High Volatility State Research Articles

The Driving Relationship of China Carbon Price Based on Different Market Volatility States

The China carbon market is a market-oriented designation for addressing climate issues. Price mechanism is the core of carbon market, studying on the price formation can promote the emission reduction targets. This paper conducts an autoregressive adjusted Markov model to divide the carbon price state, and designs a multiple regression model to test the driving mechanism. The results show the 2 order autoregressive Markov model of MS (2) - AR (2) model can classify the carbon price into high and low volatility states. Furthermore, in high volatility state, carbon price is only significantly positively correlated with macroeconomic factors of China Securities Index 300 (CSI300) and European carbon price of European emission allowance future contract (EUAF), while in low volatility state, carbon price is significantly positively influenced by energy market products JM future (JMF) and Oil, macroeconomic factor of CSI300, and European carbon price of EUAF. Furthermore, the impact strength is weaker than the whole sample regression results. The results provide reference for investors to judge carbon price and reveal price trends.

A state-dependent international CAPM for partially integrated markets: Using local and US risk factors

This study investigates the impact of emerging economies' trade levels with the US and exchange rate systems on their interdependency with the US market. We employ a comprehensive approach, analyzing both local factors (such as illiquidity and dividend yield) and US risk factors (including the S&P500 Index, US effective exchange rate, and term spread) to discern various market phases and capture equity returns. Utilizing a State-dependent International CAPM framework, we reveal a common trend among market returns: the reduced informativeness of both US and local variables during transitions from low to high volatility states. Notably, the majority of emerging markets respond to signals from the US equity market during bullish periods. We also highlight the critical role of exchange rate regimes in explaining the sensitivity of emerging markets to US risk factors. While the illiquidity ratio emerges as a significant local risk factor, its informativeness wanes during bear markets. These findings offer valuable insights for asset allocation, diversification, and risk management strategies tailored to the dynamic nature of emerging markets.

Do Exchange Rates Respond Asymmetrically to Crude Oil Market Shocks? Insights from BRICS and Pakistan

Abstract The shocks in the crude oil market are unlikely to have symmetric impacts on exchange rates, especially over various time periods. Furthermore, in low- and high-volatility regimes, exchange rates are predicted to respond differently to oil market shocks. By employing Markov-switching model and state-space model, this research empirically investigates whether oil market shocks exert asymmetric impacts on exchange rates, across the time and across different levels of volatility. In doing so, the study utilizes monthly data from January 1994-September 2017 for BRICS countries and Pakistan. The findings indicate that although oil supply shocks are mostly insignificant, oil price shocks appear statistically significant to affect the exchange rates whereas aggregate demand shocks are most volatile and are more likely to cause exchange rate fluctuations. The study concludes that various forms of oil shocks have a wide range of consequences on exchange rate determinations. Nonetheless, exchange rates’ response to oil shocks differ dramatically through low- and high-volatility states, implying that oil shocks exert a time-varying impact on exchange rates. Finally, the findings provide credence to the phenomenon of asymmetry in exchange rate responses to oil market shocks.

Quantifying the Impact of Risk on Market Volatility and Price: Evidence from the Wholesale Electricity Market in Portugal

This research aims to identify suitable procedures for determining the size of risks to predict the tendency of electricity prices to return to their historical average or mean over time. The goal is to quantify the sensitivity of electricity prices to different types of shocks to mitigate price volatility risks that affect Portugal’s energy market. Hourly data from the beginning of January 2016 to December 2021 were used for the analysis. The symmetric and asymmetric GARCH model volatility, as a function of past information, help to eliminate excessive peaks in data fluctuations. The asymmetric model includes additional parameters to separately obtain the impact of positive and negative shocks on volatility. The MSGARCH model is estimated to be in two states, allowing for transitions between low- and high-volatility states. This approach effectively represents the significant impact of shocks in a high-volatility state, indicating an acknowledgment of the lasting effects of extreme events on financial markets. Furthermore, the MSGARCH model is designed to obtain the persistence of shocks during periods of elevated volatility. Accurate price forecasting aids power producers in anticipating potential price trends and allows them to adjust their operations by considering the overall stability and efficiency of the electricity market.

The Asymmetric Effects of Real Estate Uncertainty Shock

In this paper, we explore the asymmetric impact of real estate uncertainty (REU) shock on the U.S. economy and housing market in high- and low-volatility states. To do this, we apply the Markov-switching vector autoregression model. Our empirical results show that the macroeconomic and housing markets react negatively to unexpected uncertainties, and the response to the REU shock is much greater than other housing-related variables in high-volatility states. We confirm the presence of the real-options channel based on the following results: both housing starts and construction employment decrease, and housing inventory increases, especially in low-volatility states. In the short run, we observe a delayed pattern in housing starts and construction employment variables. The effect of the channel is less significant in times of high volatility but confirmed in low volatility. The forecast error variance decomposition results show that the REU shock is greater under high REU volatility and plays a more significant role in explaining most housing-related variables.

