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Questions tagged [volatility-forecasting]

Volatility is is the degree of variation of a time series around its (conditional) mean. Volatility can be measured by conditional variance or conditional standard deviation. Volatility forecasting is important in finance and risk management.

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Portfolio Value at risk (VaR) with DCC Garch model in R

Hello respected members, I need your help to forecast portfolio VaR for 3 assets(returns) with the help of DCC Garch model in R. I have done the following steps as you can see from my codes also, 1) ...
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32 views

GARCH(1,1) volatility forecast looks biased, it is consistently higher than Parkinson's HL vol

I am trying to create one-step ahead forecasts for the S&P500 using a GARCH(1,1) model. I am using the rugarch package in R. As you can see, the forecasted points are consistently higher than the ...
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Fitting a GARCH model and forecast using validation set approach In R

I have seperated the data into training and testing data. Then I fitted this simple garch model for training data as follows,(using rugarch package) ...
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Forecasting a DCCGARCH model estimated by the ccgarch package

How can one predict a dcc-garch model in R using parameters estimated from dcc.estimation from the package ccgarch? MWExample: ...
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1answer
44 views

Estimation - Taylor's Stochastic Volatility Model

I want to estimate Taylor's stochastic volatility model (fit on stock data). Is there any package in R ? As far as I know, there is not a "standard" procedure in Eviews. Even a free-distributed ...
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29 views

Mean Equation Specification using rugarch in R

I fitted a GARCH(1,1) to my 4511 return observations using rugarch in R. Question: Which of these two mean equation specifications does ...
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1answer
29 views

How can i choose the optimal lag in GARCH-MIDAS?

I have to choose individual GARCH-MIDAS models for some variables. But the BIC value continues to decrease as I increase the lag (its even the case for k=70 and more which is unrealistic) so the BIC, ...
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10 views

Multivarite ARCH, which model should I simulate

I am trying to simulate the following model in MATLAB if this helps: I set the k matrix to all 1's and the a matrix is going to be various values. How could I simulate this in MATLAB. I know I ...
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28 views

Can I log-transform realized volatility in a co-integration setting

I'm writing my master's thesis and looking to see if there exists fractional co-integration between the volatility of some large stock-indices. My estimates of realized volatility are based on the ...
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How to calculate volatility and trends in a time series analysis?

I have a table with data for different groups by months and totals, each groups are of different scales where the max and min ...
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30 views

Estimating and forecasting stock and option prices with GARCH models

I am new in the field of time series. I wonder why there is not enough literature about GARCH models used to predict stock or option prices? In other words, is it reasonable to use a general ...
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Manuel estimation of a Garch(1,1) parameters using MLE vs rugarch package in R

I want to estimate parameters of a GARCH(1,1) model using rugarch package in R and manually(using maximum likelihood). Firstly, I import and transfrom the data as below(Amazon return data) ...
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Causality in variance with a BEKK model

I am using a BEKK model in the following form, $$H_t=C^\ast{C^\ast}^\prime+\sum_{i=1}^{m}{A_i\varepsilon_{t-i}\varepsilon_{t-i}A_i^\prime+\sum_{j=1}^{s}{B_jH_{t-j}B_j^\prime}}$$ I first start with a ...
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Time-series forecasting of weakly correlated, univariate series

Given an event that happens with a probability of $\lambda_1$, and another event happens with a probability of $\lambda_2$, what is the probability that they both occur? I have a dataset of ...
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1answer
29 views

Writing a VAR(1) with ARCH(1) errors as a bilinear model?

I have been reading a paper and found this quote? "Note that a linear conditional mean model with ARCH disturbances can be described by a nonlinear specification without ARCH, i.e. the bilinear ...
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43 views

AR(1)-GARCH(1,1). A bad fit with log likelihood?

