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Artykuły w czasopismach na temat „Directional and volatility dependence”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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Raggad, Bechir, and Elie Bouri. "Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests." Mathematics 11, no. 3 (2023): 528. http://dx.doi.org/10.3390/math11030528.

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We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in op

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3

Stádník, Bohumil. "The Riddle of Volatility Clusters." Business: Theory and Practice 15, no. (2) (2014): 140–48. https://doi.org/10.3846/btp.2014.14.

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In this financial engineering research we evaluate if observed non normalities in the market price distributions are caused mainly by a volatility clustering or also by another nonclustering mechanism. Such findings allow us to assess accor d ing to which rules the market price is actually developing or even make conclusions about market price directional forecasting chances, based on the realistic financial processes which we assign to the clustering and non clustering mechanisms. In the research we suggest certain methodology how to recognize these processes behind the market price devel

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4

Jiang, Zhuhua, Jose Arreola Hernandez, Ron P. McIver, and Seong-Min Yoon. "Nonlinear Dependence and Spillovers between Currency Markets and Global Economic Variables." Systems 10, no. 3 (2022): 80. http://dx.doi.org/10.3390/systems10030080.

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The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we aim to explore the spillovers and nonlinear dependencies between the seven major foreign exchange rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by employing a directional spillover network appr

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5

Sheraz, Muhammad, Silvia Dedu, and Vasile Preda. "Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data." Entropy 24, no. 10 (2022): 1410. http://dx.doi.org/10.3390/e24101410.

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This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow betwee

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6

Kim, Jong-Min, and Sun Young Hwang. "Functional ARCH directional dependence via copula for intraday volatility from high-frequency financial time series." Applied Economics 53, no. 4 (2020): 506–20. http://dx.doi.org/10.1080/00036846.2020.1808184.

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Stádník, Bohumil. "Market Price Forecasting and Profitability – How to Tame Mrandom Walk?" Business: Theory and Practice 14, no. (2) (2013): 166–76. https://doi.org/10.3846/btp.2013.18.

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Directional forecasting of a future market price development of liquid investment instruments is the focus of interest of investment companies, individual investors, banks and other financial market participants. This problematic has still not been fully answered because the market price development is a process which is very close to a random walk and appropriate models are still under the discussion. The opportunities can be used for the better prediction, their usage for profit making, quantification and also their discussion according to the current financial market models (models with the

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8

Boateng, Ebenezer, Peterson Owusu Junior, Anokye M. Adam, Mac Jr Abeka, Thobekile Qabhobho, and Emmanuel Asafo-Adjei. "Quantifying Information Flows among Developed and Emerging Equity Markets." Mathematical Problems in Engineering 2022 (August 22, 2022): 1–19. http://dx.doi.org/10.1155/2022/2462077.

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We rely on daily changes in implied volatility indices for the US stock market (VIX), developed markets excluding the US (VXEFA), stock markets in Brazil (VXEWZ), Russia (RVI), India (NIFVIX), China (VXFXI), and the overall emerging market volatility index (VXEEM) to examine the degree of information flows among the markets in the coronavirus pandemic. The study also employs the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the data into intrinsic mode functions (IMFs). Subsequently, we cluster the IMFs based on their level of frequencies into short-

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9

Oviedo-Gómez, Andrés, Sandra Milena Londoño-Hernández, and Diego Fernando Manotas-Duque. "Directional Spillover of Fossil Fuels Prices on a Hydrothermal Power Generation Market." International Journal of Energy Economics and Policy 13, no. 1 (2023): 85–90. http://dx.doi.org/10.32479/ijeep.13641.

