Low Volatility Regime Research Articles

SOCIAL MEDIA SENTIMENT AND STOCK MARKET VOLATILITY: EVIDENCE FROM THE US HI-TECH COMPANIES

Purpose: This study aims to investigate the effects of social media sentiment, measured by Twitter-based uncertainty index, on the stock market volatility of the US technology companies. Design/methodology/approach: Methodologically, we employ the quantile regression model. Our findings reveal that the volatility of Amazon, Apple, Google and IBM stocks are sensitive to the variations in twitter-based economic and market uncertainties (i.e., TEU and TMU indexes). Findings: We observe significant influences at both lower and upper quantiles. Thus, for both high and low volatility regimes, the information on twitter-based uncertainty indexes can be used to predict the market volatility of these leading hi-tech companies. Moreover, TEU and TMU indexes exert positive effects on the stock price implied volatility implying that the variance of these technology firms experiences an upward trend as the social media uncertainty rises. Originality/value: While numerous studies have focused on the influence of social media (e.g., Facebook, twitter etc.) on investment strategies, the impact of twitter sentiments on the risk linked to hi-tech firms remains understudied. Hence, investors participating in the technology sectors could use our findings for managing portfolio risk.

Asset prices, regime switches, and monetary policy: evidence from China

ABSTRACT This paper develops a regime-switching DSGE model that can capture time-varying volatilities in economic activity and monetary policy. The model is then used to study regime switches in macroeconomic dynamics and monetary policy in China. The results suggest a better fitness of the regime-switching model than conventional constant-parameter models. We also find notable differences in macroeconomic dynamics and monetary policy reactions between high and low volatility regimes. Further counterfactual analysis suggests that, at least in an environment with regime-switching volatilities, the optimal reaction of monetary policy involves responses to asset price variations. The findings of the paper shed new light on the regime-switching properties of the economy and the optimal choice of monetary policy within this context.

Forecasting sovereign CDS spreads with a regime‐switching combination method

AbstractWith the growing importance of the sovereign credit default swap (CDS) market, accurate forecasting of sovereign CDS spreads has gained significant attention. In view of the complex volatility in the series of sovereign CDS spreads, this study presents a novel combination forecasting framework, which introduces time‐varying weights to effectively combine diverse individual models. To identify optimal subsets of models, a mutual information approach is employed, while the regime‐switching method is utilized to integrate the selected models. The proposed method's efficacy is validated using data from 65 countries. Empirical findings underscore the superiority of the proposed approach over benchmark models in terms of both horizontal and directional prediction accuracy, particularly when the sovereign CDS data exhibits a balanced distribution between high and low volatility regimes.

Are clean energy markets hedges for stock markets? A tail quantile connectedness regression

Acknowledging the long-term potential of alternative energy sources, this paper examines the quantile frequency connectedness between clean energy markets and international stock markets, with implications related to hedging effectiveness. The main results point out that spillovers run from the US, the EU, the UK, and the Renewable Energy and Clean Technology Index to Japan and the Global Clean Energy Index. Furthermore, while the transmissions are concentrated in the short run during normal (0.5) and bull market (0.95) conditions, they extend to intermediate and long-term amid busting market (0,05 quantile) states, signifying a long-lasting impact that cannot be absorbed in the short run. Notably, clean energy index roles in information transmissions range from a net sender (Renewable Energy and Clean Technology Index), isolated (Green Bond Index), and a net receiver (Global Clean Energy Index). From a multivariant portfolio design perspective, we notice that a substantial weight should be allocated to clean energy assets, WTI, and CSI300 when compared with the rest of the financial markets. Moreover, the low (high) volatility regime yields lower (higher) weights than the ones reported in the mean state, but the results remain largely similar. Bivariant portfolio weights show that GB should have substantial weight when paired with all assets.

American put options with regime-switching volatility

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

Optimal portfolio selection with volatility information for a high frequency rebalancing algorithm

We propose a high-frequency rebalancing algorithm (HFRA) and compare its performance with periodic rebalancing (PR) and threshold rebalancing (TR) strategies. PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals, whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target allocation. The HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy, and the profitability of the HFRA is examined to determine the optimal portfolio size. The HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility regimes. Using cointegrated price data, it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility environment. For low-volatility regimes, although increasing portfolio size marginally enhances the HFRA’s profitability, the profits of portfolios of varied sizes do not significantly differ. It is demonstrated that when volatility is relatively high and the trend is upward, the HFRA can yield a substantial return via portfolios of large sizes. Moreover, the profitability of the HFRA is compared with that of the PR and TR strategies for long-term application. The HFRA is more profitable than the PR and TR strategies. This achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.