Consider these two DCC models: ...
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1answer
165 views

Obtaining point forecasts from a DCC-GARCH model in rmgarch in R [closed]

I am becoming more acquainted with GARCH models in R, but I am not sure my code is right for what I am trying to do, so I would appreciate any help. Based on an xts I create using data from a csv ...
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1answer
64 views

The exponent delta of the variance recursion in a GARCH model

The function garchFit in R for GARCH modeling, among the various possibilities include a parameter "delta" described as the exponent of variance recursion. I've ...
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1answer
100 views

Initial vector $h$ in Bayesian stochastic volatility models (Jacquier, Polson and Rossi, 1994)

I was going through the paper Jacquier, Polson and Rossi (1994): Bayesian Analysis of Stochastic Volatility Models. While the model seems straight forward to implement. I'm not able to find how the ...
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1answer
98 views

Impact of window size on estimated volatility using SMA or EWMA

When calculating volatility (either using an SMA or EWMA approach), what impact does the window size have on the volatility estimate?
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2answers
63 views

How is NN architecture chosen in a post on volatility forecasting?

I came across a post about forecasting volatility with a neural network; Honchar "Neural networks for algorithmic trading. Volatility forecasting and custom loss functions". However, it is not clear ...
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136 views

EWMA using Monte-Carlo simulation

Im trying to forecast volatility using an EWMA model in python. Where i have return(t-1) and variance(t-1). n is number of days. for every Monte-carlo simulation N: t=1: Forecast the variance using: ...
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44 views

Parsimony and Box Jenkins

Suppose that you want to estimate volatility of stock returns with the arch/garch family. An important step is to estimate the mean equation. Suppose that you estimated e.g. an ARMA(5,4) model for ...
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84 views

Volatility forecast using EGARCH model

I have a question regrading EGARCH models. I am partially basing my master thesis on the methodology followed by Brandt & Jones (2012). you can find it here: https://faculty.fuqua.duke.edu/~...
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1answer
127 views

Can ARCH, GARCH, and GARCH derivative models be used outside of finance?

I'm learning about these models and seems like they can only be used in finance, and the reasoning is the assumption that variance is returns squared. Is this correct? Can ARCH/GARCH be used to model ...
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1answer
103 views

Markov Switching GARCH - Expanding or Rolling window forecasting?

When modelling volatility do people tend to use expanding or sliding windows to predict the performance of MS GARCH models?
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1answer
206 views

Why is a return process assumed to be stationary when there is volatility clustering present?

I am analysing a logarithmic returns series only to find the ADF result to signify the stationarity of the series. I understand that this is a way of differencing the original price series, however I ...
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1answer
44 views

Chances a forecasting model exceeds/deceeds a specified threshold [closed]

I am interested in determining the confidence of a forecasting model with applications to quantitative finance. I have the following multivariate data $X$: \begin{align} X(t) \sim F_{X}(t) \end{...
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1answer
49 views

Is there a way of knowing how good my volatility estimates are?

I'm trying to estimate daily volatility of an index I created. I do not wish to forecast, simply estimate the volatility for a period of about 500 days. My problem is that the sometimes, my results/...
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2answers
164 views

Newcomer question: How does the GARCH recursive formula actually work?

So, I have a some experience with standard econometrics, and I also understand GARCH's basic concept, but I can't figure out what actually goes into its model. So we have the standard GARCH(1,1) ...
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2answers
404 views

Bootstrap sample with size greater than the original sample

I want to predict future returns over a 20 days horizon using an ARMA-GARCH model fitted to my data. The goal is to estimate different risk measures like VaR or CVar. In particular say I use AR(1) ...
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1answer
183 views

Reparametrization of the GJR-GARCH(1,1) model (Asymmetric GARCH models)

Xia et al. (2017) state that the power-transformed and threshold GARCH(1,1,1) model is equivalent to the GJR-GARCH(1,1) model (see page 355 of Xia et al. (2017)). It is not clear why the first model ...
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1answer
267 views

Stochastic volatility: particle filter vs Metropolis-Hastings

In many of the papers on particle filter I've read (e.g. Douc, Moulines and Olsson, 2007), stochastic volatility is a common example to show that a newly-proposed filter is working. At the same time, ...
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1answer
416 views

What is the 1-step-ahead forecast from an ARCH(1) model?

