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Journal articles on the topic 'Volatility dependence'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

YANG, CHUNXIA, SEN HU, BINGYING XIA, and RUI WANG. "LONG MEMORY IN STOCK MARKET VOLATILITY: THE INTERNATIONAL EVIDENCE." Modern Physics Letters B 26, no. 20 (2012): 1250128. http://dx.doi.org/10.1142/s021798491250128x.

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It is still a hot topic to catch the auto-dependence behavior of volatility. Here, based on the measurement of average volatility, under different observation window size, we investigated the dependence of successive volatility of several main stock indices and their simulated GARCH(1, 1) model, there were obvious linear auto-dependence in the logarithm of volatility under a small observation window size and nonlinear auto-dependence under a big observation. After calculating the correlation and mutual information of the logarithm of volatility for Dow Jones Industrial Average during different

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2

Guo, Mingyuan, and Xu Wang. "The dependence structure in volatility between Shanghai and Shenzhen stock market in China." China Finance Review International 6, no. 3 (2016): 264–83. http://dx.doi.org/10.1108/cfri-09-2015-0122.

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Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the a

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3

Lai, Wing-Choong, and Kim-Leng Goh. "Dependence Structure Between Renminbi Movements and Volatility of Foreign Exchange Rate Returns." China Report 57, no. 1 (2021): 57–78. http://dx.doi.org/10.1177/0009445520984737.

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This article investigates the linkages of the movements in Renminbi (RMB) to volatility of exchange rate returns of other currencies before and after the yuan devaluation on 11 August 2015. A comparison between the onshore Chinese yuan (CNY) and the offshore Chinese yuan (CNH) is made. Standard regression methods underestimate the tail dependence between yuan and other exchange rate volatility, as financial data are non-normally distributed, especially when extreme event occurs. We apply Gumbel copulas to capture the presence of tail dependence between RMB returns and the volatility of exchang

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4

LUONG, CHUONG, and NIKOLAI DOKUCHAEV. "MODELING DEPENDENCY OF VOLATILITY ON SAMPLING FREQUENCY VIA DELAY EQUATIONS." Annals of Financial Economics 11, no. 02 (2016): 1650007. http://dx.doi.org/10.1142/s201049521650007x.

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The paper studies the modeling of time series with the prescribed dependence of the volatility on the sampling frequency. This dependence is often observed for financial time series. We suggest to model the dependence of volatility on sampling frequency via delay equations for the underlying prices. It appears that these equations allow to model the price processes with volatility that increases when the sampling rates increase. In addition, these equations are able to model the inverse phenomena where the volatility decreases with the increase in sampling frequencies.

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Shi, Yafeng, Xiangxing Tao, Yanlong Shi, Nenghui Zhu, Tingting Ying, and Xun Peng. "Can Technical Indicators Provide Information for Future Volatility: International Evidence." Journal of Systems Science and Information 8, no. 1 (2020): 53–66. http://dx.doi.org/10.21078/jssi-2020-053-14.

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AbstractWe employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day, where the volatility is measured by daily realized volatility. Our empirical results, based on long samples of 8 well-known stock indexes, suggest that a significant and asymmetric tail dependence between the technical indicators based on moving average and the next day volatility. The level of dependence change over time in a persistent manner. And the dependence structure presents some distinct differences between emerging market indexe

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6

Martynov, Mikhail, and Olga Rozanova. "On dependence of volatility on return for stochastic volatility models." Stochastics 85, no. 5 (2012): 917–27. http://dx.doi.org/10.1080/17442508.2012.673616.

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7

Lee, Eun-Joo, Noah Klumpe, Jonathan Vlk, and Seung-Hwan Lee. "Modeling Conditional Dependence of Stock Returns Using a Copula-based GARCH Model." International Journal of Statistics and Probability 6, no. 2 (2017): 32. http://dx.doi.org/10.5539/ijsp.v6n2p32.

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Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula to overcome the limitations of traditional linear correlations. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this paper provides procedures of combining a copula with a GARCH model which leads to the c

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8

Candido Silva Filho, Osvaldo, and Flavio Augusto Ziegelmann. "Assessing some stylized facts about financial market indexes: a Markov copula approach." Journal of Economic Studies 41, no. 2 (2014): 253–71. http://dx.doi.org/10.1108/jes-06-2012-0080.

