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Macroeconometrics and Time Series Analysis
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Macroeconometrics and Time Series Analysis

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 7538

Special Issue Editors

Dr. Yanlin Shi

E-Mail Website
Guest Editor
Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, NSW 2109, Australia
Interests: garch model; chinese stock markets; volatility modelling
Dr. Wenying Yao

E-Mail Website
Guest Editor
Department of Economics, Faculty of Business and Law, Deakin University, Burwood, VIC 3125, Australia
Interests: time series analysis; high frequency financial econometrics; econometric theory; macroeconometrics; empirical finance; applied econometrics

Special Issue Information

Dear Colleagues,

In the past few decades, thanks to the popularity of techniques such as simultaneous equations and Vector Autoregressive (VAR) models, macroeconometric analysis has become a standard tool to analyse the economies and policies of nations. This Special Issue welcomes contributions pertaining to theoretical and/or applied issues in time series analysis, especially as they are related to novel macroeconometric and financial applications, broadly defined. We are particularly interested in papers that investigate recent macro empirical issues or develop methods related to the proposition, computation, estimation, and forecasting of econometric models. Macro methods developed in econometrics and other relevant fields have been the backbone used to resolve essential issues in international finance, macroeconomics, and risk management, among a wide range of other areas. The classic tools, such as the VAR model, are still popular among recent studies, and new ones are constantly being developed to analyse new research questions or revisit important empirical topics. The aim of this Special Issue is to contribute to what has been done empirically/theoretically and/or offer new perspectives on such studies and related ones. Some typical topics include but limited to:

  • Macroeconomics
  • Macroeconometrics
  • International finance
  • International trading
  • Financial time series
  • Point and density forecasts
  • Computation
  • Simulation
  • Estimation
  • Dynamic risk and quantile models
  • Realized measures
  • Macro business analytics

Dr. Yanlin Shi
Dr. Wenying Yao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

13 pages, 373 KiB  
Article
Understanding the Interaction of Chinese Fiscal and Monetary Policy
by Zehua Luan, Xiangyu Man and Xuan Zhou
J. Risk Financial Manag. 2021, 14(9), 416; https://doi.org/10.3390/jrfm14090416 - 3 Sep 2021
Viewed by 1686
Abstract
Interaction of fiscal and monetary policy is crucial for macroeconomic stability, especially for an economy with downward pressure as well as a tightened space for macro policy, like China. In this paper, we use a time-varying-parameter (TVP-VAR) model to study Chinese fiscal–monetary interaction [...] Read more.
Interaction of fiscal and monetary policy is crucial for macroeconomic stability, especially for an economy with downward pressure as well as a tightened space for macro policy, like China. In this paper, we use a time-varying-parameter (TVP-VAR) model to study Chinese fiscal–monetary interaction and divide it into three periods. We claim that China went through a monetary dominant regime from 1996Q to 2017Q4 since the response of CPI to a fiscal expansion was negative in the short run and about zero in the long run, while the monetary expansion had positive effects on CPI. During this period, the response of government spending and money supply to each other’s shock had the same sign, indicating that the two policies acted as complements. However, we argue that 2008Q4 was a turning point that divided this period into two different periods. The response level of M2 growth rate to a fiscal expansion kept rising from 1996Q1 to 2008Q4, indicating the central bank’s increasingly active cooperation with fiscal policy, while it decreased from 2009Q1 to 2017Q4. Since 2018Q1, the economy has been going through a fiscal dominant regime in that the response of GDP growth rate and CPI to the fiscal expansion has sharply increased. We also argue that the relative change of the role between the two policies should be mainly attributed to the variation in the fiscal authority’s characteristics because fiscal response to a monetary shock has remained at a similar level the whole time, even if there have been changes in the characteristics of the central bank. Full article
(This article belongs to the Special Issue Macroeconometrics and Time Series Analysis)
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13 pages, 503 KiB  
Article
Forecasting High-Dimensional Financial Functional Time Series: An Application to Constituent Stocks in Dow Jones Index
by Chen Tang and Yanlin Shi
J. Risk Financial Manag. 2021, 14(8), 343; https://doi.org/10.3390/jrfm14080343 - 23 Jul 2021
Cited by 4 | Viewed by 2248
Abstract
Financial data (e.g., intraday share prices) are recorded almost continuously and thus take the form of a series of curves over the trading days. Those sequentially collected curves can be viewed as functional time series. When we have a large number of highly [...] Read more.
Financial data (e.g., intraday share prices) are recorded almost continuously and thus take the form of a series of curves over the trading days. Those sequentially collected curves can be viewed as functional time series. When we have a large number of highly correlated shares, their intraday prices can be viewed as high-dimensional functional time series (HDFTS). In this paper, we propose a new approach to forecasting multiple financial functional time series that are highly correlated. The difficulty of forecasting high-dimensional functional time series lies in the “curse of dimensionality.” What complicates this problem is modeling the autocorrelation in the price curves and the comovement of multiple share prices simultaneously. To address these issues, we apply a matrix factor model to reduce the dimension. The matrix structure is maintained, as information contains in rows and columns of a matrix are interrelated. An application to the constituent stocks in the Dow Jones index shows that our approach can improve both dimension reduction and forecasting results when compared with various existing methods. Full article
(This article belongs to the Special Issue Macroeconometrics and Time Series Analysis)
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11 pages, 7139 KiB  
Article
Multi-Factorized Semi-Covariance of Stock Markets and Gold Price
by Yun Shi, Lin Yang, Mei Huang and Jun Steed Huang
J. Risk Financial Manag. 2021, 14(4), 172; https://doi.org/10.3390/jrfm14040172 - 9 Apr 2021
Cited by 2 | Viewed by 2599
Abstract
Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this [...] Read more.
Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take London, Tokyo, Shanghai, Toronto, and Nasdaq as the representative examples. Full article
(This article belongs to the Special Issue Macroeconometrics and Time Series Analysis)
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