#GARCH
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paperposterbot.bsky.social
arXiv📈🤖
Bounded GARCH Processes
By Barreto-Souza
We propose a novel class of Bounded GARCH (B-GARCH) processes designed to characterise the conditional heteroskedasticity of continuous time series constrained within a compact interval. While standard GARCH specifications are tailored for R-valued data, many empirical series, such as realised correlation, unemployment rates, expenditure shares, and relative humidity, are bounded. Fitting a traditional GARCH model to such data creates a severe support mismatch, frequently generating predictions and density forecasts that breach the natural boundaries of the data (boundary violation). Furthermore, conventional approaches utilising non-linear link transformations onto the real line might induce interpretability issues and estimation biases. To address these limitations, our proposed B-GARCH framework ensures a proper predictive distribution and an equation for the dynamic conditional variance with a parameter constraint that strictly preserves the bounded support of the entire process almost surely. We establish the fundamental probabilistic properties of the process, proving strict stationarity and ergodicity, and address parameter estimation via conditional maximum likelihood. The asymptotic consistency and normality of the estimators are rigorously derived, and their finite-sample performance is validated through Monte Carlo simulations. An empirical application to the monthly realised correlation between the Vanguard Real Estate ETF (VNQ) and the iShares 20+ Year Treasury Bond ETF (TLT) demonstrates that B-GARCH models effectively capture volatility clustering while completely eliminating boundary violation issue that plagues traditional GARCH models.
November 6, 2026 at 11:21 AM
arxiv-stat-me.bsky.social
Wagner Barreto-Souza
Bounded GARCH Processes
https://arxiv.org/abs/2611.08158
November 6, 2026 at 6:35 AM
statme-bot.bsky.social
Wagner Barreto-Souza: Bounded GARCH Processes https://arxiv.org/abs/2611.08158 https://arxiv.org/pdf/2611.08158 https://arxiv.org/html/2611.08158
November 6, 2026 at 7:03 AM
statml-bot.bsky.social
Ruizhi Deng, Guohao Shen, Ngai Hang Chan: Nonparametric GARCH: An Iterated Neural Estimator https://arxiv.org/abs/2611.04157 https://arxiv.org/pdf/2611.04157 https://arxiv.org/html/2611.04157
November 4, 2026 at 7:03 AM
paperposterbot.bsky.social
arXiv📈🤖
Nonparametric GARCH: An Iterated Neural Estimator
By Deng, Shen, Chan
We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved conditional variances. Under mild conditions, we establish finite-sample bounds for the time-averaged in-sample variance-path error and show that the estimator is minimax rate-optimal up to logarithmic factors. Under stationarity and mixing conditions, we establish the same rate for recursion-map estimation and derive finite-sample bounds for out-of-sample variance forecasts and expected excess Gaussian quasi-likelihood (QLIKE) loss. The convergence rates depend on the true lag orders and remain unchanged when the fitted orders are overspecified. Simulation studies illustrate the effectiveness of our estimators under nonlinear and time-varying volatility dynamics. Real-data applications suggest that our neural methods perform competitively and can incorporate option-price and high-frequency information.
November 4, 2026 at 10:55 PM
📰 **GARCH**

📰 Source: Stonks (Stocks) Go Up on Neural Market Trends
🔗 Link: https://neuralmarkettrends.com/tags/garch/
GARCH
Exploring the Systems That Help Us Endure and Evolve.
neuralmarkettrends.com
November 1, 2026 at 3:14 AM
cranupdates.bsky.social
New on CRAN: GEVStableGarch (2.0.0). View at https://CRAN.R-project.org/package=GEVStableGarch
GEVStableGarch: ARMA-GARCH/APARCH Models with Stable, GEV and GAt Innovations
Simulation, estimation and forecasting of autoregressive moving average (ARMA) models with generalized autoregressive conditional heteroskedasticity (GARCH) or asymmetric power ARCH (APARCH, with ARCH the autoregressive conditional heteroskedasticity model) errors, with stable (S0 parametrization), generalized extreme value (GEV) and generalized asymmetric t (GAt) innovations. The innovation distribution is a pluggable object, so user defined densities get the same fitting, simulation, stationarity and forecasting machinery.
CRAN.R-project.org
November 4, 2026 at 1:00 AM
random-repec-paper.bsky.social
Outlier Detection in GARCH Models by Jurgen A. Doornik & Marius Ooms (2005)
IDEAS/RePEc link
to RePEc:nuf:econwp:0524
ideas.repec.org
November 1, 2026 at 1:50 AM
📰 **Silver Prices Forecast — 4 Weeks Ahead**

Autogenerated silver price and volatility forecast — ensemble of AutoARIMA, AutoETS, Theta and LightGBM, with a GARCH(1,1) volatility outlook.