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Purpose – The aim of this paper is to measure and evaluate the relationship between returns-volatility and trading volume and returns and volatility of financial market indexes using time-varying copulas. Design/methodology/approach – The time dynamic dependence parameter is allowed to evolve according to a restricted ARMA-type equation which includes a constant term that is driven by a hidden two-state first-order Markov chain. Findings – In using this time dynamics in conjunction with non-elliptical distribution functions and tail dependence measure, the authors are allowing for (and focusin

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9

Sosa Castro, Magnolia Miriam, Christian Bucio Pacheco, and Héctor Eduardo Díaz Rodríguez. "Extreme volatility dependence in exchange rates." Cuadernos de Economía 40, no. 82 (2021): 25–56. http://dx.doi.org/10.15446/cuadecon.v40n82.79400.

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This paper aims to analyse asymmetric volatility dependence in the exchange rate between the British Pound, Japanese Yen, Euro, and Mexican Peso compared to the U.S. dollar during different periods of turmoil and calm sub-periods between (1994-2018). GARCH and TARCH models are employed to model conditional

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10

Kalnina, Ilze, and Kokouvi Tewou. "Cross-sectional dependence in idiosyncratic volatility." Journal of Econometrics 249 (May 2025): 106003. https://doi.org/10.1016/j.jeconom.2025.106003.

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11

Rømer, Sigurd Emil, and Rolf Poulsen. "How Does the Volatility of Volatility Depend on Volatility?" Risks 8, no. 2 (2020): 59. http://dx.doi.org/10.3390/risks8020059.

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We investigate the state dependence of the variance of the instantaneous variance of the S&P 500 index empirically. Time-series analysis of realized variance over a 20-year period shows strong evidence of an elasticity of variance of the variance parameter close to that of a log-normal model, albeit with an empirical autocorrelation function that one-factor diffusion models fail to capture at horizons above a few weeks. When studying option market behavior (in-sample pricing as well as out-of-sample pricing and hedging over the period 2004–2019), messages are mixed, but systematic, model-w

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12

Zhu, Haoren, Shih-Yang Liu, Pengfei Zhao, Yingying Chen, and Dik Lun Lee. "Forecasting Asset Dependencies to Reduce Portfolio Risk." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4397–404. http://dx.doi.org/10.1609/aaai.v36i4.20361.

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Financial assets exhibit dependence structures, i.e., movements of their prices or returns show various correlations. Knowledge of assets’ price dependencies can help investors to create a diversified portfolio, aiming to reduce portfolio risk due to the high volatility of the financial market. Since asset dependency changes with time in complex patterns, asset dependency forecast is an essential problem in finance. In this paper, we organize pairwise assets dependencies in an Asset Dependency Matrix (ADM) and formulate the problem of assets dependencies forecast to predict the future ADM give

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13

Solibakke, Per Bjarte. "Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities." Journal of Risk and Financial Management 14, no. 11 (2021): 510. http://dx.doi.org/10.3390/jrfm14110510.

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This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The

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14

Karmann, Alexander, and Rodrigo Herrera. "Volatility Contagion in the Asian Crisis: New Evidence of Volatility Tail Dependence." Review of Development Economics 18, no. 2 (2014): 354–71. http://dx.doi.org/10.1111/rode.12089.

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15

Kalli, Maria, and Jim Griffin. "Flexible Modeling of Dependence in Volatility Processes." Journal of Business & Economic Statistics 33, no. 1 (2015): 102–13. http://dx.doi.org/10.1080/07350015.2014.925457.

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16

Dario, Alan De Genaro. "Apreçamento de Ativos Referenciados em Volatilidade." Brazilian Review of Finance 4, no. 2 (2006): 203. http://dx.doi.org/10.12660/rbfin.v4n2.2006.1162.

