Volatility Dynamics Research Articles

한국 주택가격의 동태적 변화 연구

Housing is one of the essential goods that determine the conditions of life. However, housing prices fluctuate widely depending on macroeconomic conditions, making it an important research topic. This study analyzes the dynamic changes in quarterly housing prices from 2006 to 2023, using GARCH model, Granger causality test, and vector error correction model(VECM). The main finding are as follows. First, there are inertial feedback effects in house price changes. Second, there is clustering and long-run persistence in the dynamics of house price volatility. Third, we find significant effects of housing loans on the demand side and housing construction permits and unsold housing units on the supply side on house prices. Housing loans have a positive effect in the short run, while housing supply variables such as housing construction permits and the number of unsold housing units have a negative effect in the long run. These results of our analysis suggest that the housing market follows the market principle that the role of housing supply and demand works well, and that housing prices have inertial characteristics with spikes and crashes depending on the economic environment. Housing policy-makers need to take these findings into account when formulating housing policies.

Nexus among Exchange Rate Volatility, Inflation, and Economic Growth: A Panel Data Analysis

This study undertakes an examination of the intricate relationships among exchange rate fluctuations, inflation, and economic growth within South Asian nations, namely Pakistan, India, Bangladesh, Sri Lanka, and Nepal. Spanning the temporal horizon from 1990 to 2021, the research endeavors to unveil the repercussions of exchange rate instability on economic growth—a formidable challenge confronted by monetary authorities and policymakers in South Asia. The central focus lies on comprehending the dynamics of exchange rate volatility, inflationary pressures, and their collective impact on economic growth. In the pursuit of these objectives, GDP per capita serves as the yardstick for economic growth, while independent variables encompass a spectrum including broad money supply, exchange rate volatility, inflation, government consumption expenditures, and gross fixed capital formation. The quantification of exchange rate volatility entails the utilization of both the standard deviation and the GARCH (1,1) models. Subsequent to an in-depth analysis, the initial unit test results undergo reevaluation, incorporating the Breusch-Pagan LM, Pesaran CD, and Pesaran scaled LM tests to discern cross-sectional dependence. To address potential data non-stationarity while considering cross-sectional dependence, the Pesaran-CIPS and Pesaran's CADF tests are applied. A pivotal aspect of the research involves the application of a Feasibly Generalized Least Squares (FGLS) regression model. The outcomes of the FGLS panel data regression illuminate a noteworthy finding: exchange rate volatility, quantified through both techniques, significantly and adversely influences economic growth. In contrast, inflation exhibits a statistically insignificant negative impact on economic growth. The study culminates by proffering specific recommendations to policymakers, urging concerted efforts to ensure exchange rate stability and foster sustained economic growth.

Volatility transmission between upstream and midstream energy sectors

This research studies volatility dynamics of the returns to upstream (exploration and production) and midstream (pipeline and storage) energy (oil and gas) sectors over time and across sectors. We find significant structural breaks in volatility of upstream sector returns possibly triggered by major events, while no breaks are detected in the midstream sector. These results indicate the midstream sector is fairly insulated from major events relative to the upstream sector. Results based on a bivariate generalized ARCH model, which controls for breaks, indicate that the conditional variance of the upstream sector is significantly more affected by midstream volatility than vice versa. This finding could be due to the fundamental difference in functions performed by the midstream and upstream sector firms. These patterns in volatility dynamics have practical significance for financial market participants.

Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators

This paper proposes an unbiased combined weighted (CW) volatility measure and weighted volatility indicators (WVI) that integrates the return- and range-based volatility measures to model the dynamics volatility of stock returns. The main feature of the CW measure is that it is formulated based on the weighted inter- and intra-price information to quantify the volatility directly, while the WVI effectively identifies signals on the shift of volatility. Empirical analysis using five stock indices demonstrates that the CW measure, utilising squared returns in combination with range-based Garman-Klass volatility measure, exhibits the lowest losses based on root mean squared error and quasi-likelihood when compared to 5-minute realised volatility as a proxy for true volatility. Furthermore, we investigate the feasibility of incorporating the CW measure and WVI as the exogenous variable(s) in the generalised autoregressive conditional heteroscedasticity (GARCH)-type models to enhance the forecasting performance. The findings indicate that the GARCH-CW-WVI and EGARCH-CW-WVI models exhibit superior in-sample model fit based on the Akaike information criterion than the existing GARCH and EGARCH models. Moreover, our proposed models also offer the best out-of-sample forecasts evaluated using various loss functions and further tested using Hansen’s model confidence set based on the mean squared error loss. Different risk levels of value-at-risk (VaR) and expected shortfall (ES) forecasts based on GARCH-CW-WVI and EGARCH-CW-WVI models are computed and examined with various backtests to confirm the accuracies of VaR and ES forecasts.

