Low Volatility Regime Research Articles

Stock market volatility and exchange rate movements in the Gulf Arab countries: a Markov-state switching model

Purpose This paper aims to investigate the dynamic linkage between stock prices and exchange rate changes for the Gulf Arab countries (Kuwait, Qatar, Saudi Arabia and United Arab Emirates [UAE]). Design/methodology/approach The author uses the Markov-switching autoregression to detect regime-shift behavior in the stock returns of the Gulf Arab countries and Markov-switching vector autoregressive (MS-VAR) model to capture the dynamic interrelatedness between exchange and stock returns over the period 2000–2018. Findings This study’s analysis finds evidence to support the persistence of two distinct regimes for all markets, namely, a low-volatility regime and a high-volatility regime. The low-volatility regime illustrates more persistence than the high-volatility regime. Specifically, exchange rate changes do not have an influence on the stock market returns of the Gulf Arab countries, regardless of the regimes. On the other hand, stock market returns have a substantial impact on exchange markets for all countries, except Saudi Arabia, and it is more noticeable during the regime of high volatility. Practical implications The findings shed light on the interconnectedness between two of the most important financial markets in the complex international financial environment. They are thus of particular interest for economic policymakers and portfolio investors. Originality/value The author distinguishes this study from previous studies in several ways. First, while previous empirical studies of the dynamic linkage between stock prices and foreign exchange markets are primarily devoted to developed markets or emerging markets, this study’s interest is concentrated on four Gulf Arab financial markets (Kuwait, Qatar, Saudi Arabia and UAE). Second, unlike most investigations in the literature that only estimate this link for the whole period, this study attempts to estimate during the good and bad period by using a two-regime MS-VAR model. To the best of the author’s knowledge, this is the first study of the Gulf Arab countries on the stock and foreign exchange markets to apply this model.

Understanding the relationship between public and private commercial real estate markets

ABSTRACT This paper provides a modelling framework to examine the very low correlation at short horizons and high correlation at long horizons between private and public commercial real estate returns. For this purpose, we use a correlated, unobserved component model with a common trend and Markov-switching heteroskedasticity. This model decomposes the public and private commercial real estate prices into a common trend and interdependent cycles. The proposed model is able to endogenously capture low and high volatility regimes in real estate markets. More importantly, our model shows that the low correlation observed at short horizons between the public and private real estate markets is mainly due to the absence of any correlation in low-volatility regimes. On the other hand, the cycles, or short-run movements, in these two markets are highly correlated in high-volatility regimes.

Intertemporal asset pricing with bitcoin

This paper develops and tests an intertemporal regime-switching asset pricing model characterized by heterogeneous agents that have different expectations about the persistence and volatility of bitcoin prices. The model is estimated using daily bitcoin price data from 2013 until 2020 whereby three types of agents are considered: mean–variance optimizers, speculators and fundamentalists, respectively. While mean–variance optimizers trade on the basis of conditional first and second moments of the return distribution, speculators engage in trend chasing and buy when prices are rising and sell when prices are declining. Fundamentalists trade on the basis of fundamental factors that can impact the value of bitcoin. The fractions of agents engaging in one strategy over another shows statistically substantial variation during high and low bitcoin price volatility regimes. Estimation results reveal the following. First, unlike in traditional asset classes, there is evidence of mean–variance optimizers. Second, there is evidence of speculators who engage in ‘bandwagon behavior’ and buy bitcoins during price appreciations and sell bitcoins during price declines. Finally, there is evidence of fundamentalists who trade bitcoins when fundamental factors deviate from their long-run trends. Remarkably, these fundamentalists exhibit contrarian-type behaviors during low price volatility regimes while behaving more like fundamental traders during high price volatility regimes.

Uncertainty and herding behavior: evidence from cryptocurrencies

The aim of this study is to examine the existence of herding behavior in the cryptocurrency market under uncertainty by employing cross-sectional absolute deviation (CSAD) of returns, ordinary least squares (OLS), generalized autoregressive conditional heteroscedasticity (GARCH) methods and Time-Varying Markov-Switching (TV-MS) model for both overall sample and sub-periods which was determined based on the results of Quandt-Andrews and Bai-Perron breakpoint tests. We utilized the daily data of the 14 leading cryptocurrencies in terms of closing price, market cap and transaction volume. We also used dummy variables to analyze whether or not an asymmetric behavior occurred during the "up and down" market periods. Our results for the overall sample refer to an anti-herding behavior in each model. However, the results of the TV-MS model for the 3rd sub-period (2/28/2017−1/16/2018) imply the existence of a herding behavior in the low volatility regime, an anti-herding behavior occurred during the high volatility regime and the effect of uncertainty was significant on the anti-herding behavior. Finally, our results suggest that there was no significant asymmetric behavior during the "up and down" market periods.

