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32 votes
Accepted

Why aren't econometric models used more in Quant Finance?

It's an interesting question. I particularly agree with the $\mathbb{Q}-\mathbb{P}$ dichotomy mentioned by many. I would add to the other answers that, come to think of it, the Black-Scholes ...
Quantuple's user avatar
  • 14.7k
17 votes

Why aren't econometric models used more in Quant Finance?

I think you need to differentiate between Q-quants vs P-quants. The former might not use Econometrics, but P-quants use them a lot.
Kiwiakos's user avatar
  • 4,337
12 votes

Why aren't econometric models used more in Quant Finance?

Traditional econometric (time series) models are of little or no value in forecasting market prices for purposes of "making money", i.e, generating excess return over a benchmark in an asset ...
RRL's user avatar
  • 3,680
8 votes

Why aren't econometric models used more in Quant Finance?

My answer is very much in the spirit of Kiwiakos' answer. E.g. in this paper (where I am one of the coauthors) we use VMA (vector moving average) models (in the multivariate case) and AR models in ...
Richi Wa's user avatar
  • 13.7k
8 votes

Why aren't econometric models used more in Quant Finance?

Having thought about this I think the following reason is also important and wasn't mentioned so far: When you look at the inner working of this whole class of econometric models it all boils down to ...
vonjd's user avatar
  • 27.5k
5 votes

Differencing vs Detrending financial time series

Hi: It depends on what the DGP of the original process is. Is the process trend stationary or difference stationary ? If it's trend stationary then de-trending is the way to go. If it's difference ...
mark leeds's user avatar
  • 1,112
4 votes
Accepted

Python regenerating ARMA params using statsmodels

The logic of your code is all right. However, the variance of the parameters is high because nobs=250 is relatively low. Increase ...
Elrond's user avatar
  • 390
4 votes
Accepted

Why is $Z_t$ uncorrelated with $X_{t-1}$ in $X_t=\theta X_{t-1}+Z_t$?

$E(X_{t-1}Z_t) = 0$ in the causal case $|\phi | < 1$, but not in the non-causal case $|\phi | >1$. Causal case $(|\phi| < 1)$ In this case, the unique stationary solution to the AR(1) ...
Jose Avilez's user avatar
3 votes
Accepted

How are the values of the ARMA process linked in python

First, note that $\epsilon_t \sim N(0,1)$ is a white noise process and the random variates are simulated from a standard normal distribution. Hence, it does not make sense for you to multiply ...
Pleb's user avatar
  • 4,286
2 votes
Accepted

distribution of AR, MA coefficients estimation in ARMA-GARCH models

Normally distributed and that's why the two first moments are sufficient to infer their statistical significance. Proof are rather technical (and sometimes are not specific to time-series models) and ...
Malick's user avatar
  • 2,582
2 votes

How to fit a SARIMA + GARCH in R?

While SARIMA-GARCH is not currently (October 2016) implemented in R as far as I am aware, you can deal with seasonality by including some dummy variables or Fourier terms in the conditional mean model....
Richard Hardy's user avatar
2 votes
Accepted

ARIMA model coefficients from discontinuous data series

But it is sensless to include data from night hours if I trade only during day. This kind of thinking seems to be a common beginners' fallacy in econometrics and related fields (nothing personal). ...
Richard Hardy's user avatar
2 votes

Confidence Intervals for ARMA+GARCH forecasts

How are these distributed? $\epsilon_{t+1}\sim\text{SGED}(\mu_{t+1},\sigma_{t+1},\text{skew},\text{shape})$. For a $(1-\alpha)$ level $1$-step-ahead forecast interval that is consistent with the model ...
Richard Hardy's user avatar
2 votes

Can GARCH volatility simulations generally be applied to return-modelling models?