Markov-switching multifractal volatility spillovers among European stock markets during crisis periods

ABSTRACT This research investigates time-varying volatility spillovers and connectedness among European stock markets during the COVID-19 pandemic and the Russia – Ukraine war, two events that destabilized global markets. With data from 20 European stock markets spanning 17 December 2019, to 17 March 2022, we employ the TVP-VAR model and estimate volatility using the Markov-switching multifractal volatility technique. Findings from log-volatility estimates suggest that markets are highly connected, with price movement driven mainly by spillover effects from other markets in the same region. Most emerging markets are net receivers of volatility, with most of Europe’s major markets being net transmitters of shocks. The COVID-19 pandemic appears to have impacted European stock markets more than the Russia – Ukraine war. Shifting to the results obtained based on MSM volatility estimates, we find that markets strongly correlate for both high and low volatility. In the case of a high volatility regime, we document the dominance of Finland, Denmark, and Iceland over major European markets. In contrast, under a low volatility regime, we note the dominance of major markets, including the UK and France, over emerging markets in Europe. The findings reveal the diversification potential of emerging European stock markets.

Switching spillovers and connectedness between Sukuk and international Islamic stock markets

This study examines the switching volatility spillovers and connectedness between the Dow Jones Sukuk and global and two regional Islamic stock markets (World, Asia, and Europe). Using the Markov switching-vector autoregression and spillover index models, we show that Islamic stock markets are significantly affected by their own shocks under the low-volatility regime (Regime 1), whereas the results are insignificant under the high-volatility regime (Regime 2), except for the World Islamic markets. Similarly, Sukuk is affected by its own shocks, and the magnitude of the spillover increases in a high-volatility regime. Sukuk has an insignificant impact on Islamic stocks in Regime 1 and the effect becomes significant in Regime 2. Moreover, the likelihood of transition is more persistent in Regime 1 than in Regime 2. In addition, the shifts from one regime to another are more persistent than those in the opposite sense. Furthermore, the World Islamic stock markets are the net contributors of spillover to Sukuk for all regimes, and their capacity for spillover increases in Regime 2, whereas Sukuk is the net receiver of spillover for both regimes. In contrast, the ability of the European and Asian Islamic markets as net contributors decreases in Regime 2, and the contribution of Sukuk spillovers increases. Portfolio risk analysis under low- and high-volatility regimes is also discussed.

Crude Oil Price Movements and Institutional Traders

We analyze the role of hedge fund, swap dealer, and arbitrageur activity in the crude oil market. The contribution of our work is to examine the role of institutional traders in switching between high-volatility and low-volatility regimes. Using confidential position data on institutional investors, we first analyze the linkages between trader positions and fundamentals. We find that these institutional position changes reflect fundamental economic factors. Subsequently, we adopt a Markov regime-switching model with time-varying probabilities and find that institutional position changes contribute incrementally to the probability of regime changes.

Asian stock market volatility and economic policy uncertainty: The role of world and regional leaders

This paper investigates the relative influence of the United States, China, Japan and the European Union on Asian stock markets from the perspective of economic policy uncertainty (EPU). The empirical setting assumes that stock markets evolve through high- and low-volatility regimes with a probability driven by EPU in these leader countries. The main result of the paper indicates that Chinese EPU surpasses Japanese and US EPU as the main driver of Asian stock market volatility, except in the South Korean and Hong Kong stock markets. Moreover, Chinese policy-specific uncertainty indices, such as monetary, fiscal, trade and exchange rate policies play a significant role but in the most developed financial markets only. These result provides further evidence that China’s economic and financial ties with its near neighbors are deepening, which has important implications for the regional institutions tasked with promoting and monitoring regional financial stability.

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

AbstractThis study investigated the volatility linkages between energy and agricultural futures, including possible causes for these comovements, such as external macroeconomic and financial shocks during low and high volatility regimes. A combination of Markov‐switching regressions and quadrivariate VAR–DCC–GARCH and VAR–BEKK–GARCH modeling revealed that external shocks have an asymmetric effect on the relationship of these assets with higher cross‐correlations reported during high volatility regimes. This comovement effect outweighs the substitution effect between energy and agricultural products. Furthermore, the quadrivariate VAR–BEKK–GARCH model provides strong evidence of a bidirectional price volatility spillover between the agricultural and energy markets during periods of high volatility. Overall, the results suggest that energy futures can be effectively used for hedging in a portfolio comprising agricultural futures (and vice versa), while a combination of macroeconomic and financial index futures can serve as an effective hedging tool in investment portfolios comprising both energy and agricultural commodities.

