Questions tagged [arima]
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80
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eGARCH(1,1) model evaluation (R). How to assess model integrity?
I am using GARCH modelling for my bachelor thesis in Economics. I am entirely new to the concept, and have only been looking into these kind of models for about a week now. I am trying to do a ...
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YUIMA: Drift and diffusion parameters must be different?
I am currently working with the Yuima package and trying the estimate the parameters of a CARMA(p,q) model to real data. Using the eacf function of the TSA package a ARMA (2,1) process is recommended ...
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Inverse differencing in continuous time
I want to fit a continuous time ARMA (CARMA) model to traffic data $T_t$. After removing trend and seasonality I need first order differencing to obtain stationarity. Then I fit a CARMA model (yuima ...
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Weak stationarity of continuous ARMA process from Brockwell
I am currently working on Brockwell "Levy-driven CARMA processes" (2001) and I am stuck in the introduction.
So we have a continuous AR process (CAR(p))
\begin{align*}
X_t=e^{At}X_0+\...
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Does cointegration test of exogenous variable with Y variable make sense when doing ARIMAX/SARIMAX?
The cointegration test between two time series variable is generally relevant from my understanding when you are performing a regression model. In terms of ARIMA model the approach is straightforward ...
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In copula modeling for time series data, why do we need to fit ARIMA/GARCH and then work on standardized residulas.?
I have read that for standard copula modeling, you can get empirical cdf of data and use it for copulas. But for time series data, we must first fit ARIMA/GARCH, get standardized residuals, and only ...
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State space equation of CARMA(p,q) processes
Thanks for visting my question:)
I am currently working on Carma(p,q) processes and do not understand how to derive the state equation. So the CARMA(p,q) process is defined by:
for $p>q$
the ...
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Are ARMA-GARCH-type models suitable for monthly data?
I understand that ARMA-GARCH models and their variations are usually applied to daily time series. While I know that such models can be also estimated on monthly data, I have seen few applications in ...
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Why is $Z_t$ uncorrelated with $X_{t-1}$ in $X_t=\theta X_{t-1}+Z_t$?
In a solution to the problem below, the teaching assistant solves it by calculating $\mathbb{E}[X_t^2]$ and ends up with also having to calculate $\mathbb{E}[X_{t-1}Z_t]$ after expanding the square. ...
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Do I use % return, log return or diff of prices to plot ACF?
I am reading a book on time series. To make a non-stationary series stationary, sometimes we need to difference the series. When it comes to finance, prices are non-stationary. Many authors fit ARMA ...
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305
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How are the values of the ARMA process linked in python
In the code below, you can see that 'ret' is an ARMA process, and I am trying to see how the ret[0], etc... ret3, ret4, etc. are linked to each other, and although I know the formula for the ARMA ...
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73
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Price Prediction Intervals from Forecasted Returns (ARIMA)
I have successfully fit an ARIMA model to a time series of the daily returns of power futures prices. The question I have is: How can I create a prediction interval for the prices? Or, alternatively, ...
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What is the Id in the ARIMA model in Notes on financial risk of Privault?
I hope this is the right place to ask this question. I am studying the time series from Privault's Notes on Financial Risks. In the ARIMA model part I can't understand what is "I_d", it is ...
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Electricity Futures Risk Premiums With ARIMA
I am attempting to model long-term electricity prices using today's futures prices. Unlike most futures, electricity is delivered over a period of time (usually a month), rather than at a point in ...
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ARMAX model with rolling window for predicting inflation
First of all, similar questions like mine are answered on this forum but I never quite saw an answer to this specific question. I'm trying to predict inflation by using an AR model with exogenous ...
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Modelling Skew when using ARMA Time Series
I am currently modelling financial time series via ARMA processes, but I have reason to believe that in addition to significant autocorrelation, the time series also exhibit skewness. Is there a way ...