Determining the Interaction of the International Portfolio Flows with Exchange Rate Volatility in Developing Countries

The aim of this study is to examine the effect of the bond portfolio and equity inflows on the exchange rate dynamics for a set of developing countries, including Turkey, Hungary, New Zealand, India, Russia, Poland, Brazil and Argentina over the period 1997:01-2017:12 by using a Markov-switching model. According to the analysis results, the net bond inflows lead to an increase in the likelihood of a high volatility regime in Turkey and Russia and increases the probability of transition from the high volatility regime to the low volatility regime in Hungary. Additionally, the net bond inflows from New Zealand and Poland to the United States (US) rise the possibility of remaining in the low volatility regime. The net equity inflows from Turkey and Poland to the US lead to a rise in the possibility of remaining in the high volatility regime. Besides, the net equity inflows from Brazil and Argentina to the US lead to a decline in the possibility of remaining in the low volatility state. In the light of the empirical results supporting the return chasing hypothesis, this paper argues that credit controls on short-term financial inflows could be an effective means in stabilizing the foreign exchange market.

Do green investments react to oil price shocks? Implications for sustainable development

This study investigates whether green investments are connected to oil price shocks. While earlier papers mainly investigate the linkage between clean energy stock and crude oil prices, we consider recently introduced environmental indexes consisting of companies that use eco-friendly production processes and develop green infrastructures. Using the Markov regime switching regression approach, we observe that the effect of crude oil prices on environmental investments appears to be statistically insignificant, albeit positive in most of the cases. One possible reason behind such a finding is that the oil-dependence is limited to the case of eco-friendly firms. The results further indicate switching between low and high volatility regimes implying that there exist high and low volatility states for green assets. Given that growing concerns about climate change and energy security would inspire market participants shift towards ethical investments, our results could be of interest to investors aiming at decarbonizing their portfolios by including more environmental assets. One important finding of our empirical analysis is that green assets are found to be more susceptible to oil market volatility rather than to oil price fluctuations. This is a novel finding given that prior literature focusing on the association of oil sectors and green assets mainly consider the information on oil prices rather than oil volatility. Hence, our study aims to conceal an important void in the exiting literature.

Price spillovers between rare earth stocks and financial markets

We study price transmission between rare earth stocks and the base metals, gold, clean energy, oil and global MSCI stock markets. Using a Markov switching vector autoregressive model that accounts for different volatility regimes, our empirical findings reveal that price connectedness between rare earth (RE) and other stock markets differs across volatility regimes. Specifically, in a low-volatility regime: (a) RE stocks are linked to the base metals market, receiving and transmitting sizeable price spillovers, and (b) RE stocks are weakly connected with clean energy, gold, oil and general stock markets. In contrast, in a high-volatility regime, there is increased RE price co-movement with price oscillations in the clean energy, oil and general stock markets. These findings have implications for hedging risk associated with positions in RE companies using other financial assets.

Dynamic volatility spillover effects between oil and agricultural products

The strong volatility spillover between crude oil and agricultural commodity markets reduces the diversification benefits and implies costly risk management process faced by portfolio managers and agricultural producers. This paper proposes a comprehensive study of their dynamic implied volatility spillover effects after the global financial crisis 2008–2009, while considering the transition between oil volatility's regimes. By using implied volatility, our analysis emphasizes on the forward-looking information that market traders usually convey in making decisions. We employ the generalized spillover indices within a fractionally integrated VAR model to capture the dynamic patterns of the volatility spillover effects alongside the Markov Switching Autoregressive model to extract the regimes of oil. Our results show new evidence that the net volatility spillover effect from crude oil to all agricultural commodities tends to decrease when crude oil remains in its low volatility regime. Conversely, this effect experiences an increasing trend when crude oil remains in its relatively high volatility regime. A dynamic strategy that combines oil and the most balanced agricultural commodity in terms of volatility transmission with oil (i.e., close-to-zero net volatility spillovers) depending on oil's regimes consistently outperforms the buy and hold strategy in terms of information ratio.

Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching

The purpose of this paper is to investigate the role of regime switching in the prediction of the Chinese stock market volatility with international market volatilities. Our work is based on the heterogeneous autoregressive (HAR) model and we further extend this simple benchmark model by incorporating an individual volatility measure from 27 international stock markets. The in-sample estimation results show that the transition probabilities are significant and the high volatility regime exhibits substantially higher volatility level than the low volatility regime. The out-of-sample forecasting results based on the Diebold-Mariano (DM) test suggest that the regime switching models consistently outperform their original counterparts with respect to not only the HAR and its extended models but also the five used combination approaches. In addition to point accuracy, the regime switching models also exhibit substantially higher directional accuracy. Furthermore, compared to time-varying parameter, Markov regime switching is found to be a more efficient way to process the volatility information in the changing world. Our results are also robust to alternative evaluation methods, various loss functions, alternative volatility estimators, various sample periods, and various settings of Markov regime switching. Finally, we provide an extension of forecasting aggregate market volatility on monthly frequency and observe mixed results.

International economic policy uncertainty and stock market returns of Bangladesh: evidence from linear and nonlinear model

This paper explores the relationship between international economic policy uncertainty (EPU) and stock market return of Bangladesh. The study considers economic policy uncertainty of six big trading partners of Bangladesh: US, Canada, EU, China, Russia, and India. We apply time-varying linear (Break-least Square) and non-linear (Markov-Switching) regression approaches by using monthly data from January 2003 to April 2019. Our findings indicate the following. Firstly, The break-least square captured four structural breaks in the capital market of Bangladesh. Secondly, economic policy uncertainty from major importing countries (China and India) affect stock market returns of Bangladesh more significantly than major exporting countries (US and EU). Thirdly, EPU has a greater negative influence on stock returns during high volatility than low volatility regime. A number of policy measures have been recommended.

PREDICTING REGIME SHIFTS IN JOHANNESBURG STOCK EXCHANGE ALLSHARE INDEX (JSE-ALSI): A MARKOV-SWITCHING APPROACH

It has been proven several times that linear models are unable to encapsulate nonlinear dynamics of macroeconomic and financial data such as inflation rates, exchange rates and stock prices to mention fewer. As a result, to overcome this problem, this current study adopted the nonlinear models due to the fact that they have required qualities to apprehend nonlinearity in a dataset. In order to predict a regime shifts, a five-day Johannesburg stock exchange allshare index (JSE-ALSI) spanning period from 02 January 2003 to 28 June 2019 was used as an experimental unit. This current study firstly employed Terasvirta neural network test to detect the presence of nonlinearity and proceeded to estimate a two regime Markov-Switching autoregressive (MS-AR). The results of Terasvirta neural network test revealed a highly significant nonlinearity with permanent seasonality as demonstrated by Kruskal-Wallis test. The predicted regime shifts by a latent dynamic allowed the autoregressive and variance parameters to promptly react to vital systemic shocks. As a result, this current study allowed volatility to oscillate between high and low volatility regimes that produced an expected duration of high volatility of two year and two months. This was a clear indication that there is a regime shifts in JSE-ALI which are modeled using Markov-Chain (MC) stochastic process. These findings may be used to inform robust policy making with the aim of safeguarding both the JSE and other global stock markets from the episodes of stock market crash. Moreover, other researchers can utilize the results of this study to calculate the risk associated with structural breaks and high volatility periods.

A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.

Non-linearities, cyber attacks and cryptocurrencies

This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015–2/28/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets.

Inefficiency of Korean Apartment and Jeonse Market : Focused on Jeonse-to-price Ratio

According to a well-known arbitrage equation between house price and Jeonse (key money lump-sum deposit on a rental) price in Korea, when a house price is expected to rise or interest rate falls, the Jeonse-to-price ratio falls. However, in the actual housing market, when prices rise or interest rate falls, the ratio rises for a while. This study examined this process with TARCH and Markov switching analysis. The analysis shows that the growth and disappearance of expected excess returns could well explain the process of the ratio. In particular, the effect of changes in the expected apartment price appreciation rate and the mortgage rates on the ratio might differ in the low volatility and high volatility regimes of expected excess returns. Based on these results, if the housing market stabilizes, the expected excess returns accumulated in the apartment Jeonse market will gradually decrease. In addition, if the current ultra-low interest rates are resolved, the process will be strengthened. It would enhance the efficiency of the housing market, and the Jeonse market would look for relative stability.