Does Risk Return Trade-off Hold in Base Metal and Bullion Commodity Assets? A Bayesian Change Point Analysis

The risk-return trade-off is fundamental to portfolio investing whose stability is critical for portfolio optimisation. Since the relationship is dynamic, the portfolio manager should know the point of change and, thereafter duration of the changed period with certainty. First, we have done Bayesian change point analysis, and then based on the analysis, the study identifies the regimes having equal statistical variance along with the corresponding average return in two most popular commodities, that is, copper and gold. It is found that risk-return trade-off is not stable. Further, in a higher volatility regime, only gold can be considered as a diversifiable commodity because a positive risk-return trade-off holds. But in a low volatility regime, both commodities lose their diversification properties as the risk-return relation becomes negative.

The influence of the Ukraine-Russia conflict on renewable and fossil energy price cycles

This study investigates the impact of the Ukraine-Russia conflict on energy cycles by extending a business cycle-based approach in the energy sector. We employ the Markov Switching Bayesian Vector Autoregressive (MS-BVAR) model to derive filtered probabilities that represent energy cycles. By utilizing the Russia Economic Policy Uncertainty Index and dummy variables derived from Google search data as conflict indicators, we conduct regression analysis and examine the generalized impulse response function to assess the effects of the conflict on energy cycles. Our research reveals notable differences in renewable and fossil energy price cycles. The renewable price cycle exhibits greater persistence, indicating a limited response to the conflict shock. In contrast, the fossil energy price cycle exhibits more significant and enduring adjustments in high- and low-volatility regimes. These findings highlight the heterogeneous impacts of the Ukraine-Russia conflict on the fossil energy market, indicating potential disruptions in supply chains and market sensitivities. Notably, fossil energy prices demonstrate higher responsiveness to the Ukraine-Russia conflict across market conditions.

Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products

This study investigates the role of investor attention to the price of petroleum products (APPP) in forecasting Chinese stock market volatility. In the in-sample analysis, we find that a higher APPP index can lead to higher stock market volatility in the next month. The out-of-sample results show that the APPP index has strong predictive power for Chinese stock market volatility. Moreover, the volatility predictability of the APPP index is stronger than that of all commonly used economic and economic policy uncertainty predictors. Furthermore, when compared with the commonly used economic and economic policy uncertainty predictors, we find that the APPP index contains more valuable information for predicting stock market volatility than traditional predictors. When performing two robustness tests, alternative forecasting windows and lags of RV, the results are still robust. Further analysis indicates that the forecasting power of the APPP index for stock market volatility concentrates on the low-volatility regime.

THE IMPACT OF DISRUPTIVE TECHNOLOGY ON BANKING UNDER SWITCHING VOLATILITY REGIMES

This paper uses the case of Spain to investigate whether and how disruptive technology impacts banking stock returns under a high volatility regime and a low volatility regime. For this purpose, a two-factor model with heteroscedastic Markov switching regimes has been applied. The results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent, having a relevant positive impact in high volatility regimes and a less relevant negative impact in low volatility regimes. These findings suggest that investors are informed about and acknowledge the advantages of disruptive technologies and will use their adoption as a business strategy to offset adverse market circumstances. During stable market conditions, on the other hand, Spanish banking seems to have less expectations about disruptive technology as a business strategy. To summarise, this paper provides insights into the role of the pricing of banking-related assets and has other relevant implications for investors that include disruptive technology or banking exposed investments in their portfolios.

The effect of macroeconomic regimes, uncertainty, and COVID-19 outcomes on commodity price volatility: implications for green economic recovery

COVID-19 adversely affected all market mechanisms, shocked economies, halted business operations, and disrupted supply chains. This study examines macroeconomic instability by assessing the commodities market in highly traded regimes using descriptive statistical analysis, the Economic Policy Uncertainty (EPU) Index, and Markov Regime Swapping (MRS) for data covering February 26 to November 30, 2020. The findings indicate that most commodities respond to stock values in both high and low-volatility regimes of supply and demand. Additionally, commodity prices are still in a low volatility regime due to COVID-19-related market uncertainty. The returns for oil, corn, gold, copper, natural gas, silver, and soybeans adapt to low macroeconomic uncertainty and variable rankings for COVID-19. In contrast, changes in COVID-19 deaths had no impact on natural gas or oil under any regime. As agricultural commodities and valuable metals have less negative feedback to exogenous variables than any other commodities, this model is useful for regulators and stockholders. The results suggest that in a global pandemic, portfolio managers and other stakeholders should use priceless metals like gold as a short-term hedge against systemic market risks to achieve macroeconomic stability and facilitate a rapid recovery in the green economy.