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Volatility swaps are contingent claims on future realized volatility. Variance swaps are similar instruments on future realized variance, the square of future realized volatility. Unlike a plain vanilla option, whose volatility exposure is contaminated by its asset price dependence, volatility and variance swaps provide a pure exposure to volatility alone. This article discusses the risk-neutral valuation of volatility and variance swaps based on the framework outlined in the Heston (1993) stochastic volatility model. Additionally, the Heston (1993) model is calibrated for foreign currency opt

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17

CHONG, TERENCE TAI LEUNG, CHENXI LU, and WING HONG CHAN. "LONG RANGE DEPENDENCE AND STRUCTURAL BREAKS IN THE GOLD MARKETS." Singapore Economic Review 65, no. 02 (2017): 257–73. http://dx.doi.org/10.1142/s0217590817500096.

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The price of gold and its determining factors have been studied extensively in the literature. However, there is a lack of research on structural break in the long memory of the gold markets. This paper examines the long memory properties of gold prices. In particular, it attempts to test the stability of the long range dependence of gold returns and volatility. The results suggest that long memory exists in gold returns and volatility, and that the volatility of daily gold futures returns can be characterized by a hyperbolic decaying long memory process. Three episodes of structural breaks ar

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18

Bucci, Andrea. "Realized Volatility Forecasting with Neural Networks." Journal of Financial Econometrics 18, no. 3 (2020): 502–31. http://dx.doi.org/10.1093/jjfinec/nbaa008.

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Abstract In the last few decades, a broad strand of literature in finance has implemented artificial neural networks as a forecasting method. The major advantage of this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating process. This makes it suitable for forecasting time series which exhibit long-memory and nonlinear dependencies, like conditional volatility. In this article, the predictive performance of feed-forward and recurrent neural networks (RNNs) was compared, particularly focusing on the recently develop

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19

Bruzgė, Rasa, Jurgita Černevičienė, Alfreda Šapkauskienė, Aida Mačerinskienė, Saulius Masteika, and Kęstutis Driaunys. "STYLIZED FACTS, VOLATILITY DYNAMICS AND RISK MEASURES OF CRYPTOCURRENCIES." Journal of Business Economics and Management 24, no. 3 (2023): 527–50. http://dx.doi.org/10.3846/jbem.2023.19118.

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This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are

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20

Vo, Long Hai, and Duc Hong Vo. "Modelling Australian Dollar Volatility at Multiple Horizons with High-Frequency Data." Risks 8, no. 3 (2020): 89. http://dx.doi.org/10.3390/risks8030089.

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Long-range dependency of the volatility of exchange-rate time series plays a crucial role in the evaluation of exchange-rate risks, in particular for the commodity currencies. The Australian dollar is currently holding the fifth rank in the global top 10 most frequently traded currencies. The popularity of the Aussie dollar among currency traders belongs to the so-called three G’s—Geology, Geography and Government policy. The Australian economy is largely driven by commodities. The strength of the Australian dollar is counter-cyclical relative to other currencies and ties proximately to the ge

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21

MENDES, BEATRIZ V. M., and EDUARDO F. L. DE MELO. "LOCAL ESTIMATION OF DYNAMIC COPULA MODELS." International Journal of Theoretical and Applied Finance 13, no. 02 (2010): 241–58. http://dx.doi.org/10.1142/s0219024910005759.

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It has been empirically verified that the strength of dependence in stock markets usually rises with volatility. In this paper we exploit this stylized fact combined with local maximum likelihood estimation of copula models to analyze the dynamic joint behavior of series of financial log returns. Explanatory variables based on the estimated GARCH volatilities are considered as potential regressors for explaining the dynamics in the copula parameters. The proposed model can assess and discriminate how much of the strength of dependence is due just to the time-varying volatility. The final local

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22

Zhu, Liang, Christine Lim, and Jianlun Zhang. "Prediction of risk: Decoding the Serial Dependence of Stock Return Volatility with Copula." Journal of Hospitality & Tourism Research 45, no. 1 (2020): 6–27. http://dx.doi.org/10.1177/1096348020919490.