The Joint S&P 500/VIX Smile Calibration Puzzle Solved

Since VIX options started trading in 2006, many researchers have tried to build a model that jointly and exactly calibrates to the prices of S&P 500 (SPX) options, VIX futures and VIX options. So far the best attempts, which used parametric continuous-time jump-diffusion models on the SPX, only produced an approximate fit. In this article we solve this longstanding puzzle using a completely different approach: a nonparametric discrete-time model. Given a VIX future maturity T1, we build a joint probability measure on the SPX at T1, the VIX at T1, and the SPX at T2 = T1 + 30 days which is perfectly calibrated to the SPX smiles at T1 and T2, and the VIX future and VIX smile at T1. Our model satisfies the martingality constraint on the SPX as well as the requirement that the VIX at T1 is the implied volatility of the 30-day log-contract on the SPX. We prove by duality that the existence of such a model means that the SPX and VIX markets are jointly arbitrage-free. The joint calibration puzzle is cast as a dispersion-constrained martingale transport problem which is solved using (an extension of) the Sinkhorn algorithm, in the spirit of De March and Henry-Labordere (2019). The algorithm identifies joint SPX/VIX arbitrages should they arise. Our numerical experiments show that the algorithm performs very well in both low and high volatility regimes. Finally we explain how to handle the fact that the VIX future and SPX option monthly maturities do not perfectly coincide, and how to extend the two-maturity model to include all available monthly maturities.

Regime differences and industry heterogeneity of the volatility transmission from the energy price to the PPI

Using the monthly Producer Price Index (PPI) of 37 different industries in China from 2007 to 2017, this study established a Markov regime -switching model to analyze the regime differences and industry heterogeneity of the transmission of volatility from energy price to PPI. The results showed that volatility in energy prices within a given regime has a relatively high “inertia”. Regardless of being in a high- or low-volatility regime, volatility in energy prices was found to have a positive transmission effect on the PPI. In addition, the transmission coefficients of the low-volatility regime tended to be higher than the transmission coefficients of the high-volatility regime. Further analysis revealed that the impact of the volatility in energy prices on the PPI of factors of production was significantly greater than the impact on the PPI of consumer products. Furthermore, in terms of industry heterogeneity, the transmission coefficients of energy and resource industries were found to be the greatest, followed by energy- and resource-intensive industries, and labor-intensive industries; the transmission coefficients of technology- and capital-intensive industries were the lowest.

Time-Varying Periodicity in Intraday Volatility

ABSTRACT We develop a nonparametric test for whether return volatility exhibits time-varying intraday periodicity using a long time series of high-frequency data. Our null hypothesis, commonly adopted in work on volatility modeling, is that volatility follows a stationary process combined with a constant time-of-day periodic component. We construct time-of-day volatility estimates and studentize the high-frequency returns with these periodic components. If the intraday periodicity is invariant, then the distribution of the studentized returns should be identical across the trading day. Consequently, the test compares the empirical characteristic function of the studentized returns across the trading day. The limit distribution of the test depends on the error in recovering volatility from discrete return data and the empirical process error associated with estimating volatility moments through their sample counterparts. Critical values are computed via easy-to-implement simulation. In an empirical application to S&P 500 index returns, we find strong evidence for variation in the intraday volatility pattern driven in part by the current level of volatility. When volatility is elevated, the period preceding the market close constitutes a significantly higher fraction of the total daily integrated volatility than during low volatility regimes. Supplementary materials for this article are available online.

Non-Linearities, Cyber Attacks and Cryptocurrencies

This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015 - 28/2/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets.

An analysis through credit default swap, asset swap and zero-volatility spreads: Coup attempt and Bist 100 volatility

In this study, we explore the volatility structure of BIST 100 index returns through Markov Regime Switching VAR model in the domain of credit risk indicators of Turkey. Also, July 2016 coup attempt has been added to the model, to examine its impact on the volatility. According to the results, Asset Swap spread displays better performance than other two credit risk indicators in signaling. Markov Regime Switching results demonstrate that while BIST 100 return volatility is not affected by credit risk in low volatility regime, in high volatility regime, CDS, ASW and ZV spreads have a significant impact on the volatility. This effect, however, can be omitted due to its very low coefficient. Finally, it is seen that July 2016 coup attempt and following operations did not cause any increase in BIST 100 return volatility.

Conditional quantiles and tail dependence in the volatilities of gold and silver

We study the dependency structure between option-implied volatilities of gold and silver markets via the application of a copula-based quantile regression. First, we conduct a static analysis and show that the asymptotic lower tail dependence is only pronounced in the low volatility regime of both gold and silver markets. Second, given the existence of a bi-directional causality between the two option-implied volatilities, we consider the lead-lag relationship via non-parametric tail dependence estimators. Results indicate an extreme tail dependency in lower and upper quantiles, with evidence of an asymmetric behavior between/for low and high volatility regimes. Our findings have implications to investors and risk managers. Practically, findings imply evidence of predictability of the probability of gold implied volatility based on the lagged silver implied volatility across different quantiles. Another implication concerns a volatility-based trading strategy, especially during in tandem occurrence of high volatility regimes, which involves the simultaneous selling of an out-of-the money call and put with different strike prices on gold implied volatility.