Dancing with dragon: The RMB and developing economies’ currencies

We analyse Chinese RMB co-movements with the currencies of other developing economies using daily data from January 1, 2006 to December 31, 2020. We find that the RMB plays an important role in East Asia & Pacific. Bilateral trade significantly increases the probability of RMB co-movements with other currencies while inflation differential decreases it. Additionally, the currencies of the economies that are more inclined to adopt a pegging system are less likely to co-move with the RMB. We further divide the sample into three sub-periods based on two major China’s currency reforms and the results are consistent with our main finding. We also investigate the nonlinear determinants of RMB co-movements in high and low volatility regimes, respectively, and show the different patterns. Last but not least, we find that RMB currency swap and the Belt and Road Initiative amplify RMB co-movements in larger and more developed economies.

Nonlinear Contagion and Causality Nexus between Oil, Gold, VIX Investor Sentiment, Exchange Rate and Stock Market Returns: The MS-GARCH Copula Causality Method

The fluctuations in oil have strong implications on many financial assets not to mention its relationship with gold prices, exchange rates, stock markets, and investor sentiment. Recent evidence suggests nonlinear contagion among the factors stated above with bivariate or trivariate settings and a throughout investigation of contagion and causality links by taking especially nonlinearity into consideration deserves special importance for the relevant literature. For this purpose, the paper explores the Markov switching generalized autoregressive conditional heteroskedasticity copula (MS-GARCH—copula) and MS-GARCH-copula-causality method and its statistical properties. The methods incorporate regime switching and causality analyses in addition to modeling nonlinearity in conditional volatility. For a sample covering daily observations for 4 January 2000–13 March 2020, the empirical findings revealed that: i. the incorporation of MS type nonlinearity to copula analysis provides important information, ii. the new method helps in the determination of regime-dependent tail dependence among oil, VIX, gold, exchange rates, and BIST stock market returns, in addition to determining the direction of causality in those regimes, iii. important policy implications are derived with the proposed methods given the distinction between high and low volatility regimes leads to different solutions on the direction of causality.

Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method

The purpose of this paper is to explore whether the categorical Economic Policy Uncertainty (EPU) indices are predictable for the volatility of carbon futures, in the mixed data sampling (MIDAS) regression framework. The prediction methods include the MIDAS-RV model, the MIDAS models extended by individual categorical EPU index, combination prediction approaches, the MIDAS models extended by dimensionality reduction techniques as well as the machine learning methods on the basis of MIDAS model and Markov regime switching method. We find firstly that categorical EPU indices are predictable for carbon futures volatility, but the predictive power of individual categorical EPU indices is not robust. Secondly, machine learning methods, especially the machine learning method considering the Markov regime switching structure, help to obtain valid information from multiple categorical EPU indices and produce robust and superior prediction accuracy for carbon futures volatility. The results of the extension analysis also found that machine learning methods, especially the machine learning method considering the Markov regime switching structure help to produce higher investment performance and more accurate long-term carbon futures volatility forecasts. Meanwhile, we also find the advantages of the MIDAS based machine learning methods over the traditional AR based machine learning methods. Finally, the forecasting performance of the machine learning method which considering Markov regime switching structure are superior during both the low and high volatility regimes and even during the COVID-19 pandemic.

Regime specific spillover between Dow Jones Islamic Market World Index, Islamic gold-backed crypto currencies and the blockchain halal index

PurposeThe purpose of this paper is to explore the relationship between Dow Jones Islamic Market World Index, Islamic gold-backed cryptocurrencies and halal chain in the presence of state (regime) dynamics.Design/methodology/approachThe authors have used the Markov-switching model to identify bull and bear market regimes. Moreover, the dynamic conditional correlation, the Baba, Engle, Kraft and Kroner- generalized autoregressive conditional heteroskedasticity and the wavelet coherence models are applied to detect the presence of spillover and contagion effects.FindingsThe findings indicate various patterns of spillover between halal chain, Dow Jones Islamic Market World Index and Islamic gold-backed cryptocurrencies in high and low volatility regimes, especially during the COVID-19 pandemic. Indeed, the contagion dynamics depend on the bull or bear periods of markets.Practical implicationsThese present empirical findings are important for current and potential traders in gold-backed cryptocurrencies in that they facilitate a better understanding of this new type of assets. Indeed, halal chain is a safe haven asset that should be combined with Islamic gold-backed cryptocurrencies for better performance in portfolio optimization and hedging, mainly during the COVID-19 period.Originality/valueTo the best of the authors’ knowledge, this paper is the first research on the impact of the halal chain on the Dow Jones Islamic Market World Index return, Islamic gold-backed cryptocurrencies returns in the bear and bull markets around the global crisis caused by the COVID-19 pandemic.