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In this article, a new approach based on the copula theory is employed in the analysis and forecasting of hospitality and tourism-related stock return volatility (HTSRV). The application of copula-based models for univariate time series is state-of the-art methodologies with new perspectives for economic analysis in tourism and hospitality. This flexible method provides numerous functions for serial dependence specification of volatility series. Eight hospitality–tourism stocks are analyzed for their serial dependence structures to provide insight for forecasting stock return volatility. While

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23

MA, CHAOQUN, HONGQUAN LI, LIN ZOU, and ZHIJIAN WU. "LONG-TERM MEMORY IN EMERGING MARKETS: EVIDENCE FROM THE CHINESE STOCK MARKET." International Journal of Information Technology & Decision Making 05, no. 03 (2006): 495–501. http://dx.doi.org/10.1142/s0219622006002088.

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The notion of long-term memory has received considerable attention in empirical finance. This paper makes two main contributions. First one is, the paper provides evidence of long-term memory dynamics in the equity market of China. An analysis of market patterns in the Chinese market (a typical emerging market) instead of US market (a developed market) will be meaningful because little research on the behaviors of emerging markets has been carried out previously. Second one is, we present a comprehensive research on the long-term memory characteristics in the Chinese stock market returns as we

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24

Pratt, Kerri A., Lindsay E. Hatch, and Kimberly A. Prather. "Seasonal Volatility Dependence of Ambient Particle Phase Amines." Environmental Science & Technology 43, no. 14 (2009): 5276–81. http://dx.doi.org/10.1021/es803189n.

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25

Edwards, Sebastian, and Raul Susmel. "Volatility dependence and contagion in emerging equity markets." Journal of Development Economics 66, no. 2 (2001): 505–32. http://dx.doi.org/10.1016/s0304-3878(01)00172-9.

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26

Miles, William. "Long-Range Dependence in U.S. Home Price Volatility." Journal of Real Estate Finance and Economics 42, no. 3 (2009): 329–47. http://dx.doi.org/10.1007/s11146-009-9204-0.

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27

Denneberg, Dieter, and Nikola Leufer. "Dual volatility and dependence parameters and the copula." International Journal of Approximate Reasoning 48, no. 3 (2008): 697–708. http://dx.doi.org/10.1016/j.ijar.2007.03.014.

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28

Kim, Jong-Min, and S. Y. Hwang. "The copula directional dependence by stochastic volatility models." Communications in Statistics - Simulation and Computation 48, no. 4 (2018): 1153–75. http://dx.doi.org/10.1080/03610918.2017.1406512.

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29

Kim, Jong-Min, and Hojin Jung. "Can asymmetric conditional volatility imply asymmetric tail dependence?" Economic Modelling 64 (August 2017): 409–18. http://dx.doi.org/10.1016/j.econmod.2017.02.002.

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., Ghulam Mohey-ud-din, and Muhammad Wasif Siddiqui. "Determinants of GDP Fluctuations in Selected South Asian Countries: A Macro-Panel Study." Pakistan Development Review 55, no. 4I-II (2016): 483–97. http://dx.doi.org/10.30541/v55i4i-iipp.483-497.

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Now a days, the issue of volatility in GDP is becoming a fundamental development concern due to the undeniable connections between volatility and lack of development. In addition, the recognition of the negative link between short-term fluctuations and long-term growth not only signifies the importance of exploring this link but also stresses the importance of studying the determinants of the GDP fluctuations so that the efforts to manage these fluctuations can be made. Therefore, keeping in view, the importance of studying the factor causing fluctuations in GDP, the present study aims at expl

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Zheng, Mo, Han-Suck Song, and Jian Liang. "Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models." Buildings 14, no. 1 (2024): 182. http://dx.doi.org/10.3390/buildings14010182.

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In this paper, we focus on the dynamic volatility behavior of the daily Swedish Real Estate Sector Index and analyze the existence and degree of a long-range dependence or asymmetric news effect since 2003. More specifically, we give extra attention to the 2007–2008 financial crisis, the 2009–2012 European debt crisis, and the first two years of the global COVID-19 pandemic era (2020–2021). We examine changes in volatility during these extreme events. We apply standard GARCH models, asymmetric GARCH models, and long-memory GARCH models with various error distributions to identify the most accu

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32

Alòs, Elisa, Jorge A. León, Monique Pontier, and Josep Vives. "A Hull and White Formula for a General Stochastic Volatility Jump-Diffusion Model with Applications to the Study of the Short-Time Behavior of the Implied Volatility." Journal of Applied Mathematics and Stochastic Analysis 2008 (February 10, 2008): 1–17. http://dx.doi.org/10.1155/2008/359142.