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The Colombian electricity market is based on a hydrothermal power generation market with a strong dependence on exogenous variables such as fossil fuel prices and climatology factors. Besides, the Colombian economy is characterizable by relevant mining-energy activities. Therefore, the main objective of this research was to evaluate the directional spillovers between the electricity spot prices and gas, coal, and crude oil prices and thus provide relevant information for the electricity market agents to identify the risk related to energy commodity price fluctuations. The dataset used in this

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10

Akanni, Lateef Olawale. "Returns and volatility spillover between food prices and exchange rate in Nigeria." Journal of Agribusiness in Developing and Emerging Economies 10, no. 3 (2020): 307–25. http://dx.doi.org/10.1108/jadee-04-2019-0045.

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PurposeEmpirical studies have documented the linkage between exchange rate movement and food prices. However, the purpose of this study is to investigate the degree and direction of returns and volatility spillover transmission between exchange rate and domestic food prices in Nigeria.Design/methodology/approachThe study uses weekly data from January 2010 to January 2019. Also, the study adopts the improved Diebold and Yilmaz (2012) approach to evaluate the return and volatility spillover between food price and naira to dollar exchange rate. The study also account for 2016 exchange rate crash

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11

Stádník, Bohumil. "TESTING OF MARKET PRICE DIRECTION DEPENDENCE ON US STOCK MARKET." Business, Management and Education 10, no. 2 (2012): 205–19. http://dx.doi.org/10.3846/bme.2012.15.

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The correct model of a liquid financial market is very important for all market activities including for example a stock or bond portfolio management or an asset valuation. Dynamic Financial Market Model is a comprehensive model with a detailed interpretation. The model considers also feedback processes which cause price development direction dependence on the previous development. This is why it is also able to explain departures from normality as leptokurtic deformations with fat tails and sharpness, extreme values or skewness in the returns’ probability distributions. These departures are c

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12

Zeng, Hongjun, and Ran Lu. "High-frequency volatility connectedness and time-frequency correlation among Chinese stock and major commodity markets around COVID-19." Investment Management and Financial Innovations 19, no. 2 (2022): 260–73. http://dx.doi.org/10.21511/imfi.19(2).2022.23.

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This study examines the connectedness and time-frequency correlation of price volatility across the Chinese stock market and major commodity markets. This paper applies a DCC-GARCH-based volatility connectedness model and the cross-wavelet transform to examine the transmission of risk patterns in these markets before and during the COVID-19 outbreak, as well as the leading lag relationship and synergistic movements between different time domains. First, the findings of the DCC-GARCH connectedness model show dynamic total spillovers are stronger after the COVID-19 outbreak. Chinese stocks and c

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13

Liu, Ji, Zheng Xu, Ying Yang, Kun Zhou, and Munish Kumar. "Dynamic Prediction Model of Financial Asset Volatility Based on Bidirectional Recurrent Neural Networks." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–23. http://dx.doi.org/10.4018/joeuc.345925.

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Predicting financial market volatility is essential for investors and risk management. This study proposes a dynamic prediction model for financial asset volatility, with a Bi-directional Recurrent Neural Network (Bi-RNN) utilized to cleverly address market complexity. Our framework integrates Bi-RNN and gated recurrent units (GRU) to perform global optimization via particle swarm optimization algorithm (PSO). Bi-RNN combines historical data and future expectations, while GRU effectively solves long-term dependency issues through a gating mechanism, which enhances model generalization. Experim

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14

Song, Yahao, Yajun Wu, Songtao Duan, Chendan Dou, Bei Liu, and Bingxue Hou. "CNN-BiLSTM combined with Bayesian optimization for short-term wind power prediction." Journal of Physics: Conference Series 2938, no. 1 (2025): 012002. https://doi.org/10.1088/1742-6596/2938/1/012002.

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Abstract Aiming at the difficulty of wind power prediction due to the volatility and uncertainty of wind power generation, this paper proposes a hybrid model based on Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM), and optimises the model hyper-parameters using Bayesian Optimization Algorithm (BO) in order to improve the prediction accuracy. Firstly, the input features that are highly correlated with wind power are screened using the Pearson Coefficient method (PCC). Then, CNN is used to extract features from the screened data. Next, the features e

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15

Lee, Sangheon, and Poongjin Cho. "Graph-Based Stock Volatility Forecasting with Effective Transfer Entropy and Hurst-Based Regime Adaptation." Fractal and Fractional 9, no. 6 (2025): 339. https://doi.org/10.3390/fractalfract9060339.