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Should stock return series be modeled with a parametric distribution, or an autoregressive function? [closed]
If I have prior knowledg that a stock return series follows a parametric distribution, such as a Student t-distribution with 4 degrees of freedom, without actively looking for prior knowledge of ...
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How to model financial HFT time-series data with multi scale autocorrelation
I work with tick level time-series univariate prices data. Tick level means that there are hundreds to thousands observations per second. The observations are timestamped, so one can use both wall ...
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Exploding forecast when increasing sample size in ARIMA in R/Python
I am fitting ARFIMA - eGARCH to time-series log returns in Python using R Rugarch package. The sample size is 2000, and I am doing 1-step ahead forecast. The following is the output
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117
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Inter-temporal structural stability of stock markets
For my bachelor thesis I am trying to determine structural stability of some stock market in the following way:
Identify an ARMA model for the whole sample
Split the sample in two parts, and estimate ...
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78
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Fitting a Spread into ARIMA AR(1) process
I'm a newbie to econometrics. I've simply ran a regression and have coefficient values of the variables. I'm running a regression for a crypto data, and I've gotten the Spread of the variables. To ...
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214
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Exchange rate trend-stationarity
I am kinda new to time-series analysis, I want model CEE (EUR/HUF, EUR/PLN, EUR/CZK, EUR/CHF) exchange rates with ARIMA. I understand that according to Box-Jenkins modeling, I should first check if my ...
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Conditional and unconditional variance, autocovariance and autocorrelation of an ARMA process
Given an ARMA(1,1) process $x_t = a + bx_{t-1} + \varepsilon_t + \theta\varepsilon_{t-1}$, how can we
find the conditional variance, i.e. $Var_{t-1}(x_t)$,
find the unconditional variance, i.e. $Var(...
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Is non-linear correlation problematic in financial time series prediction?
Many traditional finance models assume linear relationships between variables and features. Aren't linear correlations/covariances unable to capture financial processes empirically since they actually ...
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What are some good models for stock price predictions?
For the fitting and forecasting of time-series data on stock price, the most frequent model I have heard of is ARIMA. ARIMA is actually conducting a regression of stock prices and residuals of stock ...
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The residuals of GARCH model reject Engle’s Test despite large parameters
I'm trying to build a model to predict the volatility for a financial asset with ARIMA-GARCH model. (I use log returns as data)
I fit my ARIMA model with AIC and I did Engle’s Test to ensure there is ...
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Can ARMA and GARCH models be estimated separately in ARMA/GARCH？
Can I use the residuals of the ARMA model to build a GARCH model(with Zero mean)?
If so, does this mean that this GARCH model(with Zero mean) has no effect on ARMA's estimates. For example, if I want ...
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Fitting a non-stationary GARCH model
I'm very new to financial time series. I have a dataset containing the daily simple returns of the Dow Jones Industrial Average and I want to model a (univariate) GARCH model for the daily logreturns. ...
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Expected Shortfall for ARMA-GARCH Model
I need to find an analytical solution for the 99% confidence expected shortfall (CVaR) for a long position of 100 dollars at time $t$ for an asset with returns modeled by an ARMA(1,1)-GARCH(1,1) model ...
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Differencing vs Detrending financial time series
I'm quite newbie to time series analysis and I have to understand what's the difference between differencing time series (i.e considering $Y_t= X_t-X_{t-1}$) and detrending (using linear regression ...
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ARMA moments proof
Consider a standard ARMA(1,1) process such as
$$x_t - \beta x_{t-1} = \theta u_{t-1} + u_t$$
where $u_t$ is i.i.d. $u_t \sim N(0,\sigma^2)$. I know how to derive mean and variance with stationary ...
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Combining SARIMA and GARCH model for prediction in python
I need to understand the concept of combining (S)ARIMA and (G)ARCH model for the predicting time-series data.
I understand that after fitting the arima model ...
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Confidence Intervals for ARMA+GARCH forecasts
I have fitted an ARMA(1,1)+GARCH(1,1) model to my logreturns series. When it comes to my standarized error's distribution however, I have opted for a Skewed Generalized Error Distribution, because of ...