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We obtain a Hull and White type formula for a general jump-diffusion stochastic volatility model, where the involved stochastic volatility process is correlated not only with the Brownian motion driving the asset price but also with the asset price jumps. Towards this end, we establish an anticipative Itô's formula, using Malliavin calculus techniques for Lévy processes on the canonical space. As an application, we show that the dependence of the volatility process on the asset price jumps has no effect on the short-time behavior of the at-the-money implied volatility skew.

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Sias, Richard, Harry J. Turtle, and Blerina Zykaj. "Hedge Fund Return Dependence: Model Misspecification or Liquidity Spirals?" Journal of Financial and Quantitative Analysis 52, no. 5 (2017): 2157–81. http://dx.doi.org/10.1017/s0022109017000679.

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We test whether model misspecification or liquidity spirals primarily explain the observed excess dependence in filtered (for economic fundamentals) hedge fund index returns and the links between volatility, liquidity shocks, and hedge fund return clustering. Evidence supports the model misspecification hypothesis: i) hedge fund filtered return clustering is symmetric, ii) filtered Short Bias fund returns exhibit negative dependence with filtered returns for other hedge fund types, iii) negative liquidity shocks are associated with clustering in both tails and market volatility subsumes the ro

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Mucci, Paul, Eun-Joo Lee, and Seung-Hwan Lee. "Stock Price Forecasting Using A Dependence Structure." European Journal of Mathematics and Statistics 3, no. 3 (2022): 21–29. http://dx.doi.org/10.24018/ejmath.2022.3.3.114.

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It is important to incorporate diverse dependence structures between stocks when managing a stock portfolio. Copulas are a useful statistical tool to capture dependence structure, dealing with both the linear and non-linear association that may occur in the tails of data. Financial time series datasets often exhibit volatility clustering that affects price forecasting accuracy. This work proposes the initial use of the principal component analysis followed by a copula and GARCH model that filters the effect of the volatility clustering in the series. For illustration, we consider ten banks fro

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35

Fousekis, Panos. "INFORMATIONAL EFFICIENCY OF THE US MARKETS FOR IMPLIED VOLATILITY BEFORE AND AFTER THE COVID-19 PANDEMIC." Applied Finance Letters 11 (December 14, 2022): 50–64. http://dx.doi.org/10.24135/afl.v11i.544.

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The objective of this work is to assess informational efficiency in four US markets for implied volatility. This has been pursued using daily data over 2015 to 2021 and a composite index that accounts for three possible sources of inefficiency associated with long-range dependence, short-range dependence, and entropy. The dominant pattern of long-range dependence has been that of anti-persistence both before and during the pandemic. The same applies for short-range dependence, especially before the pandemic. The presence of anti-persistence is an indication of investors’ over-reaction to incom

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Górka, Joanna, and Katarzyna Kuziak. "Volatility Modeling and Dependence Structure of ESG and Conventional Investments." Risks 10, no. 1 (2022): 20. http://dx.doi.org/10.3390/risks10010020.

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The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the

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Kalaitzi, Athanasia Stylianou, and Evgenia Stylianou Kalaitzi. "Forecasting Gasoline Market Volatility using Non-Linear Time Series Models." International Journal of Energy Economics and Policy 15, no. 4 (2025): 139–51. https://doi.org/10.32479/ijeep.18825.

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This study forecasts the dynamics of gasoline price returns using daily data from January 2, 1992, to June 6, 2022, and crude oil price returns as a regressor. The non-linear dependence in the volatility of the gasoline return is confirmed and the Markov Switching (MS), the autoregressive conditional heteroskedasticity (ARCH) and the generalized autoregressive conditional heteroskedasticity (GARCH) models are estimated. To account for the linear dependence found in the initial estimates, a GARCH (1,1) model with lagged gasoline returns is used, while a GARCH (1,1) is fitted on the Markov switc

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Dufitinema, Josephine, and Seppo Pynnönen. "Long-range dependence in the returns and volatility of the Finnish housing market." Journal of European Real Estate Research 13, no. 1 (2019): 29–54. http://dx.doi.org/10.1108/jerer-07-2019-0019.