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This study proposes a novel hybrid model for stock volatility forecasting by integrating directional and temporal dependencies among financial time series and market regime changes into a unified modeling framework. Specifically, we design a novel Hurst Exponent Effective Transfer Entropy Graph Neural Network (H-ETE-GNN) model that captures directional and asymmetric interactions based on Effective Transfer Entropy (ETE), and incorporates regime change detection using the Hurst exponent to reflect evolving global market conditions. To assess the effectiveness of the proposed approach, we compa

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16

Din, Riaz Ud, Salman Ahmed, Saddam Hussain Khan, Abdullah Albanyan, Julian Hoxha, and Bader Alkhamees. "A novel decision ensemble framework: Attention-customized BiLSTM and XGBoost for speculative stock price forecasting." PLOS ONE 20, no. 4 (2025): e0320089. https://doi.org/10.1371/journal.pone.0320089.

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Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-

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17

Lovreta, Lidija, and Joaquín López Pascual. "Structural breaks in the interaction between bank and sovereign default risk." SERIEs 11, no. 4 (2020): 531–59. http://dx.doi.org/10.1007/s13209-020-00219-z.

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AbstractThe recent financial and sovereign debt crises emphasized the interdependence between bank and sovereign default risk and showed that major shocks may lead to a self-reinforcing negative spiral. In this paper, we analyse the pattern of interaction between bank and sovereign default risk by endogenously estimating the timing of structural breaks. The endogenous approach avoids the problem of choosing the number and the location of important turning points associated with the exogenous selection of break dates, commonly applied in the literature. In addition, it provides additional insig

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18

Hadi Abdullah, Aamna Tariq, Ijaz khan, Rizwan Iqbal, Faisal Khan, and Arshad iqbal. "<b>Recurrent Neural Networks in Time-Series Forecasting: A Deep Learning Approach to Stock Market Prediction</b>." Annual Methodological Archive Research Review 3, no. 6 (2025): 72–101. https://doi.org/10.63075/24bjb734.

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Stock market prediction has been a grand challenge due to dynamic nature, non-linearity and volatility of the financial markets. Traditional statistical models have proved useful historically, but are less likely to successfully model the complex temporal dependencies in stock price data. In recent years there was a breakthrough in deep learning, namely Recurrent Neural Networks (RNNs), which opens up new opportunities in time-series forecasting. The purpose of the work is to investigate how three variations of RNN-based models, such as Simple RNN, Long Short-Term Memory (LSTM), and Gated Recu

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19

Hung, Ngo Thai. "Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin." Managerial Finance 48, no. 4 (2022): 587–610. http://dx.doi.org/10.1108/mf-08-2021-0355.

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PurposeThis paper investigates the dynamic intercorrelation among cryptocurrency (Bitcoin) and conventional financial assets (gold, oil and S&P 500).Design/methodology/approachThe dynamic contemporaneous nexus has been analyzed using spillover index developed and extended by Diebold and Yilmaz (2012, 2014) and Kyrtsou-Labys (2006) nonlinear causality tests. This study is implemented using the daily data spanning from January 2013 to December 2021.FindingsFirst, using the spillover index, the authors find evidence that the S&P 500 was a net transmitter of volatility from oil and gold ma

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20

Abualuroug, Said, Ahmad Alzubi, and Kolawole Iyiola. "Inventory Prediction Using a Modified Multi-Dimensional Collaborative Wrapped Bi-Directional Long Short-Term Memory Model." Applied Sciences 14, no. 13 (2024): 5817. http://dx.doi.org/10.3390/app14135817.

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Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a n

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21

Przekota, Grzegorz, and Anna Szczepańska-Przekota. "Directions of Price Transmission on the Diesel Oil Market in Poland." Energies 18, no. 1 (2025): 139. https://doi.org/10.3390/en18010139.

