Article2012

SEMIPARAMETRIC VECTOR MEM

Fabrizio Cipollini · Robert F. Engle · Giampiero Maria Gallo

57 citations0/0 files storedJournal of Applied Econometrics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Risk and Volatility Modeling Stochastic processes and financial applications Complex Systems and Time Series Analysis

Abstract

SUMMARY Financial time series are often non‐negative‐valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element‐by‐element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d. innovation process) is a suitable strategy. Its parameters can be estimated by generalized method of moments, bypassing the problem of specifying a multivariate distribution for the errors. Simulated results show the gains in efficiency relative to an equation‐by‐equation approach. A vMEM on several measures of volatility justifies a joint approach revealing full interdependence. Copyright © 2012 John Wiley & Sons, Ltd.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 57 citations exist upstream.

References

1986Generalized autoregressive conditional heteroskedasticity 2003Modeling and Forecasting Realized Volatility 2008A Simple Approximate Long-Memory Model of Realized Volatility 2005Quantitative Risk Management: Concepts, Techniques, and Tools 1980The Extreme Value Method for Estimating the Variance of the Rate of Return 2006Quantitative Risk Management: Concepts, Techniques, and Tools 1998Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data 2007Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

Cited by

2018Volatility spillovers among the U.S. and Asian stock markets: A comparison between the periods of Asian currency crisis and subprime credit crisis 2020Realized volatility forecasting: Robustness to measurement errors 2009A Model for Multivariate Non-Negative Valued Processes in Financial Econometrics

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.