31
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 ...
- 14.5k
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.
- 4,307
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 ...
- 3,595
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 ...
- 27.3k
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 ...
- 13.6k
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 ...
- 1,082
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 ...
- 390
4
votes
Filtering out AR(1) effects before using stochastic volatility model
Even though it's a straightforward extension, it took me a while (a year? yikes!); but now you can easily incorporate Bayesian ar(1) (or more generally, Bayesian regression) in joint estimation by ...
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) ...
- 225
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 ...
- 4,186
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....
- 2,658
2
votes
Accepted
Is it too important that my residuals be normal? I am Using an ARMA/GARCH model
To get it out the way: you cannot ask 'what model is better' without a reference to what its use is. Do you want to test for the mean or the AR parameter to trade it? Do you want to calculate VaR? Do ...
- 4,307
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 ...
- 2,552
2
votes
Accepted
Estimating Carma(2,1) parameters (using yuima package)
It is just a problem of how you pass times series to yuima.
Just one more thing, if you want to estimate a CARMA driven by a Brownian motion, it is better to work with log-prices instead of prices. ...
- 36
2
votes
Accepted
Define polynomials of an ARMA process
There is no particular issue with your polynomials. However if you really want them to both start with a 1, you can apply a change of variable by defining :
\begin{equation}Y_t = -\frac{1}{4}X_t\end{...
- 279
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). ...
- 2,658
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
...
- 2,658
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 ...
- 932
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)-...
- 11.5k
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 ...
- 5,041
2
votes
Accepted
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 ...
- 2,658
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 ...
- 1,082
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 ...
- 8,062
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 !) . ...
- 2,552
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 ...
- 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 ...
- 702
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-...
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
...
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