Generalized autoregressive conditional heteroskedasticity

By Tim Bollerslev. 1986

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Citations in the corpus

Financial Time Series Prediction Using Deep Learning (2017, methodology): “Other common AR schemes is the ARCH (Autoregressive Conditional Heteroskedasticity), GARCH [4] (Generalized ARCH) and the ARIMA algorithms, and are often applied to ﬁnancial series forecasting.”
A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data (2017, methodology): “While others proposed methods based on the time series models such as the ARIMA (Autoregressive Moving Average) model [13]–[16] and the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model [17]–[19].”
Textual Sentiment in Finance: A Survey of Methods and Models (2014, neutral): “(2008) Sinha (2010) Carretta et al. (2011) Engelberg et al.”
Extreme Value Theory and Extremely Large Electricity Price Changes (2005, methodology): “By combining this AR model with the simplest possible GARCH model (GARCH(1,1)), that explicitly models the conditional volatility as a function of past conditional volatilities and returns (Engle, 1982 and Bollerslev, 1986), we hope to capture the most important dependencies in the return series.”
Predicting Risk from Financial Reports with Regression (2009, methodology): “We tested a state-of-theart model known as GARCH(1, 1) (Engle, 1982; Bollerslev, 1986) and found that it was no stronger than our historical volatility baseline on this sample.”
Forecasting value‐at‐risk with a parsimonious portfolio spillover GARCH (PS‐GARCH) model (2008, methodology): “The Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986) have subsequently led to a family of autoregressive conditional volatility models.”
Bayesian Forecasting of Value at Risk and Expected Shortfall Using Adaptive Importance Sampling (2010, unknown): “We consider the GARCH model (Engle (1982), Bollerslev (1986)) with Student-t innovations:16 yt = µ + ut (19) ut = εt(%ht) 1/2 (20) εt ∼ Student-t(ν) (21) % ≡ ν − 2 ν (22) ht = α0 + α1u 2 t−1 + βht−1 (23) where Student-t(ν) is the standard Student-t distribution with ν degrees of freedom, with…”
Energy markets volatility modelling using GARCH (2014, methodology): “Since the development of the ARCH and GARCH models by Engle (1982) and Bollerslev (1986), respectively, a significant body of literature has focused on using these to model the volatility of energy commodity prices.”
Forecasting Value-at-Risk under Different Distributional Assumptions (2016, methodology): “The ability to account for volatility clustering is one of the key strengths of the ARCH modelling approach developed in Engle [1] and extended in Bollerslev [2].”
Statistics of Extremes (2004, background): “Particularly popular in finance are the auto-regressive conditionally heteroscedastic (ARCH) process (Engle 1982) and its numerous ramifications, in particular, generalized ARCH or GARCH (Bollerslev 1986).”
Stochastic volatility models: Conditional normality versus heavy tailed distributions (2000, methodology): “It represents an alternative to the autoregressive conditionally heteroskedastic (ARCH) model of Engle (1982) or Bollerslev's (1986) generalized ARCH (GARCH) model.”