High 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.

Exploring asymmetries in cryptocurrency intraday returns and implied volatility: New evidence for high-frequency traders

This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders.

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.

Flight-to-safety across time and market conditions

This paper examines the time-varying directional predictability from the implied volatility of risky assets, such as US stocks, to the implied volatility of less risky or safe haven assets, such as gold, cryptocurrency, foreign exchange rates, and US Treasury notes, which allows for making time and regime dependent inferences on the flight-to-safety phenomenon. The results show a positive directional predictability from the VIX to the implied volatility of gold, cryptocurrency, and foreign exchange rates in periods of high market uncertainty such as the COVID-19 outbreak and the war in Ukraine. The volatility of US Treasury notes has a different response dynamic to the VIX, switching to negative cross-correlations in some cases, given its tight link to inflationary pressure and economic policy uncertainty. The main results remain qualitatively unchanged after incorporating economic policy uncertainty and oil volatility. An additional analysis indicates that flight-to-safety is not exclusively triggered by the high volatility regime in US equities but happens at median levels too. Our findings have important implications for investors, risk managers, and policymakers regarding volatility predictability, hedging, and market stability.

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.

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.

Catastrophe risk in a stochastic multi‐population mortality model

AbstractThis paper incorporates mortality shocks in the scenarios for future mortality rates produced by a stochastic multi‐population mortality model. Hereto, the proposed model combines a decreasing stochastic mortality trend with a mechanism that switches between regimes of low and high volatility. During the high volatility regimes, mortality shocks occur that last from one to several years and temporarily impact the mortality rates before returning to the overall mortality trend. Furthermore, we account for the age‐specific impact of these mortality shocks on mortality rates. Actuaries and risk managers can tailor this scenario generator to their specific needs, risk management objectives, or supervisory requirements. The generated scenarios allow (re)insurers, policymakers, or actuaries to evaluate the effects of different catastrophe risk scenarios on, for example, the calculation of solvency capital requirements.

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.

Can a self-exciting jump structure better capture the jump behavior of cryptocurrencies? A comparative analysis with the S&P 500

This paper introduces a stochastic volatility model with an independent self-exciting jump structure model (SE-SVIJ) to capture the jump dynamics of cryptocurrency daily returns. The empirical results show that the SE-SVIJ model can provide a less volatile and less persistent volatility process. We find clear evidence of self-exciting jump clustering in the cryptocurrency market. The SE-SVIJ model identifies more jumps than the stochastic volatility model with independent jumps (SVIJ). We also conduct a comparison between the cryptocurrency and S&P 500 markets in terms of jump behavior: S&P 500 returns have more frequent negative jumps and larger negative jumps on average. In comparison, the cryptocurrency markets suffer from positive jumps continuously, and also suffer from an interaction effect between positive and negative jumps during the high-volatility regime. We also demonstrate the effectiveness of the SE-SVIJ model in modeling cryptocurrency return and volatility, as reflected by volatility comparisons and tail fitting.

The asymmetric impact of oil market shocks on the time-varying connectedness and spillover of financial stress

ABSTRACT Applying a novel time-varying parameter VAR (TVP-VAR) extended joint connectedness approach this study first examines the connectedness and spillover of financial stress among twelve large Asian economies. The finding shows that the usually high connectedness of the financial stress among these economies was further elevated during the Asian financial crisis and global financial crisis but not significantly during COVID-19 pandemic. The direction of spillover depends on the level of economic development. Developed economies such as South Korea, Singapore and Hong Kong are significantly net transmitters of financial stress to other Asian economies whereas countries such as Pakistan and Indonesia are significantly net receivers. Applying linear and non-linear Markov-switching models this study further examines the impact of oil-supply, and consumption- and speculation-led oil-demand shocks on the dynamic connectedness of financial stress indexes. In the linear model only a speculation-led increase in demand for oil increases the connectedness. However, the Markov-switching model reveals that in the high-volatility regime all types of oil shocks influence the connectedness of the financial stress among these economies. This emphasizes the elevated role of oil market in transmitting financial stress across economies during the time of economic turmoil.