High-resolution kinetics of herbivore-induced plant volatile transfer reveal clocked response patterns in neighboring plants.

Volatiles emitted by herbivore-attacked plants (senders) can enhance defenses in neighboring plants (receivers), however, the temporal dynamics of this phenomenon remain poorly studied. Using a custom-built, high-throughput proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) system, we explored temporal patterns of volatile transfer and responses between herbivore-attacked and undamaged maize plants. We found that continuous exposure to natural blends of herbivore-induced volatiles results in clocked temporal response patterns in neighboring plants, characterized by an induced terpene burst at the onset of the second day of exposure. This delayed burst is not explained by terpene accumulation during the night, but coincides with delayed jasmonate accumulation in receiver plants. The delayed burst occurs independent of day:night light transitions and cannot be fully explained by sender volatile dynamics. Instead, it is the result of a stress memory from volatile exposure during the first day and secondary exposure to bioactive volatiles on the second day. Our study reveals that prolonged exposure to natural blends of stress-induced volatiles results in a response that integrates priming and direct induction into a distinct and predictable temporal response pattern. This provides an answer to the long-standing question of whether stress volatiles predominantly induce or prime plant defenses in neighboring plants, by revealing that they can do both in sequence.

High-resolution kinetics of herbivore-induced plant volatile transfer reveal clocked response patterns in neighboring plants

Volatiles emitted by herbivore-attacked plants (senders) can enhance defenses in neighboring plants (receivers), however, the temporal dynamics of this phenomenon remain poorly studied. Using a custom-built, high-throughput proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) system, we explored temporal patterns of volatile transfer and responses between herbivore-attacked and undamaged maize plants. We found that continuous exposure to natural blends of herbivore-induced volatiles results in clocked temporal response patterns in neighboring plants, characterized by an induced terpene burst at the onset of the second day of exposure. This delayed burst is not explained by terpene accumulation during the night, but coincides with delayed jasmonate accumulation in receiver plants. The delayed burst occurs independent of day:night light transitions and cannot be fully explained by sender volatile dynamics. Instead, it is the result of a stress memory from volatile exposure during the first day and secondary exposure to bioactive volatiles on the second day. Our study reveals that prolonged exposure to natural blends of stress-induced volatiles results in a response that integrates priming and direct induction into a distinct and predictable temporal response pattern. This provides an answer to the long-standing question of whether stress volatiles predominantly induce or prime plant defenses in neighboring plants, by revealing that they can do both in sequence.

The dynamic volatility nexus of geo-political risks, stocks, bond, bitcoin, gold and oil during COVID-19 and Russian-Ukraine war.

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Dynamic volatility spillover relationships between the Chinese carbon and international energy markets from extreme climate shocks

The immense attention to climate change risk has stimulated increasing interest in carbon markets and their linkages to other markets. This study investigates the dynamic volatility spillover relationships between the Chinese carbon and international energy markets, and the impact of extreme weather events on the relationships. Empirical results show that the risk is transferred from the international gas (GAS) and crude oil (OIL) markets to the Chinese carbon market (GDC). The total volatility spillover among markets and the net spillover of GDC with other markets both have obvious periodicity. The shocks from extreme weather events and extremely high temperatures can amplify the trading volume of GDC, and strengthen the fluctuations of GDC, ultimately turning GDC from a risk receiver to a risk transmitter. The portfolio strategies based on the reversal phenomenon can achieve higher average returns compared to the Chinese and EU carbon markets. In addition, the shocks of the Sino-US trade war, the Covid-19 outbreak, the COP 26 and the Russo-Ukrainian war have different effects on the volatility spillovers among markets. Economic and policy implications for investors and regulators are also provided along with these findings.