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Purpose The purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types. Design/methodology/approach For each house price return series, both parametric and semiparametric long memory approaches are used to estima

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LAHMIRI, SALIM. "INVESTIGATING LONG-RANGE DEPENDENCE IN AMERICAN TREASURY BILLS VARIATIONS AND VOLATILITIES DURING STABLE AND UNSTABLE PERIODS." Fractals 24, no. 02 (2016): 1650025. http://dx.doi.org/10.1142/s0218348x16500250.

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Detrended fluctuation analysis (DFA) is used to examine long-range dependence in variations and volatilities of American treasury bills (TB) during periods of low and high movements in TB rates. Volatility series are estimated by generalized autoregressive conditional heteroskedasticity (GARCH) model under Gaussian, Student, and the generalized error distribution (GED) assumptions. The DFA-based Hurst exponents from 3-month, 6-month, and 1-year TB data indicates that in general the dynamics of the TB variations process is characterized by persistence during stable time period (before 2008 inte

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Aganin, Artem D. "Russian Stock Index volatility: Oil and sanctions." Voprosy Ekonomiki, no. 2 (February 7, 2020): 86–100. http://dx.doi.org/10.32609/0042-8736-2020-2-86-100.

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Since 2014, the Russian stock market has been under pressure due to both sanctions and a sharp drop in oil prices, which led to its increased volatility. This paper analyzes the impact of the price volatility of Brent oil and sanctions on the volatility of the Russian stock index RTS. Under volatility the paper understands both its parametric estimate obtained from the GARCH model estimation as well as non-parametric estimate — realized volatility. To estimate the effect of oil price volatility and sanctions, several cointegrated regressions were analyzed. The robustness of the results in rela

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Yoon, Ji-Hun, Jeong-Hoon Kim, Sun-Yong Choi, and Youngchul Han. "Stochastic volatility asymptotics of defaultable interest rate derivatives under a quadratic Gaussian model." Stochastics and Dynamics 17, no. 01 (2016): 1750003. http://dx.doi.org/10.1142/s0219493717500034.

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Stochastic volatility of underlying assets has been shown to affect significantly the price of many financial derivatives. In particular, a fast mean-reverting factor of the stochastic volatility plays a major role in the pricing of options. This paper deals with the interest rate model dependence of the stochastic volatility impact on defaultable interest rate derivatives. We obtain an asymptotic formula of the price of defaultable bonds and bond options based on a quadratic term structure model and investigate the stochastic volatility and default risk effects and compare the results with th

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Chakrabarti, B. B., and Vivek Rajvanshi. "Intraday Periodicity and Volatility Forecasting: Evidence from Indian Crude Oil Futures Market." Journal of Emerging Market Finance 16, no. 1 (2017): 1–28. http://dx.doi.org/10.1177/0972652716686207.

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We estimate intraday periodicities in return volatility by implementing two time series procedures—flexible Fourier form and cubic spline. We use intraday data for more than five years for crude oil futures contracts traded at the Multi Commodity Exchange India Limited. Filtration of the intraday periodicities from the raw returns reveals long-run dependence in volatility. We observe the presence of recurring and consistent intraday patterns in return volatility. Further, we find that adjustment for the intraday periodicity in return volatility improves forecasting performance. Our results are

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Mootamri, Imène. "Long Memory Process in Asset Returns with Multivariate GARCH Innovations." Economics Research International 2011 (September 7, 2011): 1–15. http://dx.doi.org/10.1155/2011/564952.

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The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long-term dependence in stock returns. More precisely, the long-term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process, and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confirm the presence of long memory property in the conditional mean of

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Cavaliere, Giuseppe, and A. M. Robert Taylor. "HETEROSKEDASTIC TIME SERIES WITH A UNIT ROOT." Econometric Theory 25, no. 5 (2009): 1228–76. http://dx.doi.org/10.1017/s026646660809049x.

