High Volatility Regime Research Articles

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.

Firm Characteristics, Stock Market Regimes, and the Cross-Section of Expected Returns

I propose a regime-switching generalization of instrumented principal components analysis (IPCA) that yields new insights about the relation between characteristics, factor loadings, and expected stock returns. Using a two-regime specification, I find evidence of a high-volatility regime in which individual stocks have high conditional expected returns. This contrasts sharply with the pattern of bull and bear regimes that is obtained by analyzing only market returns. Although exact factor pricing can be rejected, characteristics are more strongly related to priced covariances in the high-volatility regime. Furthermore, regime-switching predictability makes a substantial incremental contribution to the out-of-sample explanatory power of IPCA estimates.

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.

Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading

In this work, the use of Markov-switching GARCH (MS-GARCH) models is tested in an active trading algorithm for corn and soybean future markets. By assuming that a given investor lives in a two-regime world (with low- and high-volatility time periods), a trading algorithm was simulated (from January 2000 to March 2019), which helped the investor to forecast the probability of being in the high-volatility regime at t + 1. Once this probability was known, the investor could decide to invest either in commodities, during low-volatility periods or in the 3-month US Treasury bills, during high-volatility periods. Our results suggest that the Gaussian MS-GARCH model is the most appropriate to generate alpha or extra returns (from a passive investment strategy) in the corn market and the t-Student MS-GARCH is the best one for soybean trading.

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.

Interfuel Substitution: Evidence from the Markov Switching Minflex Laurent Demand System with BEKK Errors

We investigate interfuel substitution in the United States using the minflex Laurent demand system and a century of data (from 1919 to 2012). We relax the assumption of constant parameters in the demand system, and also relax the ho-moskedasticity assumption, instead assuming that the covariance matrix of the errors is time-varying. Our results are consistent with theoretical regularity and indicate that the Morishima elasticities of substitution are always positive for all pairs of the energy goods (suggesting substitutability), but exhibit large swings across two regimes, generally being higher in the high demand volatility regime before the 1950s.

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.

A path-integral approximation for non-linear diffusions

Using the path-integral formalism we develop an accurate and easy-to-compute semi-analytical approximation to transition probabilities and Arrow–Debreu densities for arbitrary diffusions. We illustrate the accuracy of the method by presenting results for the Black–Karasinski model for which the proposed approximation provides remarkably accurate results, even in regimes of high volatility and for multi-year time horizons. The accuracy and the computational efficiency of the proposed approximation makes it a viable alternative to fully numerical schemes for a variety of applications, ranging from maximum-likelihood estimation in econometrics to derivatives pricing.

Active portfolio management in the Andean countries’ stock markets with Markov-Switching GARCH models

In the present paper we test the benefits of using two-regime Markov-Switching (MS) models in the stock markets of the MSCI Andean index (Chile, Colombia and Perú). We tested this with either, constant, ARCH or GARCH variances and Gaussian or t-Student log-likelihood functions. By performing 996 weekly simulations from January 2000 to January 2019 with each MS model, we tested the next investment strategy for a U.S. dollar based investor: 1) to invest in the risk-free asset if the probability of being in the high-volatility regime at t+1 is higher than 50 % or 2) to do it in the stock market index otherwise. Our results suggest that the Gaussian MS-GARCH models are the most suitable to generate alpha in the Chilean stock market and the Gaussian MS-ARCH in the Colombian one. For the Peruvian case, we found that is preferable to perform passive investing instead of active trading.

Transmisión de los precios del arroz en Colombia y el mundo

The aim of this paper is to analyse the spatial price transmission of rice in Colombia, emphasizing the impact of trade policies. For this purpose, a Markov-switching vector error correction model was used to model regime shifts in the relationship between domestic and international rice prices in Colombia and some control countries, from January 1996 to September 2018. The results reveal three price transmission regimes that coincide with internal trade policies and with the food crisis of 2007-2008. The high volatility regime was the most persistent, with an average duration of 15.4 months, a transition probability of 93 % and an adjustment speed of 0.24. In addition, during this regime, Colombia was less integrated into the international rice market. These results are relevant, since they constitute the application of a threshold methodology to the analysis of the transmission of agricultural prices and can be useful for the design of agrarian policies that contribute to the integration and competitiveness of the Colombian rice sector.

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.