Volatility and dependence in crude oil and agricultural commodity markets

ABSTRACT This paper studies the evolution, volatility dynamics, and nonlinear dynamics between crude oil prices and major agricultural commodity prices. Semiparametric GARCH-in-Mean copula models are applied to monthly crude oil, corn, soybean, palm oil, rice, sugar, and wheat prices from January 1990 to May 2023. We find that the Clayton copula is the best copula to describe the (bivariate) dependence structures between the crude oil and soybean, crude oil and palm oil, crude oil and sugar, and crude oil and wheat markets, suggesting the statistically significant lower tail dependence between crude oil and major agricultural commodity returns. Moreover, the tail dependence is strongest between crude oil and palm oil.

Analysis of the impact of COVID-19 on the dynamic volatility spillover network in the Saudi stock market

This study investigates the risk spillover among the COVID-19-related news Panic Index, the insurance industry, the Tadawul All-Share Index, and sector indices in Saudi stock market. Volatility spillovers serve as an accurate indicator for the transmission of risk across various sectors. Our empirical findings show that, prior to the outbreak of COVID-19, the Tadawul All-Share stock market played a dominant role as a net transmitter of risk for sector indices, including insurance, media, pharma, retail, transport, and utilities. Furthermore, our analysis demonstrates that the COVID-19 Panic Index exerts significant influence on the net pairwise connectedness, particularly impacting the Tadawul All-Share Index, energy, banking, development, materials, real estate, transport, and utilities sectors. Additionally, our results indicate that the insurance sector acts as a net receiver of spillovers from the utilities, media, pharma, and retail sectors throughout the sample period, affirming the heightened impact of the pandemic on these sectors. The pandemic, being a health-related issue, led to a notable surge in the volatility of sectors associated with health, given their pivotal role in effectively addressing the crisis. These insights carry substantial implications for investors, portfolio managers, and policymakers, shedding light on the potential repercussions of the pandemic on the stock market.

Revisiting the predictive prowess of economic policy uncertainty (EPU) in stock market volatility: GEPU or NEPU?

We employ the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variant of the Mixed Data Sampling (MIDAS) [GARCH-MIDAS] model to revisit the forecasting prowess of economic policy uncertainty (EPU) in the predictability of stock market volatility. We show that both the global economic policy uncertainty (GEPU) and its domestic dimension, namely the Nigerian Economic Policy Uncertainty (NEPU), discourage future investment in the stock market. However, compared to NEPU, we show results that give credence to the relatively higher effect of GEPU on the volatility dynamics of the stock market. We also show the comparative out-of-sample forecasts of the NEPU and GEPU to be episodic in the predictability of volatility in the stock market. The fact that our results are robust across multiple forecast horizons is an indication that events that form the premise upon which a particular dimension of the EPU is measured matter in their forecasting prowess.

Co-volatility dynamics in global cryptocurrency and conventional asset classes: a multivariate stochastic factor volatility approach

PurposeThis study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds.Design/methodology/approachIt used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation.FindingsThe study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics.OriginalityThis study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.

Volatility Spillovers in Emerging Markets: Oil Shocks, Energy, Stocks, and Gold

This paper extends the literature on the relationship between oil price shocks and financial markets by examining the effect of supply and demand oil shocks on emerging energy markets, stocks of emerging markets, and gold and exploring the impact of unpredictable oil events on the degree of connectedness among these markets. We show that the effect of supply oil price disturbances drives volatility spillovers in emerging markets with prominent medium- and long-term effects, unlike demand oil price unanticipated changes, particularly during turbulent periods such as the 2008 subprime crisis, the COVID-19 pandemic, and the 2015 oil price crash. These volatility spillover effects are influenced by a marked relationship between supply oil disturbances and emerging energy markets. We also expose that the COVID-19 pandemic volatility spillover consequences in emerging markets are unprecedented compared to the 2008 financial crisis. This can be attributed to the different nature of the related oil price disturbances and financial crises. Overall, the findings highlight the role of crude oil supply shocks as drivers not only of volatility dynamics in energy and equity emerging markets but also of financial connectedness patterns in these economies.

