Journal ArticleDOI
Glossary to ARCH (GARCH)
Tim Bollerslev,Tim Bollerslev +1 more
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The literature on modeling and forecasting time-varying volatility is ripe with acronyms and abbreviations used to describe the many different parametric models that have been put forth since the original linear ARCH model introduced in the seminal Nobel Prize winning paper by Engle (1982).Abstract:
The literature on modeling and forecasting time-varying volatility is ripe with acronyms and abbreviations used to describe the many different parametric models that have been put forth since the original linear ARCH model introduced in the seminal Nobel Prize winning paper by Engle (1982). The present paper provides an easy-to-use encyclopedic reference guide to this long list of ARCH acronyms. In addition to the acronyms associated with specific parametric models, I have also included descriptions of various abbreviations associated with more general statistical procedures and ideas that figure especially prominently in the ARCH literature.read more
Citations
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Journal ArticleDOI
Volatility, correlation and tails for systemic risk measurement
TL;DR: In this paper, the authors proposed an empirical methodology to measure systemic risk of a financial institution with its contribution to the deterioration of the system capitalization that would be experienced in a crisis.
Posted Content
Market Models: A Guide to Financial Data Analysis
TL;DR: Caroline Alexander as mentioned in this paper provides an authoritative and up-to-date treatment of the use of market data to develop models for financial analysis and provides real world illustrations to motivate theoretical developments.
Book ChapterDOI
Copula Methods for Forecasting Multivariate Time Series
TL;DR: This chapter reviews the growing literature on copula-based models for economic and financial time series data, and discusses in detail methods for estimation, inference, goodness-of-fit testing, and model selection that are useful when working with these models.
Journal ArticleDOI
Improving forecasts of GARCH family models with the artificial neural networks
TL;DR: This study discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987-22.02.2008 period in Istanbul Stock Exchange and proposed ANN-APGarch model to increase the forecasting performance of APGARCH model.
Journal ArticleDOI
On loss functions and ranking forecasting performances of multivariate volatility models
Sébastien Laurent,Sébastien Laurent,Jeroen V.K. Rombouts,Francesco Violante,Francesco Violante +4 more
TL;DR: In this article, the authors propose a generalized necessary and sufficient functional form for a class of non-metric distance measures of the Bregman type which ensure consistency of the ordering when the target is observed with noise.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.
Book ChapterDOI
Time Series Analysis
TL;DR: This paper provides a concise overview of time series analysis in the time and frequency domains with lots of references for further reading.