Market risk and Bitcoin returns

Bitcoin is emerging as a distinct asset class among investors given its seemingly detached price behavior relative to market and economic fundamentals. Its incomparably high returns in recent years has further fuelled intense interest and investment into Bitcoin and cryptocurrencies at large. This paper cautions that Bitcoin prices, despite their seemingly attractive independent behavior relative to economic variables, may still be exposed to the same types of market risks which afflict the performance of conventional financial assets. Using a Markov regime-switching model to distinguish between regimes of high and low Bitcoin price volatility, this paper shows that while returns on the aggregate market portfolio cannot explain Bitcoin returns, other asset pricing risk factors, such as interest rates and implied stock market and foreign exchange market volatilities, are important determinants of Bitcoin returns. Distinguishing between periods of high and low Bitcoin price volatility reveals heterogeneity in the explanatory power of market risk factors; in particular, Bitcoin returns are more difficult to explain during periods of high volatility relative to periods with low volatility. This finding can partially explain why extant studies, which neglect to distinguish between exchange rate regimes in Bitcoin, have difficulty linking Bitcoin prices to economic fundamentals.

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.

The size premium and macrovolatility risks: Evidence from U.S. and U.K. equity markets

AbstractThe size effect is alive well but visible only when the economy is in high volatility regimes. This paper develops variant conditional asset pricing tests for the size effect with independent business cycle and volatility regimes and shows that the size effect is present conditionally during the high‐volatility regimes. This result is robust across two countries (United States and United Kingdom) with various specifications and the January effect. An economic rationale for the relation between the size premium and macrovolatility risk is provided through the capital‐market‐imperfection hypothesis.

Dynamic Information Regimes in Financial Markets

We develop a model of investor information choices and asset prices where the availability of information about fundamentals is time-varying. 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.

The relationship between trading activity and stock market volatility: Does the volume threshold matter?

This paper examines whether trading activity conveys valuable information about changes in market volatility dynamics. We use a modelling framework, in which the market smoothly switches from one state to another, according to the volume level. Results show that large volume drives the high volatility regime for most of the markets, quite consistently with the disagreement-in-beliefs hypothesis. The volume decomposition into normal trading activity and surprising information arrival reveals a reverse threshold linkage for emerging markets. Results support the sequential information arrival hypothesis and highlight the key role of asymmetric information and thin trading in modelling the volume-volatility relationship. The proposed volume-based models provide significant forecast improvements over competing models and offer scope for investors to earn substantial profits.

Regime-switching in emerging market business cycles: Interest rate volatility and sudden stops

We document the presence of discrete regime-switching in emerging market business cycles, particularly in the volatility of interest rates at which countries borrow internationally, using a multi-country regime-switching vector autoregressive model. We study the statistical relationship of such business cycle regimes with episodes of sudden stops. Periods of high volatility tend to be persistent and are associated with high interest rates, the occurrence of sudden stops in external financing, and large declines in economic activity. Most strikingly, we show that regime switches drive the countercyclicality of interest rates in emerging markets documented in previous literature (Neumeyer and Perri, 2005) and that high-volatility regimes forecast sudden stops 6 and 12 months ahead.

Economic Policy Uncertainty and Herding Behavior: Evidence from the South African Housing Market

This paper examines the link between economic policy uncertainty and herding behaviour in financial markets with an application to the South African housing market. Building on the evidence in the literature that herding behaviour driven by human emotions is not only limited to financial markets, but is also present in real estate investments, we examine the presence of herding in this emerging market via static and dynamic herding tests. While the static model fails to detect herding in the South African housing market, a dynamic model based on a two-regime Markov switching specification shows evidence of herding during the high volatility regime only, consistent with the notion that herd behaviour is primarily driven by increased market uncertainty. Extending our analysis via quantile regressions, we further show that higher quantiles of policy uncertainty are associated with greater likelihood of being in the herding regime, thus establishing a link between policy uncertainty and herding behaviour. Overall, our findings suggest that policy uncertainty can serve as a driver of market inefficiencies, which in our case, is associated by the presence of herding.

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.

A Path-Integral Approximation for Non-Linear Diffusions

Using the path-integral formalism we develop an accurate and easy-to-compute semi-analytical approximation to transition probabilities and Arrow-Debreu densities for arbitrary diffusions. We illustrate the accuracy of the method by presenting results for the Black-Karasinski model for which the proposed approximation provides remarkably accurate results, even in regimes of high volatility and for multi-year time horizons. The accuracy and the computational efficiency of the proposed approximation makes it a viable alternative to fully numerical schemes for a variety applications, ranging from maximum-likelihood estimation in econometrics to derivatives pricing.