Forecasting Sector-Level Stock Market Volatility: The Role of World Uncertainty Index

This study investigates the predictive ability of the World Uncertainty Index (WUI) on forecasting sector-level stock market volatility in the Shanghai Stock Exchange. The out-of-sample results demonstrate that the WUI improves volatility predictions, particularly in the materials, industrials, and health care sectors. Further analysis reveals the WUI's stronger predictive capability in high volatility states. The findings highlight the value of the WUI as a tool for anticipating and managing risk in specific sectors of the stock market.

Gold and crude oil: A time-varying causality across various market conditions

This paper examines the relationship between the returns and volatility of gold and crude oil markets using a novel procedure capturing both the quantile and time-varying Granger causality in mean and in variance, which is necessary to understand the complexity and multi-layered causal flow between these two strategic commodities. Using daily data from March 2nd, 2005, to February 7th, 2022, covering the global financial crisis and COVID-19 pandemic, the results are summarized as follows. Firstly, there is a bidirectional causal relationship in mean between gold and oil returns, regardless of the market conditions. Secondly, causality in variance varies across quantiles and mainly flows from gold to Brent during moderate to high volatility states of the volatility conditional distribution. Thirdly, the rolling-window causality-in-quantiles approach provides solid evidence that the return and volatility predictability between gold and oil markets is both time and quantile dependent, with evidence that the causal flows from Brent to gold are relatively stronger and span longer episodes than those running from gold to crude oil. The overall findings imply the unsuitability of applying a static quantile causality approach between these two strategic commodities, particularly over a long sample period, since the interactions between gold and crude oil markets tend to fracture suddenly under crisis periods. Accordingly, our main findings have important implications for portfolio diversification and hedging decisions.

Decomposed oil price shocks and GCC stock market sector returns and volatility

This study investigates dynamic mean and tail-based connectedness between decomposed oil price shocks (demand shock, supply shock, and risk shock) and Gulf Cooperation Council (GCC) stock returns and volatility at the aggregate and sectoral levels of stock data. The sample period is January 13, 2012 - January 14, 2022, covering calm and turbulent periods. The results of mean-based connectedness show generally low levels of connectedness over the full sample period for both return and volatility, despite some time-variation and irregularity in the patterns of connectedness across stock sectors. Total, directional, and pairwise connectedness measures surge significantly during Phase 1 of COVID-19, highlighting the pandemic's influence on the oil shocks–GCC stocks nexus. However, Phase 2 of COVID-19 displays a gradual reduction in return and volatility connectedness, which could be attributed to fiscal stimulus and COVID19 relief packages adopted by policymakers to contain the adverse impact of the virus on the economy. Quantile-based connectedness measures reveal evidence of heterogeneity in the impacts of oil shocks across the various quantiles of stock sector returns and volatility. Unlike supply shock, demand shock is a net transmitter of spillovers, irrespective of the quantiles. Volatility connectedness is stronger at the high volatility state than the moderate and low volatility states for all sectors, but strongest for the industrials sector. The results indicate that modelling oil shocks and GCC stock sector returns and volatility should concentrate on the most influential oil shocks (e.g. demand shock) affecting the lower and upper quantiles of sector returns and upper quantiles of sector volatility, especially for the industrials. The findings have implications for improving portfolio risk management and regulatory procedures, which should be necessary to address spillover effects from decomposed oil shocks in the stock markets of oil-exporters such as GCC countries.

Volatility spillovers between sovereign CDS and futures markets in various volatility states: Evidence from an emerging economy around the pandemic

We study quantile connectedness across the realized volatility of the 5-year Turkish CDS spreads and four futures contracts of USDTRY, EURTRY, XU030, and XAUTRY around the pandemic period. The procedure identifies, on average, the XU030 (EURTRY) stock index futures as the main net transmitter (receiver) of volatility shocks irrespective of subperiods. The level of total connectedness (i) fluctuates over time; (ii) is highly sensitive to major events; and (iii) strengthens in the high volatility state. The dynamic connectedness reaches a peak in December 2021, one day after the introduction of a new scheme, FX-protected deposit accounts, to address higher financial dollarization rates and lower the depreciation pressure on the lira. We find that investing in currency futures is very attractive, while XAUTRY futures have the highest reward-to-volatility. The hedging costs are highly related to changes in the infectious disease equity-market volatility tracker, geopolitical, and economic policy index for the US.

