Submit to JRFM Review for JRFM Propose a Special Issue

Journal Menu

Journal Browser

Stock Market Volatility Modelling and Forecasting

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (31 August 2018) | Viewed by 32868

Share This Special Issue

Special Issue Editor

Prof. Dr. Tai-Leung Terence Chong

E-Mail Website
Guest Editor

1. Lau Chor Tak Institute of Global Economics and Finance, The Chinese University of Hong Kong, Cheng Yu Tung Building, Shatin, NT, Hong Kong, China
2. Department of International Economics and Trade, School of Business, Nanjing University, Anzhong Building, Hankou Road #22, Gulou District, Nanjing, China
Interests: econometrics; econometric theory; banking and finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The determinants of stock return volatility have been investigated for the past two decades. The understanding of stock market volatility is crucial for asset pricing, portfolio management, trading strategy, risk management and capital setting in prudential regulation. In this Special Issue, we are open to theoretical and empirical research on stock market volatility. The deadline for papers is 31 August 2018. Please contact Terence Tai Leung Chong for details.

Prof. Dr. Tai-Leung Chong
Guest Editor

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.

Keywords

Nonlinear models for stock market volatility
GARCH-MIDAS models for stock market volatility
Effects of Macro-economic variables on stock market volatility
Market volatility by sector
Market volatility by countries
Other related topics

Related Special Issue

Empirical Finance Research in Journal of Risk and Financial Management (27 articles)

Published Papers (7 papers)

Download All Papers

Research

15 pages, 425 KiB

Open AccessArticle

Contribution to the Valuation of BRVM’s Assets: A Conditional CAPM Approach

by Mamadou Cisse, Mamadou Konte, Mohamed Toure and Smael Afolabi Assani

J. Risk Financial Manag. 2019, 12(1), 27; https://doi.org/10.3390/jrfm12010027 - 06 Feb 2019

Cited by 6 | Viewed by 3322

Abstract

The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk (

β

) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems [...] Read more.

The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk (

β

) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems to best explain the returns of the assets of the Regional Stock Exchange of West Africa (BRVM) by comparing two dynamics: one by the Kalman filter (assuming that the

β

follow a random walk) and the other by the Markov switching (MS) model (assuming that

β

varies according to regimes) for four portfolios of the BRVM. Having found a link between the beta of the market portfolio and the size criterion (measured by capitalization), the two previous models were re-estimated with the addition of the SMB (Small Minus Big) variable. The results show according to the RMSE criterion that the estimation by the Kalman filter fits better than MS, which suggests that investors cannot anticipate systematic risk because of its high volatility. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

► Show Figures

Graphical abstract

18 pages, 596 KiB

Open AccessArticle

Forecasting Volatility: Evidence from the Saudi Stock Market

by Naseem Al Rahahleh and Robert Kao

J. Risk Financial Manag. 2018, 11(4), 84; https://doi.org/10.3390/jrfm11040084 - 28 Nov 2018

Cited by 7 | Viewed by 4967

Abstract

The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

► Show Figures

Graphical abstract

17 pages, 909 KiB

Open AccessArticle

Stock Market Volatility and Trading Volume: A Special Case in Hong Kong With Stock Connect Turnover

by Brian Sing Fan Chan, Andy Cheuk Hin Cheng and Alfred Ka Chun Ma

J. Risk Financial Manag. 2018, 11(4), 76; https://doi.org/10.3390/jrfm11040076 - 31 Oct 2018

Cited by 2 | Viewed by 4249

Abstract

The cross-boundary Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect provides a special data set to study the dynamic relationships among volatility, trading volume and turnover among three stock markets, namely Shanghai, Shenzhen, and Hong Kong. We employ the Granger Causality test with the vector autoregressive model (VAR) to examine whether Stock Connect turnover contributes to future realized volatility and market volume of these three markets. Our results support the evidence of causality from Stock Connect turnover to market volatility and trading volume. The finding of this causality is consistent with the implication of the sequential information arrival model in the literature. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

► Show Figures

Figure A1

12 pages, 482 KiB

Open AccessArticle

Volatility Spillovers Arising from the Financialization of Commodities

by Wing Hong Chan, Bryce Shelton and Yan Wendy Wu

J. Risk Financial Manag. 2018, 11(4), 72; https://doi.org/10.3390/jrfm11040072 - 27 Oct 2018

Cited by 6 | Viewed by 3938

Abstract

This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors’ positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

► Show Figures

Figure 1

8 pages, 743 KiB

Open AccessArticle

Dynamic Linkages between Japan’s Foreign Exchange and Stock Markets: Response to the Brexit Referendum and the 2016 U.S. Presidential Election

by Mirzosaid Sultonov and Shahzadah Nayyar Jehan

J. Risk Financial Manag. 2018, 11(3), 34; https://doi.org/10.3390/jrfm11030034 - 28 Jun 2018

Cited by 5 | Viewed by 4098

Abstract

In this paper, we analyse the response of Japan’s foreign exchange and stock markets to the outcomes of the Brexit referendum and the U.S. presidential election. We estimate the changes in returns of the daily exchange rates of the yen (JPY), the daily closing price index of the Nikkei and the dynamic conditional correlation (DCC) coefficients between the JPY and the Nikkei caused by both events. The empirical findings showed a significant change in the daily logarithmic returns of exchange rates of the JPY and the closing price index of the Nikkei, as well as their time-varying comovement (DCC) after both events. In general, the impact of the U.S. elections on financial markets and their dynamic correlation was stronger than the impact of the Brexit referendum. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

► Show Figures

Figure 1

14 pages, 230 KiB

Open AccessArticle

Determinants of Stock Market Co-Movements between Pakistan and Asian Emerging Economies

by Muhammad Aamir and Syed Zulfiqar Ali Shah

J. Risk Financial Manag. 2018, 11(3), 32; https://doi.org/10.3390/jrfm11030032 - 21 Jun 2018

Cited by 7 | Viewed by 4645

Abstract

This study analyzes the determinants of stock market co-movement between Pakistan and Asian emerging economies for the period 2001 to 2015. Augmented Dickey and Fuller (ADF) and Philips-Perron (PP) tests are applied to check co-integration between their stock markets. Results of this study reveal that there is long-term integration between the stock market of Pakistan and the stock markets of China, India, Indonesia, Korea, Malaysia and Thailand. This study reports the driving forces of the co-movement between the Pakistan and Asian emerging markets where co-integration is found. Results of the panel data reveal that there are significant underlying forces of integration between Pakistan and each Asian emerging stock market. The findings of this study have significant implications for policy makers in Pakistan who are designing strategies for macroeconomic harmonization and stability of the country’s economy against financial shocks. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

10 pages, 1571 KiB

Open AccessArticle

Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution

by Nahida Akter and Ashadun Nobi

J. Risk Financial Manag. 2018, 11(2), 22; https://doi.org/10.3390/jrfm11020022 - 28 Apr 2018

Cited by 6 | Viewed by 6782

Abstract

In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency distribution for both daily stock returns and volatility. We also determined the beta-coefficient and correlation among the stocks for 15 years and found that, during the crisis period, the beta-coefficient between the market index and stock’s prices and correlation among stock’s prices increased remarkably and decreased during the non-crisis period. We compared the stock volatility and stock returns for specific time periods i.e., non-crisis, before crisis and during crisis year in detail and found that the distribution behaviors of stock return prices has a better long-term effect that allows predictions of near-future market behavior than realized volatility of stock returns. Our detailed statistical analysis provides a valuable guideline for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods. Full article

(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)

► Show Figures