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Essays on Models with Mixed Frequency Data and Time Varying Parameters.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays on Models with Mixed Frequency Data and Time Varying Parameters./
Author:	Ghalayini, Aya.
Description:	1 online resource (181 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29805345click for full text (PQDT)
ISBN:	9798352981528

Essays on Models with Mixed Frequency Data and Time Varying Parameters.
Ghalayini, Aya.

Essays on Models with Mixed Frequency Data and Time Varying Parameters. - 1 online resource (181 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--Lancaster University (United Kingdom), 2022.

Includes bibliographical references

This thesis focuses on two statistical challenges in time-series modelling. The first is when variables' observations are available at different frequencies. The second is when the coefficients of a model are time-varying with stochastic volatility. The impact of these challenges and the value of the suggested remedies are assessed in empirical financial-economic applications.In addressing the first statistical challenge, disaggregation from the low- to the high-frequency domain is one of the methods that has long been used in several pieces of literature. The first chapter evaluates the existing disaggregation methods with thorough comparisons to provide comprehensive guidance for an empirical user. The second chapter builds on these results to examine the value-added in forecasting the volatility of financial stock prices by incorporating information from variables with mixed frequencies such as market sentiment indicators, economic variables, and activity measures. A representative factor(s) of all potential predictors from both frequencies results in significant forecast gains in predicting long-term financial volatility even during the 2007-08 financial crisis.The third chapter proposes a state-space model to incorporate features of time-varying coefficients to reflect the dynamic relationship between the dependent and the explanatory variables. Mainly, the model consists of two hierarchical states. First, the time-varying coefficients follow an autoregressive (AR) process with heteroskedastic innovations. Second, the log-transformation of the conditional variance of these innovations is also modelled as an AR process. In an empirical study, we utilize the proposed methodology to forecast the volatility of financial stock prices. We find that the proposed features consistently and significantly enhance the forecasting accuracy compared to a benchmark model and its existing variants.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352981528Subjects--Topical Terms:

517247
Statistics.
Index Terms--Genre/Form:

542853
Electronic books.

Essays on Models with Mixed Frequency Data and Time Varying Parameters.
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100 1 $a Ghalayini, Aya. $3 3698007
245 1 0 $a Essays on Models with Mixed Frequency Data and Time Varying Parameters.
264 0 $c 2022
300 $a 1 online resource (181 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
500 $a Advisor: Izzeldin, Marwan; Tsionas, Mike.
502 $a Thesis (Ph.D.)--Lancaster University (United Kingdom), 2022.
504 $a Includes bibliographical references
520 $a This thesis focuses on two statistical challenges in time-series modelling. The first is when variables' observations are available at different frequencies. The second is when the coefficients of a model are time-varying with stochastic volatility. The impact of these challenges and the value of the suggested remedies are assessed in empirical financial-economic applications.In addressing the first statistical challenge, disaggregation from the low- to the high-frequency domain is one of the methods that has long been used in several pieces of literature. The first chapter evaluates the existing disaggregation methods with thorough comparisons to provide comprehensive guidance for an empirical user. The second chapter builds on these results to examine the value-added in forecasting the volatility of financial stock prices by incorporating information from variables with mixed frequencies such as market sentiment indicators, economic variables, and activity measures. A representative factor(s) of all potential predictors from both frequencies results in significant forecast gains in predicting long-term financial volatility even during the 2007-08 financial crisis.The third chapter proposes a state-space model to incorporate features of time-varying coefficients to reflect the dynamic relationship between the dependent and the explanatory variables. Mainly, the model consists of two hierarchical states. First, the time-varying coefficients follow an autoregressive (AR) process with heteroskedastic innovations. Second, the log-transformation of the conditional variance of these innovations is also modelled as an AR process. In an empirical study, we utilize the proposed methodology to forecast the volatility of financial stock prices. We find that the proposed features consistently and significantly enhance the forecasting accuracy compared to a benchmark model and its existing variants.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Time series. $3 3561811
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Lancaster University (United Kingdom). $3 1294170
773 0 $t Dissertations Abstracts International $g 84-05B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29805345 $z click for full text (PQDT)