<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng’s algorithm is carried out via numerical simulation in the case of simulated T-GARCH models and two real series, namely German and US stock indices. Simulations show that the NPCPM algorithm is superior to ICSS because is not over-sensitive either to heavy tails of market returns or to their serial dependence. Some signals generated by ICSS are falsely classified as structural breaks in volatility, while Cheng’s technique works well only when a single break occurs.</p></abstract>

We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng’s algorithm is carried out via numerical simulation in the case of simulated T-GARCH models and two real series, namely German and US stock indices. Simulations show that the NPCPM algorithm is superior to ICSS because is not over-sensitive either to heavy tails of market returns or to their serial dependence. Some signals generated by ICSS are falsely classified as structural breaks in volatility, while Cheng’s technique works well only when a single break occurs.

Selected Techniques of Detecting Structural Breaks in Financial Volatility

Financial Internet Quarterly

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Selected Techniques of Detecting Structural Breaks in Financial Volatility"}

We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time...

Selected Techniques of Detecting Structural Breaks in Financial Volatility

Pubblicato online: 08 feb 2017

Pagine: 32 - 43

Ricevuto: 30 set 2014

Accettato: 18 mag 2015

DOI: https://doi.org/10.1515/fiqf-2016-0104

Parole chiave
volatility, structural breaks, financial time series, logarithmic returns, Threshold-GARCH model

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Selected Techniques of Detecting Structural Breaks in Financial Volatility

Pubblicato online: 08 feb 2017

Pagine: 32 - 43

Ricevuto: 30 set 2014

Accettato: 18 mag 2015

DOI: https://doi.org/10.1515/fiqf-2016-0104

Parole chiavevolatility, structural breaks, financial time series, logarithmic returns, Threshold-GARCH model

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
volatility, structural breaks, financial time series, logarithmic returns, Threshold-GARCH model