When Safe-Haven Asset Is Less than a Safe-Haven Play

AbstractWe propose a four-state regime-switching model that pairs low-volatility and high-volatility (HV) states to test eight stock–safe-haven asset portfolios’ risk properties. We find the correlations between gold, U.S. T-bond, and the Swiss franc and stock markets are negative or zero in all states, including the HV–HV state, while the correlations between Bitcoin (BTC) and stock markets are positive in the HV–HV state, implying that gold, T-bond, and the Swiss franc are full safe-havens and BTC is a partial safe-haven asset. Moreover, our model is effective in portfolio construction, performing better than conventional time-varying generalized autoregressive conditional heteroskedasticity-based models.

CORRELATION BETWEEN BITCOIN, STOCKS AND GOLD: A MARKOV REGIME-SWITCHING APPROACH

ABSTRACT The following article explores the correlation between bitcoin and both stocks and gold. A Markov regime-switching approach was used to identify and date two regimes in each of these financial assets. Stock returns are characterized by short-lived episodes of elevated volatility and negative returns whereas bitcoin returns are characterized by a persistent high volatility state with positive returns. Gold stayed in the low volatility period most of the time and only a few short-lived episodes of high volatility were identified during the first year of the pandemic. A concordance measure was computed to assess the synchronicity and correlation between the regimes. The regimes of bitcoin and gold are uncorrelated suggesting that bitcoin is not yet perceived as a safe haven like gold. The regimes of bitcoin and stocks were also uncorrelated suggesting that bitcoin may be used as a hedge against stocks. Keywords Bitcoin, correlation between bitcoin and stock market, correlation between bitcoin and gold, Markov regime-switching.

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

AbstractUnderstanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile‐based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward‐looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision‐making process of risk managers and policymakers.

Do investors herd in a volatile market? Evidence of dynamic herding in Taiwan, China, and US stock markets

This article investigates whether investors exhibit herd behavior in a high market volatility state. A modified herding model with the Kalman filter and GARCH methodology is used to estimate the time-variation herding corresponding to each influential event. Our proposed model provides comprehensive results for the relationship between investor herding and the market state, which has been argued in the previous literature. We find that investors indeed herd in volatile markets, including the 2001 dot.com bubble and the 2009 global financial crisis. However, in recent years, anti-herding is prevalent and herding is slight even in turbulent markets, such as the 2020 Covid-19 pandemic.

Interest rate uncertainty and sovereign default risk

Empirical studies suggest that fluctuations in the level and volatility of the world interest rate affect sovereign spreads in emerging economies. We incorporate an estimated time-varying process for the world interest rate (with both level and volatility shocks) into a model of sovereign default calibrated to a panel of emerging economies. Time variation in the world interest rate interacts with default incentives in the model and leads to state contingent effects similar to the empirical literature. On average, in response to a rise in the world interest rate the model delivers a 1.4 times increase in the spread. The volatility state has a major impact on this average – the increase in spreads is much larger in high volatility states. Moreover, we show that fluctuations in the world interest rate can generate considerable co-movement in sovereign yields across nations, as seen in the data.

Detecting Financial Crisis in Nigerian Capital Markets Using Markov Switching Generalized Autoregressive Conditional Heteroscedasticity

Financial crisis is often a thing of major concern in many countries. It has led so many experts to be interested in identifying the causes and the general statistical pattern associated with them. Based on the causes, pattern and characteristics exhibited by the financial crisis, it is expected that experts should be able to develop a model to detect the financial crisis. The financial crisis in Nigerian stock markets can be observed as a macro-economic indicator. Therefore, the aim of this study is to develop a model using a macro-economic indicator (All share index) that can explain the combined nature of volatility and switching characteristics associated with Nigerian capital markets. The data used for the study is the All Share Index (ASI) of Nigerian stock markets spanned from the month of January, 1985 – January 2021. The series is fitted to conditionally compound monthly return and the result obtained was used in fitting volatility and Markov switching GARCH models. Based on the AIC and SIC, the EGARCH model was found to be the appropriate model for modeling Nigerian stock markets. However, structural changes in the model were tested using the Chow breakpoint test. The results obtained based on the test of structural changes, found that there was a structural change in the real exchange rate in the period between February 1998 and July 2010. For MSGARCH (3,1.1), the results show that the conditional probability of surviving in the high volatility state in the next period is 0.984. Also, the conditional probability of surviving in a medium volatility state in the next period is 0.617 and the conditional probability of surviving in a low volatility state in the next period is 0.410. The results showed that the MSGARCH (3,1,1) model could explain that the period from February 1997 until July 2020 had probability values of more than 0.6. This reveals high volatility confirming the occurrence of a crisis