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The formation of crude oil prices and their impact on diesel prices represent a significant economic challenge. The economy’s dependence on energy resources means that the development and competitiveness of the economy, as well as the standard of living of society, are contingent upon energy prices, including those of liquid fuels. It is therefore important to recognise the process by which changes in the price of crude oil affect other commodities. The recognition of these dependencies will have implications for political and fiscal decision-making at the governmental level, investment strate

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22

Akgüller, Ömer, Mehmet Ali Balcı, Larissa Margareta Batrancea, and Lucian Gaban. "Fractional Transfer Entropy Networks: Short- and Long-Memory Perspectives on Global Stock Market Interactions." Fractal and Fractional 9, no. 2 (2025): 69. https://doi.org/10.3390/fractalfract9020069.

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This study addresses the challenge of capturing both short-run volatility and long-run dependencies in global stock markets by introducing fractional transfer entropy (FTE), a new framework that embeds fractional calculus into transfer entropy. FTE allows analysts to tune memory parameters and thus observe how different temporal emphases reshape the network of directional information flows among major financial indices. Empirical evidence reveals that when short-memory effects dominate, markets swiftly incorporate recent news, creating networks that adapt quickly but remain vulnerable to trans

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23

DANKO, Yuriy I., and Anna M. OREL. "MODERN DIRECTIONS OF ENSURING THE COMPETITIVENESS OF AGRICULTURAL PRODUCTS OF UKRAINE." Ukrainian Journal of Applied Economics 4, no. 3 (2019): 98–105. http://dx.doi.org/10.36887/2415-8453-2019-3-11.

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The complexity and versatility of the concept of «competitiveness», as well as its consideration at all levels of socio-economic systems requires in-depth analysis of its nature to determine the characteristics of agricultural products, whose exclusive role is related to food security. The purpose of the study is to identify modern areas of ensuring the competitiveness of agricultural products in Ukraine. It is established that the competitiveness should be understood as the ability of a subject to operate successfully in the market, having an absolute or relative advantage over competitors. I

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Sandeep, Yadav. "Predictive Modeling of Cryptocurrency Price Movements Using Autoregressive and Neural Network Models." International Journal on Science and Technology 14, no. 1 (2023): 1–9. https://doi.org/10.5281/zenodo.14288541.

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Cryptocurrency markets are highly volatile and driven by complex, non-linear dynamics, posing significant challenges for price prediction. This research explores the predictive modeling of cryptocurrency price movements by integrating traditional statistical techniques, such as Autoregressive (AR) models, with advanced Neural Network (NN) architectures. The study evaluates the performance of these models in forecasting short-term price trends for major cryptocurrencies like Bitcoin, Ethereum, and Binance Coin. The dataset consists of historical price data and technical indicators, preprocessed

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25

Qadir, Mariyam, Dr Saqib Gulzar, and Dr Muhammad Owais. "Dynamics of Volatility Spillover among the US and emerging Asian stock markets amid the COVID-19 pandemic." Inverge Journal of Social Sciences 2, no. 3 (2023): 44–64. https://doi.org/10.63544/ijss.v2i3.46.

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This study examines the dynamics of volatility Spillover among the US and emerging Asian Stock markets (China, Pakistan, India, Malaysia and Korea) amid the COVID-19 pandemic. The analysis used data of daily stock returns and the time period is divided into two phases: pre and during COVID-19. The pre period is from November 1st, 2017 to November 30th, 2019 and during period is from December 1st, 2019 to December 31st, 2021. The pre-period has been taken for comparative purpose. The Spillover index method provided by Diebold and Yilmaz (2012) is use to check these dynamics. The findings indica

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Mohammed, Walid Abass. "Volatility Spillovers among Developed and Developing Countries: The Global Foreign Exchange Markets." Journal of Risk and Financial Management 14, no. 6 (2021): 270. http://dx.doi.org/10.3390/jrfm14060270.