📰 Source: Stonks (Stocks) Go Up on Neural Market Trends
🔗 Link […]
Original post on igeek.gamer-geek-news.com
igeek.gamer-geek-news.com
November 2, 2026 at 12:34 PM
📰 **HPE Options Wheel: Selling-Premium Analysis**

A quantitative read on wheeling options in HPE: GARCH volatility term-structure forecast, regime tagging, the variance-risk-premium sell signal, a cash-secured put ladder, and walk-forward backtest...

📰 Source: Stonks (Stocks) Go Up on Neural […]
Original post on igeek.gamer-geek-news.com
igeek.gamer-geek-news.com
November 2, 2026 at 12:24 PM
paperposterbot.bsky.social
arXiv📈🤖
Identification through Heteroskedasticity in One Lemma: Partial Identification, an Eigenvalue Construction, and a Caution on Rank Tests
By Milunovich
This teaching note unifies known results on identification through heteroskedasticity, under volatility regimes, GARCH, stochastic volatility and covariate-driven variances, in one lemma with a short linear-algebra proof. A shock is identified up to sign and labelling if and only if no other shock shares its variance path, and shocks that share a path are identified up to a rotation. The rows of the structural coefficient matrix solve one generalised eigenvalue problem that nests Rigobon's (2003) two-regime solution. Rank tests on second moments of the errors count at most linearly independent centred variance paths, not identified shocks. A simulation illustrates each point.
November 2, 2026 at 11:56 PM
random-repec-paper.bsky.social
Velocity And The Variability Of Money Growth: Evidence From A Varma, Garch-M Model by Serletis, Apostolos & Shahmoradi, Akbar (2006)
IDEAS/RePEc link
to RePEc:cup:macdyn:v:10:y:2006:i:05:p:652-666_05
ideas.repec.org
October 30, 2026 at 10:50 PM
📰 **Gold Prices Forecast — 4 Weeks Ahead**

Autogenerated gold price and volatility forecast — ensemble of AutoARIMA, AutoETS, Theta and LightGBM, with a GARCH(1,1) volatility outlook.

📰 Source: Stonks (Stocks) Go Up on Neural Market Trends
🔗 Link […]
Original post on igeek.gamer-geek-news.com
igeek.gamer-geek-news.com
October 31, 2026 at 12:16 PM
📰 **HPE Options Wheel: Selling-Premium Analysis**

A quantitative read on wheeling options in HPE: GARCH volatility term-structure forecast, regime tagging, the variance-risk-premium sell signal, a cash-secured put ladder, and walk-forward backtest...