An EM/MCMC Markov-Switching GARCH Behavioral Algorithm for Random-Length Lumber Futures Trading

This paper tests using two-regime Markov-switching models with asymmetric, time-varying exponential generalized autoregressive conditional heteroskedasticity (MS-EGARCH) variances in random-length lumber futures trading. By assuming a two-regime context (a low s=1 and high s=2 volatility), a trading algorithm was simulated with the following trading rule: invest in lumber futures if the probability of being in the high-volatility regime s=2 is lower or equal to 50%, or invest in the 3-month U.S. Treasury bills (TBills) otherwise. The rationale tested in this paper was that using a two-regime Markov-switching (MS) algorithm leads to an overperformance against a buy-and-hold strategy in lumber futures. To extend the current literature in MS trading algorithms, two location parameter scenarios were simulated. The first uses an unconditional mean or expected value (no factors), and the second incorporates market and behavioral factors. With weekly simulations form 2 January 1994 to 28 July 2023, the results suggest that using MS-EGARCH models in a no-factors scenario is appropriate for active lumber futures trading with an accumulated return of 158.33%. Also, the results suggest that it is not useful to add market and behavioral factors in the MS-GARCH estimation because it leads to a lower performance.

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.

Dynamic Information Regimes in Financial Markets

We develop a model of investor information choices and asset prices in which the availability of information about fundamentals is time-varying and responds to investor demand for information. A competitive research sector produces more information when more investors are willing to pay for that research. This feedback, from investor willingness to pay for information to more information production, generates two regimes in equilibrium, one having high prices and low volatility, the other the opposite. The low-price, high-volatility regime is associated with greater information asymmetry between informed and uninformed investors. Information dynamics move the market between regimes, creating large price drops even with no change in fundamentals. In our calibration, the model suggests an important role for information dynamics in financial crises. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.01213 .

The asymmetric volatility spillover across Shanghai, Hong Kong and the U.S. stock markets: A regime weighted measure and its forecast inference

Based upon generalized spillover index method under Markov switching vector auto-regression (MS-VAR) framework, this paper aims at a regime dependent measure of the volatility spillover asymmetry across Shanghai, Hong Kong and the U.S. stock markets. For that, we apply a regime weighted measure of spillover and its asymmetry. The empirical results show that there existed evident negative asymmetric effect of spillover across the three markets, and it was regime dependent and time-varying: it got intensified under high volatility regime and turned out stronger after the Program of Shanghai-Hong Kong stock connect. The regime weighted measure of spillover asymmetry is found to be reliable; it could be explained appropriately with the economic policy uncertainty and investors' fear in the U.S., extreme shocks like crash of Shanghai stock market and COVID-19 as well as the capital flow through the Program. From in-sample evidence and out-of-sample forecast of Shanghai stock market, we infer that traditional forecast models could be improved if regime dependent spillover and its asymmetry are considered as additional factors.

Return-volatility relationships in cryptocurrency markets: Evidence from asymmetric quantiles and non-linear ARDL approach

Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.

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.

Geopolitical risk on energy, agriculture, livestock, precious and industrial metals: New insights from a Markov Switching model

This paper delineates a novel approach to analyze the dynamic effects of a Geopolitical Risk (GPR) shock on five types of commodities (energy, precious metals, agriculture, industrial metals, and livestock products). Covering a period of 10 years, from 2013 to 2023, we use a Markov-Switching model with two regimes (low and high volatility). The findings indicate that a Markov-Switching model with a t-distribution for the errors is the most suitable to analyze the impact of GPR shocks on commodity markets. All commodities react to a GPR shock but differently. The energy market is the most reactive market and livestock is the less sensitive one. Our results suggest that investors may want to consider the impact of geopolitical risk on different types of commodities before making investment decisions. Market participants should pay attention to changes in the Geopolitical Risk Index during high volatility regimes to better understand the behavior of commodity markets.