Volatility Models Applied to Geophysics, Financial Market Data and Other Disciplines II

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 6388

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Department of Mathematical Sciences, University of Texas at El Paso, 500 University Ave., Bell Hall 124, El Paso, TX 79968-0514, USA
Interests: stochastic processes; nonlinear partial differential equations; mathematical finance; mathematical physics; numerical methods; geophysics
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Special Issue Information

Dear Colleagues, 

Over the past few decades, several volatility models have been developed to describe phenomena arising in geophysics, financial markets and other disciplines. Many known methods, both deterministic and stochastic, have been used to study the volatility structures of datasets arising in geophysical and financial time series. Many of these deterministic and stochastic models provide interesting, potentially useful tools for modeling and describing volatility structures in these time series.

In this Special Issue, we invite and welcome commentaries, review, expository and original research articles dealing with the recent advances in the theory and applications of volatility models to data sets arising in geophysics, financial markets data and other disciplines.

Prof. Dr. Maria C. Mariani
Guest Editor

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Keywords

  • stochastic volatility models
  • deterministic volatility models
  • geophysics
  • financial market data
  • time series analysis
  • high-frequency data

Published Papers (6 papers)

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Research

15 pages, 289 KiB  
Article
Research on the Correlation between the Exchange Rate of Offshore RMB and the Stock Index Futures
by Zhi Yang, Zhao Fei and Jing Wang
Mathematics 2024, 12(5), 695; https://doi.org/10.3390/math12050695 - 27 Feb 2024
Viewed by 400
Abstract
The offshore RMB exchange rate is affected by the supply and demand relationship in the international market, investor sentiment, market liquidity, and other factors, while the onshore RMB exchange rate is mainly affected by government regulation and intervention. Therefore, the offshore RMB exchange [...] Read more.
The offshore RMB exchange rate is affected by the supply and demand relationship in the international market, investor sentiment, market liquidity, and other factors, while the onshore RMB exchange rate is mainly affected by government regulation and intervention. Therefore, the offshore RMB exchange rate may be a better reflection of the market’s macroeconomic expectations and risk appetite for China. Stock index futures are mainly affected by macroeconomic factors, so studying the correlation between the offshore RMB exchange rate and stock index futures is helpful for risk management, hedging, and price discovery. In this study, we selected the offshore RMB exchange rate, the volume of stock index futures, and the absolute rate of return as variables of investor sentiment. Through the Granger causality test, impulse response function, and variance decomposition, we studied the correlation between the rate of return of stock index futures and the rate of return of the offshore RMB exchange rate. Furthermore, we constructed a GARCH conditional volatility model. It was concluded that the trading volume and the absolute rate of return of stock index futures could explain the price fluctuations of stock index futures very well. A change in the offshore RMB exchange rate yield causes a change in the yield of stock index futures. Policymakers need to pay close attention to changes in the offshore RMB exchange rate in order to better grasp market trends and manage risks accordingly. Full article
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22 pages, 7424 KiB  
Article
Impact of Geopolitical Risk on G7 Financial Markets: A Comparative Wavelet Analysis between 2014 and 2022
by Oana Panazan and Catalin Gheorghe
Mathematics 2024, 12(3), 370; https://doi.org/10.3390/math12030370 - 24 Jan 2024
Viewed by 773
Abstract
This study investigates co-movements between the GPR generated by the Crimean Peninsula’s annexation in 2014, the Russia–Ukraine war in 2022, and the volatility of stock markets in the G7 states. Using wavelet analysis, concentrated co-movement was found for all indices in both periods. [...] Read more.
This study investigates co-movements between the GPR generated by the Crimean Peninsula’s annexation in 2014, the Russia–Ukraine war in 2022, and the volatility of stock markets in the G7 states. Using wavelet analysis, concentrated co-movement was found for all indices in both periods. Contrary to the general perception, we find that the G7 financial market response in 2014 was robust. Using a time-varying parameter vector autoregression (TVP-VAR) test, we found a larger reaction in the amplitude of the G7 financial markets in 2022 than in 2014. The financial markets in France, Germany, and the UK showed a similar reaction in 2022. We have identified some common aspects, even if the political and military contexts of the two studied events were completely different. Our findings offer new and interesting implications for understanding how geopolitical risk affects financial assets for market participants with multiple investment horizons and strategies. Full article
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17 pages, 7130 KiB  
Article
Connectedness between Pakistan’s Stock Markets with Global Factors: An Application of Quantile VAR Network Model
by Syeda Beena Zaidi, Abidullah Khan, Shabeer Khan, Mohd Ziaur Rehman, Wadi B. Alonazi and Abul Ala Noman