📰 Source: Stonks (Stocks) Go Up on Neural […]
Original post on igeek.gamer-geek-news.com
igeek.gamer-geek-news.com
October 31, 2026 at 12:19 PM
econem-bot.bsky.social
Jonas Fiedler Frederiksen, Frederik Vilandt: Smooth Threshold GARCH: Quasi Maximum Likelihood Estimation and Empirical Evidence https://arxiv.org/abs/2610.40047 https://arxiv.org/pdf/2610.40047 https://arxiv.org/html/2610.40047
October 30, 2026 at 6:44 AM
paperposterbot.bsky.social
arXiv📈🤖
Identification through Heteroskedasticity in One Lemma: Partial Identification, an Eigenvalue Construction, and a Caution on Rank Tests
By Milunovich
This teaching note unifies known results on identification through heteroskedasticity, under volatility regimes, GARCH, stochastic volatility and covariate-driven variances, in one lemma with a short linear-algebra proof. A shock is identified up to sign and labelling if and only if no other shock shares its variance path, and shocks that share a path are identified up to a rotation. The rows of the structural coefficient matrix solve one generalised eigenvalue problem that nests Rigobon's (2003) two-regime solution. Rank tests on second moments of the errors count at most linearly independent centred variance paths, not identified shocks. A simulation illustrates each point.
November 2, 2026 at 11:10 AM
gj2blu.bsky.social
Puty’s poodle puppet (who hates our freedom) setting us, the U.S., up for the big fall … ‘guess democracy doesn’t align with greedy, fascist oily-garch goals. Fuck Jeff Roberts, P2025, Muskkk, Zuck, and the Heritage Foundation, and all these selfish, hateful, hurtful pricks … May each rot in prison
October 30, 2026 at 3:49 AM
econem-bot.bsky.social
Muyi Li, Yajia Chen, Yuqing Xu, Feiyu Jiang: A Semiparametric Real-Time GARCH Model with Nonstationary Variance https://arxiv.org/abs/2610.36739 https://arxiv.org/pdf/2610.36739 https://arxiv.org/html/2610.36739
October 28, 2026 at 6:45 AM
statap-bot.bsky.social
Zhihong Yin, Zhiming Li: Analysis of Uncertain GARCH Model with the application of crude oil future prices https://arxiv.org/abs/2610.37071 https://arxiv.org/pdf/2610.37071 https://arxiv.org/html/2610.37071
October 28, 2026 at 7:02 AM
paperposterbot.bsky.social
arXiv📈🤖
Smooth Threshold GARCH: Quasi Maximum Likelihood Estimation and Empirical Evidence
By Frederiksen, Vilandt
It is well established that the volatility of daily stock returns responds asymmetrically to news, as described by the news impact curve (NIC), which relates volatility to the previous return. Empirical studies suggest that (i) volatility reacts more strongly to negative returns than to positive returns of the same magnitude, (ii) the point of asymmetry, where the NIC attains its minimum, is positive, and (iii) returns above this point often do not increase volatility at all. The popular GJR and A-GARCH models each rule out one of these features. We study the Smooth Threshold GARCH (STGARCH) model, which nests both and allows for (i)-(iii) by combining an estimated threshold, the point of asymmetry, with separate slopes on either side of it. Although the likelihood is not twice continuously differentiable in the threshold, we show that the quasi maximum likelihood estimator is consistent and converges at the usual $\sqrt{n}$-rate, whether or not (iii) holds. If (iii) does not hold, the estimator is asymptotically normal, while if it holds, the slope above the threshold is zero, a value on the boundary of the parameter space, and the limit is non-Gaussian. In that case, the $t$-statistic for (iii) converges to the positive part of a standard normal variable, and the $t$-test of the GJR model remains valid. These results allow us to test the GJR and A-GARCH models, as well as (i)-(iii), within the STGARCH using standard normal critical values. For daily returns on five major stock indices, we reject the GJR model for all five and the A-GARCH model for three, suggesting that the STGARCH improves upon both. Volatility is lowest after returns 0.3-0.6 percentage points above their conditional mean, in line with (i) and (ii). Finally, the hypothesis in (iii) is rejected for two of the five indices.
October 30, 2026 at 11:05 AM
📰 **Silver Prices Forecast — 4 Weeks Ahead**

Autogenerated silver price and volatility forecast — ensemble of AutoARIMA, AutoETS, Theta and LightGBM, with a GARCH(1,1) volatility outlook.

📰 Source: Stonks (Stocks) Go Up on Neural Market Trends
🔗 Link […]
Original post on igeek.gamer-geek-news.com
igeek.gamer-geek-news.com
October 27, 2026 at 6:46 PM
dscottrobinson.bsky.social
youtu.be/BH6vwZwgnqc?...
The Legend of Lizzie Borden Song
YouTube video by Garch the Great
youtu.be
October 25, 2026 at 10:34 PM
rfontaine18.bsky.social
He is the criminal behind the criminal in the White House, the principal “Garch” who leads the others by being the junk yard dog of the rich willing to do things others find distasteful, like putting your servants in deportation cages.
October 25, 2026 at 3:30 PM
📰 **Silver Prices Forecast — 4 Weeks Ahead**

Autogenerated silver price and volatility forecast — ensemble of AutoARIMA, AutoETS, Theta and LightGBM, with a GARCH(1,1) volatility outlook.

📰 Source: Stonks (Stocks) Go Up on Neural Market Trends
🔗 Link […]
Original post on igeek.gamer-geek-news.com
igeek.gamer-geek-news.com
October 26, 2026 at 12:37 AM
gj2blu.bsky.social
It’s the last, desperate gasps of Putin’s P2025 Oily-garch poodle puppet handlers before the 03nov2026 reckoning. Gawd willing, a Democratic majority will bring Puty’s puppet to heel, and with it the first steps on the long fucking return …
October 24, 2026 at 2:39 AM