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In this paper, we investigate the “static and dynamic” return and volatility spillovers’ transmission across developed and developing countries. Quoted against the US dollar, we study twenty-three global currencies over the time period 2005–2016. Focusing on the spillover index methodology, the generalised VAR framework is employed. Our findings indicate no evidence of bi-directional return and volatility spillovers between developed and developing countries. However, unidirectional volatility spillovers from developed to developing countries are highlighted. Furthermore, our findings document

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Noh, Hohsuk, Hyuna Jang, Kun Ho Kim, and Jong-Min Kim. "Nonparametric Directional Dependence Estimation and Its Application to Cryptocurrency." Axioms 12, no. 3 (2023): 293. http://dx.doi.org/10.3390/axioms12030293.

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This paper proposes a nonparametric directional dependence by using the local polynomial regression technique. With data generated from a bivariate copula having a nonmonotone regression structure, we show that our nonparametric directional dependence is superior to the copula directional dependence method in terms of the root-mean-square error. To validate the directional dependence with real data, we use the log returns of daily prices of Bitcoin, Ethereum, Ripple, and Stellar. We conclude that our nonparametric directional dependence, by using the local polynomial regression technique with

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von Eye, Alexander, and Richard P. DeShon. "Decisions concerning directional dependence." International Journal of Behavioral Development 36, no. 4 (2012): 323–26. http://dx.doi.org/10.1177/0165025412450111.

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Petrov, Vladimir, Anton Golub, and Richard Olsen. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time." Journal of Risk and Financial Management 12, no. 2 (2019): 54. http://dx.doi.org/10.3390/jrfm12020054.

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We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates, as well as a stock market index. We demonstrate the long memory of instantaneou

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Maris, K., K. Nikolopoulos, K. Giannelos, and V. Assimakopoulos. "Options trading driven by volatility directional accuracy." Applied Economics 39, no. 2 (2007): 253–60. http://dx.doi.org/10.1080/00036840500427999.

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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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Aslam, Faheem, Paulo Ferreira, Khurrum Shahzad Mughal, and Beenish Bashir. "Intraday Volatility Spillovers among European Financial Markets during COVID-19." International Journal of Financial Studies 9, no. 1 (2021): 5. http://dx.doi.org/10.3390/ijfs9010005.

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During crises, stock market volatility generally rises sharply, and as consequence, spillovers are identified across markets. This study estimates the volatility spillover among twelve European stock markets representing all four regions of Europe. The data consists of 10,990 intraday observations from 2 December 2019 to 29 May 2020. Using the methodology of Diebold and Yilmaz, we use static and rolling windows to characterize five-minute volatility spillovers. Our results show that 77.80% of intraday volatility forecast error variance in twelve European markets comes from spillovers. Furtherm

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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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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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von Eye, Alexander, and Richard P. DeShon. "Directional dependence in developmental research." International Journal of Behavioral Development 36, no. 4 (2012): 303–12. http://dx.doi.org/10.1177/0165025412439968.

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In this article, we discuss and propose methods that may be of use to determine direction of dependence in non-normally distributed variables. First, it is shown that standard regression analysis is unable to distinguish between explanatory and response variables. Then, skewness and kurtosis are discussed as tools to assess deviation from normality. Deviation from normality can be used to assess direction of dependence. This proposition is based on the fact that the response variable will always have less skew than the independent variable (Dodge & Rousson, 2000). It has been shown that th

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Quesada-Molina, José Juan, and Manuel Úbeda-Flores. "Directional dependence of random vectors." Information Sciences 215 (December 2012): 67–74. http://dx.doi.org/10.1016/j.ins.2012.05.019.

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Nelsen, Roger B., and Manuel Úbeda-Flores. "Directional dependence in multivariate distributions." Annals of the Institute of Statistical Mathematics 64, no. 3 (2011): 677–85. http://dx.doi.org/10.1007/s10463-011-0329-6.

