0
2014-12-21T09:40:53.863Z
Try [papers referring](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&sciodt=0%2C5&cites=17091735262456791337&scipsc=&as_ylo=2000&as_yhi=) this one. And this paper: http://www.ssc.wisc.edu/~mchinn/CCG-P_JIMF.pdf
Anton Tarasenko
<international-economics><exchange-rates>
What are the most popular technical models used for forecasting exchange rates in international economics?
Jason Nichols
# What are the most popular technical models used for forecasting exchange rates in international economics?

(Asked by: username_0 on 2014-12-20)

Are there any *de facto* standards when publishing, or is there substantial latitude in deriving one's own methodology?

I understand in practice, even the [St. Louis Fed](http://research.stlouisfed.org/wp/2011/2011-001.pdf) is gently skeptical of the validity of most technical methods, but I was wondering how academia viewed them.

While Dooley and Shafer (1976, 1984) were largely dismissed, Neely and Weller (1999) seemed to fare fairly well using genetic algorithms, and Markov and ARIMA methods are reviewed more or less favorably in terms of predictive value.

Is accuracy of prediction (and consequently profitability) even necessarily a factor in modelling for academia, or is the simplicity and explanatory value more heavily favored?$^1$
  
  
$^1$\*Note: Not sure if this merits a separate question, or can be answered as an elaboration of the primary\*

**Tags:** international-economics, exchange-rates

> **Comments:**
> **username_1**: Maybe too general, but try this: http://scholar.harvard.edu/files/rogoff/files/51_jie1983.pdf  
> **username_0**: Certainly helpful, @Anton. Thank you. Do you know if there are any updates to this paper? Though I suppose I could run it through a citation engine to see who built off it.  
> **username_1**: Try [papers referring](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&sciodt=0%2C5&cites=17091735262456791337&scipsc=&as_ylo=2000&as_yhi=) this one. And this paper: http://www.ssc.wisc.edu/~mchinn/CCG-P_JIMF.pdf  

---

## 1 answers

### Answer by username_2 on 2014-12-20. Score: 1

Writing as an academic modeller who would use the outputs of such models, what's required (and what I look for as a reviewer) is:

* reproducibility: the algorithm is described with sufficient detail to enable someone else to reimplement it;
* validation, using data that is completely distinct from the calibration dataset; and
* that the output is a *distribution* of forecasts, not just point values.

Such a distribution can then be fed into ensemble modelling. So instead of using a single forecast of the future value of a currency pair, I could feed in the distribution to a monte carlo simulator or similar.

It doesn't need to have the right expected value, nor a correct value for volatility (or any other moment).

All that makes it essentially useless for trading. And that's a good thing, because no one of sound mind would give away a profitable forex forecaster: such a forecaster would immediately render itself obsolete as it entered common use.

> **Comments:**
> **username_0**: I agree with the fact that forecasters only work if no one know about them, but I'm surprised to hear that there isn't a standard forecasting model (like Black-Scholes for derivatives). That's helpful though. Thank You!  

---