Herding behavior in the cryptocurrency market during COVID-19 pandemic: The role of media coverage

This paper examines herding behavior in the cryptocurrency market during the COVID-19 pandemic using daily data and based on static and regime-switching models. Furthermore, we investigate whether herding behavior is affected by the coronavirus media coverage. Based on a sample of the top-43 cryptocurrencies in terms of market capitalization between 2013 and 2020, we find significant evidence of herding for the entire sample period only during high volatility state. Moreover, during the COVID-19 crisis, results suggest that investors in the cryptocurrency market follow the consensus. Finally, the impact of coronavirus media coverage is significant on herding among investors, explaining such behavior in the cryptocurrency market during the COVID-19 crisis. Our findings explain herding determinants that may help investors avoid such comportment, mainly during the crisis.

Effects of economic policy uncertainty: A regime switching connectedness approach

This paper investigates the impact of economic policy uncertainty (EPU) in the US and China on international financial markets across different states. Although earlier studies have examined the interactions between EPUs and financial markets, few considered the issue from the state-dependence perspective. To fill this gap, we extend the Diebold and Yilmaz connectedness to the two-state Markov Switching Vector Autoregressive model to conduct the empirical analysis. Empirical results indicate connectedness is larger (smaller) in the high (low) volatility state than in the single-state model. Moreover, the two EPUs play distinct roles at different time scales. On the weekly time scale, EPUs are influenced by the financial markets only in the low volatility state while the China EPU becomes a net information sender in the high volatility state. Our findings imply that more attention should be devoted to the possible negative impact of China EPU.

Re-examining oil price-stock market returns nexus in Nigeria using a two-stage Markov regime switching approach

PurposeIt has been said that oil price shocks affect stock market returns. However, empirical studies remain inconclusive regarding the nexus between oil price shocks and stock market returns. Consequently, the purpose of this study is to investigate the asymmetric impact of oil price shocks on stock returns in Nigeria.Design/methodology/approachA two-stage Markov regime-switching approach is used to examine the asymmetric effects of three different structural oil shocks on stock returns. The oil shocks, which include oil supply shock, aggregate demand shock and oil-specific demand shock, are derived using structural vector autoregressive. Monthly data that spans the period between January 1990 and December 2018 are deployed for estimation.FindingsThe linear estimation results show that only oil demand shock negatively and significantly affects the stock market returns. The Markov-switching regime results reveal that oil supply shock has a significant positive impact on the stock returns in a low-volatility state, whereas oil-specific demand shock negatively impacts the stock returns in a high-volatility state.Practical implicationsThere is a need for policymakers and investors to take cognizance of not only the positive outcomes of a relatively stable state of oil price but also the negative consequences of a high-volatility state when formulating policy and making investment decisions, respectively.Originality/valueThis study differs from other similar studies in Nigeria that have examined the asymmetric relationship between oil price shocks and stock market return by using a two-stage Markov regime-switching approach. To the best of the authors’ knowledge, this is the first attempt at using this methodology.

Liquidity, Asset Price Volatility, and Monetary Policy Choices: Empirical Evidence from China

This article effectively identifies the high and low volatility state of asset prices in China by constructing the MS‐AR model, and further investigates the relationship between different dimensions of liquidity and asset price volatility. Moreover, we try to incorporate liquidity into the analytical framework and adopt the TVP‐SV‐VAR model to study the time‐varying characteristics between monetary policy, liquidity, asset price volatility and macroeconomy. The results are as follows: firstly, it shows that the high or low volatility state of China’s stock market and real estate market can be clearly divided, and display the consistency with the trend of asset price volatility. Secondly, liquidity has a strong ability to explain the high and low volatility state of asset prices, but it shows some hysteresis effects. Thirdly, the time‐varying results reveal that monetary policy has a regulating effect on liquidity, and the response cycle of quantitative monetary policy is relatively short, which reflects the effects of macroeconomy precisely. However, price‐based monetary policy has a longer response cycle and plays a vital role in the anticipatory adjustment and fine‐tuning of asset price volatility. These conclusions can provide an explanation for the attention to asset price bubbles and potential financial risks, and offer decision‐making references for the central bank to implement differentiated and dynamic monetary policy choices.

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In this article, the state of high volatility in the modern financial market emphasizes the need to make effective decisions from the process of private capital and debt capital formation in order to ensure the sustainable development of companies. This alone is the basis for an in-depth analysis of balance sheet data and financial reports of enterprises. We can see that the capital category is correctly defined in the article and the principles of capital optimization based on the principles of financial management are defined.