Navigating Economic Volatility: Predictive Models and Risk Analysis of Business Cycles

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: 31 December 2024 | Viewed by 3963

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Department of Transportation, Logistics, and Finance College of Business, North Dakota State University, Fargo, ND 58108, USA
Interests: risk management and insurance; mathematical optimization; business cycle prediction; tail risk analysis; moment problems; portfolio management
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Special Issue Information

Dear Colleagues,

Understanding and predicting business cycles are crucial for risk assessment and financial management for both public and private sectors. As the world is facing more post-pandemic uncertainties, including inflation, government deficits, stagnant housing markets, and more catastrophic events driven by global warming, the prediction of business cycles and the assessment of associated risks have become more indispensable. This Special Issue aims to collate research that advances our understanding of business cycle dynamics, predictive models, and the corresponding risks that affect various sectors (including finance, risk management and insurance, investment, etc.).

Original research or practitioner papers may explore the following topics:

  • Advanced models for business cycles forecasting;
  • Quantitative and qualitative risk assessments at different phases of business cycles;
  • The role of fiscal and monetary policy in mitigating risks caused by business cycles;
  • The impact of business cycles on risk management strategies;
  • The interaction between international economic activities and domestic business cycles.

We hope submissions in the Special Issue advance theoretical modelling, present empirical evidence, and provide constructive insights to professionals and policymakers.

Dr. Ruilin Tian
Guest Editor

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Keywords

  • business cycle forecasting
  • risk assessment
  • economic volatility
  • financial management
  • predictive modelling

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Published Papers (3 papers)

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Research

18 pages, 623 KiB  
Article
Liquidity Spillover between Exchange-Traded Funds: Variations across News Regimes
by Yang Liu and Yongchen Zhao
J. Risk Financial Manag. 2024, 17(9), 391; https://doi.org/10.3390/jrfm17090391 - 4 Sep 2024
Viewed by 460
Abstract
Understanding liquidity and liquidity risk is essential for effective risk management. We investigate liquidity spillover effects among ETFs that track the S&P sectors. In particular, using COVID-related news shocks as a natural experiment, we estimate the direction and magnitude of two-way net spillovers [...] Read more.
Understanding liquidity and liquidity risk is essential for effective risk management. We investigate liquidity spillover effects among ETFs that track the S&P sectors. In particular, using COVID-related news shocks as a natural experiment, we estimate the direction and magnitude of two-way net spillovers and their asymmetry across good and bad news regimes, where liquidity is measured by the daily quoted bid–ask spread and the Amihud illiquidity ratio. Our results confirm the liquidity links amongst ETFs and suggest that liquidity spillovers are more pronounced during bad news periods compared to good news periods. In addition, we document the variations in the results obtained using the bid–ask spread and the Amihud ratio, which provide insights into different dimensions of liquidity and liquidity risk, including volatility and trading volume. Full article
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11 pages, 632 KiB  
Article
Patterns in the Chaos: The Moving Hurst Indicator and Its Role in Indian Market Volatility
by Param Shah, Ankush Raje and Jigarkumar Shah
J. Risk Financial Manag. 2024, 17(9), 390; https://doi.org/10.3390/jrfm17090390 - 3 Sep 2024
Viewed by 1234
Abstract
Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for [...] Read more.
Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for improved accuracy. Specifically, we introduce a novel trading strategy based on a technical indicator, Moving Hurst (MH). MH utilizes the Hurst exponent which characterizes the chaotic properties of time series. We hypothesize and then prove empirically that MH outperforms traditional indicators like Moving Averages (MA) in analyzing Indian equity indices and capturing profitable trading opportunities while mitigating the impact of volatility. Full article
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20 pages, 1452 KiB  
Article
Volatility Persistence and Spillover Effects of Indian Market in the Global Economy: A Pre- and Post-Pandemic Analysis Using VAR-BEKK-GARCH Model
by Narayana Maharana, Ashok Kumar Panigrahi and Suman Kalyan Chaudhury
J. Risk Financial Manag. 2024, 17(7), 294; https://doi.org/10.3390/jrfm17070294 - 10 Jul 2024
Cited by 1 | Viewed by 1835
Abstract
This study examines how the COVID-19 pandemic impacted stock market volatility and interconnectedness between India and other selected global economies. The analysis, using data from 2016 to 2024, reveals a substantial rise in volatility within both the Indian market and those of several [...] Read more.
This study examines how the COVID-19 pandemic impacted stock market volatility and interconnectedness between India and other selected global economies. The analysis, using data from 2016 to 2024, reveals a substantial rise in volatility within both the Indian market and those of several other countries after the pandemic. Interestingly, the volatility transmission patterns also changed. While the Indian market’s volatility significantly influenced Brazil, China, and Mexico throughout the entire period, the influence of the US market became negligible post-pandemic. In contrast, Russia exhibited a weak but statistically significant impact on India’s volatility only after the pandemic. These findings highlight the lasting impact of the pandemic on global financial markets and emphasize the need for investors and policymakers to adapt. By understanding these new dynamics, investors can make more informed decisions, and policymakers can develop stronger risk management strategies and international coordination during periods of increased volatility. This study offers valuable insights for navigating the current financial landscape and the interconnectedness of emerging economies. Full article
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