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Problem with Hurst exponent estimation for ARFIMA models
guys.
I try to realize my ARFIMA model identification script in R. I try to find the best method for unbiased Hurst exponent estimation (fractional difference parameter could be found as Hurst - 0.5) ...
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MLE error in R: initial value in 'vmmin' is not finite
I am trying to fit an ARIMA(1,1)-GARCH(1,1) model. I changed the starting values a lot but still its returning the same error.
Below is my code which contains two functions ...
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What kind of ARMA-GARCH model is that?
My question is what kind of ARMA-GARCH model is the following equation and how to specify it in rugarch R module:
$$r_{t+1}- r_t = \alpha_0 + \alpha_1r_t+\...
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Error distribution assumption in a simple ARIMA model
why in an ARIMA-GARCH structure I have to assume an error distribution to run the estimation while in a simple ARIMA model it is not required?
Thank you
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Can GARCH volatility simulations generally be applied to return-modelling models?
This may be a naive question, but I still hope some discussion can elucidate a (so far) totally nebulous point for me.
I've recently learned that GARCH models can give one simulations of ...
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ARIMA vs ARIMA + GARCH [closed]
If an ARIMA model converges quickly, would using GARCH improve the forecast performance? By improve I mean provide longer time periods for forecasts. Basically trying to forecast returns.
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900
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Interpreting ACF/PACF of return series
Researching a return series on some currency pairs I grabbed 2 years worth of daily data and got to work trying to fit an ARIMA/GARCH model to it.
Fitting the (log) return series:
...
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ARMA-GARCH Forecasting [closed]
I want to forecast a differenced time series of an Index using the combined ARMA-GARCH model (because I want to forecast the mean and not the variance). My model is a ARMA(2,2)-GARCH(1,1) model. So ...
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Are some stock prices not ARIMA(0,1,0) processes?
I am studying stock prices. Let Pt be price of stock at time t.
While Pt is non stationary, the return, rt=log(Pt/Pt-1) is stationary.
However, when I study on rt, I decide on an ARMA(0,1) without ...
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Joint estimation of GARCH models with ARMA terms in the conditional mean: a necessity?
Supposing I am using the following models to forecast conditional volatility of index returns, whereby In-sample data is 1996-2007 and out of sample data is 2007-2012, using GARCH type models.
I have ...
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VARMA GARCH modelling in R
I want to simulate a VARMA-GARCH process in R. Unfortunately, I found no package to help me with that.
I tried modelling the MGARCH part on itw own and combine it with the VARMA simulation using MTS ...
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1
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Modeling tail data using Generalized Pareto distribution
I just estimated a ARMA(1,1)+GARCH(1,1)+Threshold order(1) equation for time series of stock prices.
Now I'm going to estimate the residuals' marginal ...
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ARIMA model coefficients from discontinuous data series
Stock prices are not stationary processes during all week or all day. For example EURGBP has low variability at night in Europe but during working hours is changing much more dynamic because of market ...
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Please advice free Java library for classical time series forecasting
I've got an ARIMA model (with a GARCH model for variance estimation) and parameters estimated in Matlab for my set of data. Now I need to use this model in my Java based application for making ...
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ARIMA prediction for currencies
I was browsing TradingEconomics.com and I came across their forecast models which immediately captivated my interest. They describe them as "projected using an autoregressive integrated moving average ...
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191
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x13 Arima analysis with negative values
I'm running x13 Arima analysis on a US GDP series to get the "trend" component.
...
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143
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Estimating time-varying tail dependence for Archimedean copulas
Patton (2006) defines the upper tail dependence coefficient for a time-varying bivariate SJC copula as
$$\tau^u_t=\Lambda \left(\omega_u + \beta_u \tau^u_{t-1}+\alpha_u \frac{1}{10}\sum^{10}_{i=1}|u_{...
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