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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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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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40

Thoemmes, Felix. "Empirical evaluation of directional-dependence tests." International Journal of Behavioral Development 39, no. 6 (2015): 560–69. http://dx.doi.org/10.1177/0165025415582055.

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Testing of directional dependence is a method to infer causal direction that recently has attracted some attention. Previous examples by e.g. von Eye and DeShon (2012a) and extensive simulation studies by Pornprasertmanit and Little (2012) have demonstrated that under specific assumptions, directional-dependence tests can recover the true causal direction between two variables. Simulation results are important in the evaluation of any statistical method, but they are necessarily less complex than real data that come with potential irregularities (e.g. departures from linearity, presence of con

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Janković, Irena. "THE EFFECTS OF VOLATILITY SPILLOVER ON THE LARGEST GLOBAL FINANCIAL MARKET SEGMENTS." Facta Universitatis, Series: Economics and Organization, no. 2 (January 23, 2019): 319. http://dx.doi.org/10.22190/fueo1804319j.

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The aim of the paper is to present and analyse indicators of financial connectedness and volatility spillover on important segments of the global financial market – the stock market, bond market, CDS market, and foreign exchange market. Total, net, and directional measures of volatility spillover are presented and analysed, indicating the level of connectedness of countries’ particular market segments and the level of volatility spillover in periods of crisis and stability.

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Vurur, Necmiye Serap. "The Relationship of BIST Sector Indices with Exchange Rate Volatility." Journal of corporate governance, insurance and risk management 8, no. 1 (2021): 56–74. http://dx.doi.org/10.51410/jcgirm.8.1.4.

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Through globalization, the increased integration in financial markets has made the relationship between exchange rate and stocks important. The study aims to model the exchange rate volatility using daily data for the period 04.01.2010-15.10.2020 and investigate the causality relationship between sector returns and exchange rate return volatility. In order to model the volatility of the exchange rate return series, the GARCH model was used to reveal the possible asymmetry feature in the series. As a result of the model applications, GARCH (2,2) was determined as the most suitable model to meas

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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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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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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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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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Lin, Ching-Chung, Shen-Yuan Chen, Dar-Yeh Hwang, and Chien-Fu Lin. "Does Index Futures Dominate Index Spot? Evidence from Taiwan Market." Review of Pacific Basin Financial Markets and Policies 05, no. 02 (2002): 255–75. http://dx.doi.org/10.1142/s021909150200078x.

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By utilizing vector error correction model (VECM) and EGARCH model, this article uses 5-minute intraday data to examine the interaction of return and volatility between Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the newly introduced TAIEX futures. VECM model shows that there exists bi-directional Granger causality between index spot and index futures markets, but spot market plays a more important role in price discovery. The results of impulse response function and information share indicate that most of the price discovery happens in index spot market. The evidence

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Emenike, Kalu Onwukwe. "Exchange rate volatility in West African countries: is there a shred of Spillover?" International Journal of Emerging Markets 13, no. 6 (2018): 1457–74. http://dx.doi.org/10.1108/ijoem-08-2017-0312.

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Purpose The purpose of this paper is to evaluate selected West African currencies/US dollar exchange rates for the evidence of volatility spillover. Specifically, the paper examines West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD, for any evidence of shock and volatility spillover. Design/methodology/approach The author employs multivariate GARCH (1,1)–BEKK model which enables the evaluation of the interaction within the volatility of two or more series because of its capability to detect volatility spillover among time series observations, as we

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Middlebrooks, John C., and David M. Green. "Directional dependence of interaural envelope delays." Journal of the Acoustical Society of America 87, no. 5 (1990): 2149–62. http://dx.doi.org/10.1121/1.399183.

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Sungur, Engin A., and Jessica M. Orth. "Understanding Directional Dependence Through Angular Correlation." Communications in Statistics - Theory and Methods 43, no. 19 (2014): 4143–55. http://dx.doi.org/10.1080/03610926.2012.707735.

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