Special Issue "Stock Market Volatility Modelling and Forecasting"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (31 August 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Tai-Leung Terence Chong

Lau Chor Tak Institute of Global Economics and Finance, The Chinese University of Hong Kong, Cheng Yu Tung Building, Shatin, NT, Hong Kong, China and the Department of International Economics and Trade, School of Business, Nanjing University, Anzhong Building, Hankou Road #22, Gulou District, Nanjing, Jiangsu Province, China
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Phone: +852 2609 8193
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Interests: econometrics; econometric theory; banking and finance

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

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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

Published Papers (6 papers)

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Research

Open AccessArticle Forecasting Volatility: Evidence from the Saudi Stock Market
J. Risk Financial Manag. 2018, 11(4), 84; https://doi.org/10.3390/jrfm11040084
Received: 24 October 2018 / Revised: 19 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
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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
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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)
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Graphical abstract

Open AccessArticle Stock Market Volatility and Trading Volume: A Special Case in Hong Kong With Stock Connect Turnover
J. Risk Financial Manag. 2018, 11(4), 76; https://doi.org/10.3390/jrfm11040076
Received: 5 September 2018 / Revised: 18 October 2018 / Accepted: 25 October 2018 / Published: 31 October 2018
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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
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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)
Open AccessArticle Volatility Spillovers Arising from the Financialization of Commodities
J. Risk Financial Manag. 2018, 11(4), 72; https://doi.org/10.3390/jrfm11040072
Received: 7 September 2018 / Revised: 17 October 2018 / Accepted: 25 October 2018 / Published: 27 October 2018
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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
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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)
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Open AccessArticle Dynamic Linkages between Japan’s Foreign Exchange and Stock Markets: Response to the Brexit Referendum and the 2016 U.S. Presidential Election
J. Risk Financial Manag. 2018, 11(3), 34; https://doi.org/10.3390/jrfm11030034
Received: 20 June 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 28 June 2018
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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
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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)
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Open AccessArticle Determinants of Stock Market Co-Movements between Pakistan and Asian Emerging Economies
J. Risk Financial Manag. 2018, 11(3), 32; https://doi.org/10.3390/jrfm11030032
Received: 29 May 2018 / Revised: 7 June 2018 / Accepted: 8 June 2018 / Published: 21 June 2018
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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
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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)
Open AccessArticle Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution
J. Risk Financial Manag. 2018, 11(2), 22; https://doi.org/10.3390/jrfm11020022
Received: 2 April 2018 / Revised: 20 April 2018 / Accepted: 26 April 2018 / Published: 28 April 2018
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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
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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)
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