In general, if you have a model of relation between $y$ and $x$ whereby the relation is not perfect but measured with errors: $$y_t = f(x_t) + \varepsilon_t,$$ where errors $\varepsilon$ are assumed ...
Igor Pozdeev's user avatar
2 votes

Differencing vs Detrending financial time series

Let me try to write formulae to explain the differences: When $X_t=a+b\,t + c\,\xi_t$, where $\xi_t$ is an iid centered and reduced noise (ie $\mathbb{E}\xi=0$ and $\mathbb{E}\xi^2=1$. With $X_(t+1)-...
lehalle's user avatar
  • 12.2k
2 votes

Exchange rate trend-stationarity

Both @Con and @markleeds give good advice. Please don't worry - ADF is famously headache-inducing ;-) The core problem here is that drifts and trends look horribly alike; and thus approaches like ADF ...
demully's user avatar
  • 5,071
2 votes
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Modelling Skew when using ARMA Time Series

Conceptually, if you want constant conditional skewness, you could simply choose an error distribution that is skewed for your ARMA model. ARMA only restricts the conditional mean of the time series ...
Richard Hardy's user avatar
1 vote

Should stock return series be modeled with a parametric distribution, or an autoregressive function?

Parametric distributions and autoregressive functions live in different dimensions. You cannot contrast them as you cannot contrast, say, a person's race with gender. But you can combine them, letting ...
Richard Hardy's user avatar
1 vote

Exchange rate trend-stationarity

This is done simply in R with Rob Hyndmans Forecast packages, you need to run ACF, and PACF, there is an automatic algorithm for calculating the model of best fit, which takes most of the difficulty ...
Con Fluentsy's user avatar
1 vote

What are some good models for stock price predictions?

Long story short no. Also, your question is too general in my opinion. Stock prices are not predictable according to the efficient market hypothesis. However there are many models one can try, they ...
Stelios Kounis's user avatar
1 vote

Can ARMA and GARCH models be estimated separately in ARMA/GARCH´╝č

You can combine AR(I)MA and GARCH models. For instance, a (Gaussian) ARMA(1,1)-GARCH(1,1) model would read as \begin{align*} r_t &= c + ar_{t-1} + b\epsilon_{t-1} + \epsilon_t, \\ \sigma^2_t &=...
Alex's user avatar
  • 688
1 vote

ARMA moments proof

For the first, where $|\beta| < 1.0$, you can write it using the lag operator. $x_t (1 - \beta L) = (1 + \theta L) u_t $ $X_t = \frac{(1 + \theta L) u_t}{(1- \beta L)} $ Since $|\beta| < 1.0 ...
mark leeds's user avatar
  • 1,112
1 vote

ARIMA vs ARIMA + GARCH

Without testing it is hard to know. I am assuming you are trying to predict volatility and not returns. Hansen and Lunde (2005) concluded that hardly anything beats a Garch(1,1) for a stock and an ...
phdstudent's user avatar
  • 8,381
1 vote

Joint estimation of GARCH models with ARMA terms in the conditional mean: a necessity?

You are confusing the cond. mean process and cond. variance process : the autocorrelation plot of the squared returns gives you information about the cond. variance process (not the ARMA part !) . ...
Malick's user avatar
  • 2,582
1 vote

Modeling tail data using Generalized Pareto distribution

You might be interested in this ARTICLE (published in Quantitative Finance 2016) and citations therein. The authors consider different distributions to model tails in financial time series and in ...
CFW's user avatar
  • 206
1 vote

ARIMA model coefficients from discontinuous data series

When you say discontinuous, you are referring to the clock time. So depending on your assumptions, it may not be discontinuous in trading time. For some securities that are not trading over night and ...
Will Gu's user avatar
  • 712
1 vote

Please advice free Java library for classical time series forecasting

For Java you may try: https://github.com/signaflo/java-timeseries https://github.com/signaflo/java-timeseries/wiki/The-timeseries-package https://github.com/signaflo/java-timeseries/wiki/ARIMA-...
Damianos P. Melidis's user avatar
1 vote
Accepted

Python statsmodel ARMA question

For ARIMA(2,1,4) you would need to use the ARIMA model, as described here. You would call with something like this ...
Bob Jansen's user avatar
  • 8,562

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