Can anybody shortly show and explain how to do an 1-step-forward ARCH(1) forecast?
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2k views

GARCH(1,1) model with exogenous variable using STATA and EVIEWS

I want to estimate a GARCH model with an exogenous variable. Data is as below: ...
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1answer
2k views

Understanding the GARCH(1,1) model: the constant, the ARCH term and the GARCH term

I would like some help with a GARCH(1,1) volatility modeling. I am working with the assumption the volatility is the weighted sum of three factors: Long run variance + $n-1$ squared return + $n-1$ ...
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1answer
189 views

Expression for the unconditional variance in the EGARCH model

Given the EGARCH specification: $\log(\sigma_t^2)=\omega + \alpha(|z_{t-1}| + E[|z_{t-1}|]) + \gamma z_{t-1} + \beta \log(\sigma_{t-1}^2)$ Is it possible to find a closed-form solution for the ...
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1answer
40 views

Distributional forecast for $X_{t+1}$ in GARCH(1,1) with residuals student t distributed

Let's say I have time series of $n$ (simulated) data from the GARCH(1,1) process: $$X_{t+1,n} = \sigma_{t+1,n}(...)*Z_{t+1,n}$$ where $Z_{t,n}$ is iid student t-distributed with $v$ degree of freedom. ...
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1answer
560 views

GARCH estimates differ in rugarch (R) vs. EViews

I modelled a stock's volatility using the "rugarch" package in R and Eviews. The estimated model is GARCH(1,1). Data is as below: ...
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1answer
107 views

GARCH volatility modelling with external variables using R packages [closed]

I want to model stocks' volatility with GARCH based models, with external variables. Until now, I found RUGARCH package fot that purpose. However, I wonder is there any other packages for GARCH ...
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1answer
180 views

Volatility modelling with proxy variable using rugarch package in R

I want to model a stock's volatility with a proxy variable. Consider a standard GARCH(1,1) model: $$ \sigma^2_t=a+b \sigma^2_{t-1}+c r^2_{t-1}. $$ Classically, $r_{t}$ is the return of the stock at ...
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2answers
5k views

Measure of volatility for time series data?

I would like to calculate some measure of volatility or noise for stationary time series data. This can be a measure for a single time series or a relative measure comparing multiple time series ...
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1answer
297 views

Variance of random walk, mean reverting and trending series

I am reading Ernie Chan's blog post "Mean reversion, momentum, and volatility term structure". It says that To be precise, if $z$ is the log price, then volatility, sampled at intervals of $\tau$, ...
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112 views

Real using of the “Realized GARCH” for 1 minute forecast

I would like to ask someone who has an experience with the "Realized GARH" by Peter Hansen. I have 2 questions: Is there a logical purpose to use realGARCH for 1 minute forecast? How long could it ...
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1answer
43 views

How to get volatility of a stock price using Artificial Neural Networks? [closed]

I am working on how to get the volatility using ANNs. I don't know how to go about it after getting the output of a single neuron.
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1answer
214 views

ARMA-GARCH, invertibility, stationarity and insignificance

I am trying to forecast volatility out-of-sample using ARCH, GARCH, GJR and EGARCH. I used AIC to identify the ARMA and ARCH order and decided to stick with (1,1) for GARCH-type models. However, I ...
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2answers
426 views

Why replace Pearson's correlation with DCC GARCH? (non-technical)

How you would try convincing a non-technical audience that applying DCC GARCH for correlation estimation is better than Pearson's correlation? The task becomes even more challenging since, as seen in ...
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2answers
225 views

How to measure the true underlying daily volatility from daily data?

I am looking at how well GARCH, GJR-GARCH and EGARCH capture the volatility dynamics before, during and after the financial crisis. I also compute out-of-sample forecasts and examine if there is one ...
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1answer
780 views

Which one should I use for rolling forecast, dynamic or static?

I'm doing a rolling forecast using a fitted arma-garch model, but I'm confused regarding the rolling method, my window length is 1209 obs, and I roll 100 times, and each time I reset my window to ...
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3answers
513 views

Normal vs leptokurtic distribution for financial returns

Financial returns have been shown to follow leptokutotic distributions, however volatility forecasting models like EWMA and DCC-GARCH assume conditionally (dependent on time) Normal distribution for ...