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In this paper we provide a unified theory, and associated invariance principle, for the large-sample distributions of the Dickey–Fuller class of statistics when applied to unit root processes driven by innovations displaying nonstationary stochastic volatility of a very general form. These distributions are shown to depend on both the spot volatility and the integrated variation associated with the innovation process. We propose a partial solution (requiring any leverage effects to be asymptotically negligible) to the identified inference problem using a wild bootstrap–based approach. Results

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Men, Zhongxian, Tony S. Wirjanto, and Adam W. Kolkiewicz. "Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects." Journal of Risk and Financial Management 14, no. 5 (2021): 225. http://dx.doi.org/10.3390/jrfm14050225.

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This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market.

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Lawal, Adedoyin I., Russel O. C. Somoye, and Abiola A. Babajide. "Impact of Oil Price Shocks and Exchange Rate Volatility on Stock Market Behavior in Nigeria." Binus Business Review 7, no. 2 (2016): 171. http://dx.doi.org/10.21512/bbr.v7i2.1453.

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The impact of exchange rate and oil prices fluctuation on the stock market has been a subject of hot debate among researchers. This study examined the impact of both the exchange rate volatility and oil price volatility on stock market volatility in Nigeria, so as to guide policy formulation based on the fact that the nation’s economy was foreign induced and mono-cultured with heavy dependence on oil. EGARCH estimation techniques were employed to examine if either the volatility in exchange rate, oil price volatility or both experts on stock market volatility in Nigeria. The result shows that

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Schervish, Meredith, and Neil M. Donahue. "Peroxy radical chemistry and the volatility basis set." Atmospheric Chemistry and Physics 20, no. 2 (2020): 1183–99. http://dx.doi.org/10.5194/acp-20-1183-2020.

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Abstract. Gas-phase autoxidation of organics can generate highly oxygenated organic molecules (HOMs) and thus increase secondary organic aerosol production and enable new-particle formation. Here we present a new implementation of the volatility basis set (VBS) that explicitly resolves peroxy radical (RO2) products formed via autoxidation. The model includes a strong temperature dependence for autoxidation as well as explicit termination of RO2, including reactions with NO, HO2, and other RO2. The RO2 cross-reactions can produce dimers (ROOR). We explore the temperature and NOx dependence of t

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FEDOTOV, SERGEI, and ABBY TAN. "LONG MEMORY STOCHASTIC VOLATILITY IN OPTION PRICING." International Journal of Theoretical and Applied Finance 08, no. 03 (2005): 381–92. http://dx.doi.org/10.1142/s0219024905003013.

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The aim of this paper is to present a stochastic model that accounts for the effects of a long-memory in volatility on option pricing. The starting point is the stochastic Black–Scholes equation involving volatility with long-range dependence. We define the stochastic option price as a sum of classical Black–Scholes price and random deviation describing the risk from the random volatility. By using the fact that the option price and random volatility change on different time scales, we derive the asymptotic equation for this deviation involving fractional Brownian motion. The solution to this

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Oyinlola, Mutiu Abimbola, Oluwatosin Adeniyi, and Terver Theophilus Kumeka. "Dependence between foreign trade performance and exchange rate volatility: Panel ARDL approach." Croatian Review of Economic, Business and Social Statistics 9, no. 1 (2023): 1–15. http://dx.doi.org/10.2478/crebss-2023-0001.

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Abstract The purpose of this study is to analyse the influence of exchange rate shocks on foreign trade (exports and imports) of fifteen economies within the ECOWAS sub-region. To accomplish the goal of this paper, Autoregressive Distributed Lag (ARDL) procedure was employed to investigate the impact volatility in the exchange rate market has on foreign trade in both long- and short-term with data between 1980 and 2020. To compute volatility, it relied on the GARCH (1, 1) model which predicted the conditional variances as proxy for volatility. Our empirical results are distinguished into expor

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Janssen, Anja, and Holger Drees. "A stochastic volatility model with flexible extremal dependence structure." Bernoulli 22, no. 3 (2016): 1448–90. http://dx.doi.org/10.3150/15-bej699.

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