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

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 accurate volatility models of the daily returns of the Swedish Real Estate Sector Index for the full sample period, January 2003 to June 2021. Our results show that the volatility of the Swedish Real Estate Sector Index is time-varying and highly volatile. The impacts of the global financial crisis, European debt crisis, and COVID-19 pandemic are noticeable. Moreover, the volatility pattern during COVID-19 displays significant time-varying long-range dependence and an asymmetrical news impact, which lead to market inefficiency. Finally, the volatility pattern shows a tendency towards increasing leverage effects and less persistent behavior, indicating that the market stakeholders are highly sensitive to negative returns and becoming quicker to respond to market changes.

Explore the Impact of Macroeconomic Indicators on WTI Oil Prices

This research paper explores the relationships between West Texas Intermediate (WTI) oil prices and various macroeconomic indicators. Given the significant role of WTI prices in shaping global economic activities, understanding their volatile dynamics is crucial for numerous industries. Predicting these prices, however, poses significant challenges due to the complex nature of the oil market, influenced by myriad factors such as geopolitical developments, supply-demand imbalances, and diverse macroeconomic parameters. The research addresses this issue by developing a predictive model for WTI oil prices, deploying an Autoregressive Integrated Moving Average (ARIMA) model. The study has a selection of macroeconomic variables that accurately reflect the global economy's various aspects influencing WTI prices. These include the NASDAQ index, the US Dollar Index (DXY), the Economic Uncertainty Index, the 5-Year Inflation Break Even, the US 3-Month Treasury, the 10Y Less 2Y, the US Oil Demand, and the difference between oil Demand and Supply. This research aims to deepen our understanding of the multifaceted relationship between WTI oil prices and these critical macroeconomic indicators. In doing so, this study offers substantial academic contributions to comprehending oil price dynamics. Additionally, it provides valueable insights for industry stakeholders, financial analysts, policymakers, and investors, enhancing economic planning and risk management across sectors.

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

Locally risk minimizing pricing of Asian option in a semi-Markov modulated market

We consider a regime-switching model where the stock volatility dynamics is a semi-Markov process. Under this model assumption, we find the locally risk-minimizing price of some Asian options with European-style exercise. The price function is shown to satisfy a non-local degenerate system of parabolic PDEs in dimension two with a terminal condition. We show this by deriving the F-S decomposition of the discounted contingent claim. The related Cauchy problem involving the PDE is shown to be equivalent to an integral equation (IE). The existence and uniqueness of the classical solution to the PDE are determined by studying the IE and using semigroup theory. To be more precise, we first obtain the mild solution of the PDE and then we show that the mild solution has sufficient regularity. The locally risk-minimizing hedging for the option has also been identified in this work. Finally, the computational aspects of Asian option prices have been discussed by solving the equation numerically.

Diversity-induced trivialization and resilience of neural dynamics.

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.

Decoupling Interday and Intraday Volatility Dynamics with Price Durations

Decoupling Interday and Intraday Volatility Dynamics with Price Durations

Essential oil vapor phase approach for antimicrobial evaluation using Hesperozygis myrtoides (A. St.-Hill. ex Benth.) Epling (Lamiaceae) as a model.

The demand for less hazardous and environmentally friendly surface disinfectants is growing and essential oils may be efficient alternatives. Vapor phase assay is a promising technique which explore the volatility of essential oils for determining the antimicrobial activity. In this study, we modified the conditions of the inverted Petri dish technique to evaluate the minimum inhibitory concentrations (MIC). As a proof of concept, the antimicrobial activity of Hesperozygis myrtoides essential oil (HM) was studied for the first time in the vapor phase. According to the established protocol the oil volatilization dynamics was used to determine the major compounds in the vapor phase. After 270 min, the relative percentage was like that observed in the crude oil analysis. The HM was also evaluated for safety (IC50=921.2 µg ml-1), being considered safe in case of acute oral exposure. The oil vapors completely inhibited the growth of Staphylococcus aureus (MIC=0.39 mg l-1), Candida albicans (MIC=0.83 mg l-1), Aspergillus brasiliensis (MIC=378 mg l-1) and Bacillus subtilis (MIC=704.8 mg l-1). This protocol offers an economical and efficient alternative for antimicrobial activity and safety determination of essential oils, can be used for the evaluation of several applications of essential oils, as disinfectant agent in inhabited environments.