DYNAMIC MODELS FOR VOLATILITY AND HEAVY TAILS: WITH APPLICATIONS TO FINANCIAL AND ECONOMIC TIME SERIES, BY A. C. HARVEY. PUBLISHED BY CAMBRIDGE UNIVERSITY PRESS, 2013 NEW YORK, USA. TOTAL NUMBER OF PAGES: 261. PRICE: $36.99. ISBN: 978-1-107-63002-4

2014 ◽  
Vol 35 (2) ◽  
pp. 187-188
Author(s):  
Alastair R. Hall
Keyword(s):  
New York ◽  
Time Series ◽  
Heavy Tails ◽  
Dynamic Models ◽  
Economic Time Series ◽  
Cambridge University ◽  
Economic Time

Mathematical and Quantitative Methods: Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series

2013 ◽  
Vol 51 (4) ◽  
pp. 1190-1192
Author(s):  
Timo Teräsvirta
Keyword(s):  
Time Series ◽  
Quantitative Methods ◽  
Heavy Tails ◽  
Dynamic Models ◽  
Economic Time Series ◽  
Corpus Christi ◽  
Economic Time ◽  
Heavy Tailed ◽  
The University ◽  
Changes Over Time

Timo Terasvirta of Aarhus University reviews, “Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series” by Andrew C. Harvey. The Econlit abstract of this book begins: “Presents a theory for a class of nonlinear time series models that can deal with dynamic distributions, with an emphasis on models in which the conditional distribution of an observation may be heavy-tailed and the location and/or scale changes over time. Discusses statistical distributions and asymptotic theory; location; scale; location/scale models for nonnegative variables; dynamic kernel density estimation and time-varying quantiles; multivariate models, correlation, and association; and further directions in dynamic models. Harvey is Professor of Econometrics at the University of Cambridge and Fellow of Corpus Christi College, the Econometric Society, and the British Academy.”

scholarly journals Distribution-Based Entropy Weighting Clustering of Skewed Time Series

2021 ◽  
Author(s):  
Raffaele Mattera ◽  
MassimilIano Giacalone ◽  
Karina Gibert Oliveiras
Keyword(s):  
Time Series ◽  
Power Distribution ◽  
Heavy Tails ◽  
Financial Time Series ◽  
Parameter Estimates ◽  
Economic Time Series ◽  
Data Set ◽  
Exponential Power Distribution ◽  
Economic Time ◽  
Exponential Power

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series have motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skew Exponential Power Distribution (also called Skew Generalized Error Distribution) ensures a unified and general framework for clustering possibly skewed time series. This paper develop a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine all the parameter estimates to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is showed by means of an application to financial time series, showing also how the obtained clusters can be used to form portfolio of stocks.

scholarly journals Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 959
Author(s):  
Raffaele Mattera ◽  
Massimiliano Giacalone ◽  
Karina Gibert
Keyword(s):  
Time Series ◽  
Power Distribution ◽  
Heavy Tails ◽  
Financial Time Series ◽  
Economic Time Series ◽  
Data Set ◽  
Exponential Power Distribution ◽  
Economic Time ◽  
Heavy Tailed ◽  
Exponential Power

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

Sign in / Sign up

Export Citation Format

Share Document