Mathematics 2023, 11(19), 4177; https://doi.org/10.3390/math11194177 - 06 Oct 2023
Viewed by 961
Abstract
This study aims to provide important insights regarding the integrated structure of global factors and Pakistan’s leading sector-level indices by estimating the dynamic network and pairwise connectedness of the global crude oil index, MSCI index, European economic policy uncertainty index, and important sector-level [...] Read more.
This study aims to provide important insights regarding the integrated structure of global factors and Pakistan’s leading sector-level indices by estimating the dynamic network and pairwise connectedness of the global crude oil index, MSCI index, European economic policy uncertainty index, and important sector-level indices of Pakistan based on QVAR using daily frequency over the period of 20 years from 2002 to 2022. The findings demonstrate high interconnectedness among global factors indices and Pakistan’s leading sector-level indices. The results of net directional connectivity showed that the EPEUI, WTI, and MSCI indices are the “net receivers” of volatility spillover. At the same time, the financial and energy sectors are the “net transmitter” of shocks. Connectedness is high amid financial upheavals. The research findings provide crucial insights for policymakers, businesses, portfolio managers, and investors. Full article
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19 pages, 600 KiB  
Article
Volatility Contagion from Bulk Shipping and Petrochemical Industries to Oil Futures Market during the Economic Uncertainty
by Arthur Jin Lin
Mathematics 2023, 11(17), 3737; https://doi.org/10.3390/math11173737 - 30 Aug 2023
Cited by 1 | Viewed by 689
Abstract
The purposes of the research have evidenced the spillover effects of oil-related factors in the oil market and the leading indexes of petrochemical commodities and the bulk shipping markets. The research gap was fitted and explored the effects associated with leading indexes for [...] Read more.
The purposes of the research have evidenced the spillover effects of oil-related factors in the oil market and the leading indexes of petrochemical commodities and the bulk shipping markets. The research gap was fitted and explored the effects associated with leading indexes for the shipping and petrochemical markets on the oil market during the US-China trade war, which is seldom bridged with significant relations in the history of oil. The scope of data for the period from 4 January 2016, through 31 August 2022, were analyzed using a generalized autoregressive conditional heteroskedastic mixed data sampling model as methodology of mix frequency to examine volatility spillover of four research hypotheses from the bulk shipping and petrochemical markets to the oil market. Main contributions revealed that spillover from the bulk shipping and petrochemical commodity markets transmitted significant volatility to West Texas Intermediate (WTI) oil returns after the US-China trade war began, a trend that has continued throughout the COVID-19 era until Ukraine–Russia war. These rare events indicate that the realized volatility derived from these market variables can be used to track the more significant contagions on WTI futures volatility in this empirical research than the weak relation in past studies. Full article
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15 pages, 4056 KiB  
Article
Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter
by Shafiqah Azman, Dharini Pathmanathan and Aerambamoorthy Thavaneswaran
Mathematics 2022, 10(17), 3190; https://doi.org/10.3390/math10173190 - 04 Sep 2022
Viewed by 1787
Abstract
During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility [...] Read more.
During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models. Full article
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27 pages, 400 KiB  
Article
Estimation of Endogenous Volatility Models with Exponential Trends
by Juan R. A. Bobenrieth, Eugenio S. A. Bobenrieth, Andrés F. Villegas and Brian D. Wright
Mathematics 2022, 10(15), 2647; https://doi.org/10.3390/math10152647 - 28 Jul 2022
Cited by 1 | Viewed by 1145
Abstract
Nonlinearities, exponential trends, and Euler equations are three key features of standard dynamic volatility models of speculation, economic growth, or macroeconomic fluctuations with occasionally binding constraints and endogenous state-dependent volatility. A natural way to estimate a model with all such three features could [...] Read more.
Nonlinearities, exponential trends, and Euler equations are three key features of standard dynamic volatility models of speculation, economic growth, or macroeconomic fluctuations with occasionally binding constraints and endogenous state-dependent volatility. A natural way to estimate a model with all such three features could be to use the observed nonstationary data in a single step without preliminary linearization, log-linearization, or preliminary detrending. Adoption of this natural strategy confronts a serious challenge that has been neither articulated nor solved: a dichotomy in the empirical model implied by the Euler equation. This leads to a discontinuity in the regression in the limit, rendering the approaches employed in available proofs of consistency inapplicable. We characterize the problem and develop a novel method of proof of consistency and asymptotic normality. Our methodological contribution establishes a foundation for consistent estimation and hypothesis testing of nonstationary models without resorting to preliminary detrending, an a priori assumption that any trend is exactly zero, linearization, or other restrictions on the model. Full article
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