Return Dynamics Research Articles

Bubbles and crashes in cryptocurrencies: Interdependence, contagion, or asset rotation?

Using a quantile vector autoregressive model to capture return dynamics in extreme market conditions, we find that the cryptocurrency market exhibits a high level of market connectedness. Bitcoin is a net transmitter of return spillovers during busts and a net receiver during booms. Analysis of the timing of bubble and crash periods uncovers the presence of interdependence and contagion effects. Asset dynamics is driven to a great extent by the technology, in particular the consensus protocol of cryptocurrencies. There is only limited evidence for asset rotation, and it involves mostly Ripple.

Illiquidity risk and equity trading performance: A statistical analysis

Subject. The article considers the development of special statistical methods for estimating and analyzing the liquidity risk. Objectives. The purpose is to improve the methodology for statistical appraisal and analysis of risk in equity trading. Methods. The study rests both on well-known methods for equity risk analysis and my own development results (calculation of equity annual illiquidity ratio, formation of illiquidity risk factor). The data analyzed in this paper come from the Moscow Exchange (MOEX) and cover January 2011 to May 2021. The sample included all common stocks traded on MOEX and issuers’ financial statements. I also apply analysis and synthesis, induction and deduction, and methods of comparison and grouping. Results. I calculated monthly illiquidity factor as zero-investment long-short portfolio. I examined the impact of illiquidity risk on the return dynamics of size, book-to-market ratio sorted portfolios. Conclusions. The study shows that expected equity returns are related cross-sectionally to the illiquidity factor. The evidence strongly supports the hypothesis that the illiquidity risk factor is priced. The premium for this risk is positive and offers higher expected returns in equities with strong illiquidity. However, for liquid equities no significant premium is revealed. The offered approach to the factor equity risk analysis based on illiquidity risk enables a true picture of how the risks impact the equity trading performance and how they can be improved in the future.

The effect of COVID-19 pandemic on global stock markets: Return, volatility, and bad state probability dynamics.

This study investigates the impact of COVID‐19 pandemic on stock returns, conditional volatility, conditional skewness and bad state probability. This study utilizes an asymmetric exponential generalized autoregressive conditional heteroscedasticity model to capture the asymmetric effect of positive and negative shocks (news) on conditional volatility. Using a sample consisting of international stock market indices in Brazil, China, Italy, India, Germany, Russia, Spain, United Kingdom, and United States, over the period from January 1, 2013 to December 31, 2020, we find unprecedented increases in conditional volatilities and bad state probabilities across all the markets. However, this impact is not symmetric across markets. Furthermore, we find that the negative affect of deaths is more pronounced, compared to the positive impact of recovered cases.

A Computational Gait Model With a Below-Knee Amputation and a Semi-Active Variable-Stiffness Foot Prosthesis.

Simulations based on computational musculoskeletal models are powerful tools for evaluating the effects of potential biomechanical interventions, such as implementing a novel prosthesis. However, the utility of simulations to evaluate the effects of varied prosthesis design parameters on gait mechanics has not been fully realized due to the lack of a readily-available limb loss-specific gait model and methods for efficiently modeling the energy storage and return dynamics of passive foot prostheses. The purpose of this study was to develop and validate a forward simulation-capable gait model with lower-limb loss and a semi-active variable-stiffness foot (VSF) prosthesis. A seven-segment 28-DoF gait model was developed and forward kinematics simulations, in which experimentally observed joint kinematics were applied and the resulting contact forces under the prosthesis evolved accordingly, were computed for four subjects with unilateral below-knee amputation walking with a VSF. Model-predicted resultant ground reaction force (GRFR) matched well under trial-specific optimized parameter conditions (mean R2: 0.97, RMSE: 7.7% body weight (BW)) and unoptimized (subject-specific, but not trial-specific) parameter conditions (mean R2: 0.93, RMSE: 12% BW). Simulated anterior-posterior center of pressure demonstrated a mean R2 = 0.64 and RMSE = 14% foot length. Simulated kinematics remained consistent with input data (0.23 deg RMSE, R2 > 0.99) for all conditions. These methods may be useful for simulating gait among individuals with lower-limb loss and predicting GRFR arising from gait with novel VSF prostheses. Such data are useful to optimize prosthesis design parameters on a user-specific basis.

On the time-varying dynamics of stock and commodity momentum returns

Using a state-of-the-art Markov switching framework augmented by popular proxies of arbitrage activity and investor sentiment, we reexamine the dynamics of stock momentum returns and provide a first structured time-series analysis of commodity momentum portfolios. Our study arrives at the important finding that, in contrast to previous studies relying on restrictive static models, we cannot detect persuasive links between momentum returns and such variables in recent data. Consequently, the evolution of momentum returns remains puzzling. Furthermore, putting the behavior of extremes aside, stock and commodity momentum returns exhibit quite similar regime-switching behavior. This supports the frequent statement that the financialization of commodity futures markets has non-trivially linked stock and commodity returns.

Pricing the Volatility Risk Premium with a Discrete Stochastic Volatility Model

Investors’ decisions on capital markets depend on their anticipation and preferences about risk, and volatility is one of the most common measures of risk. This paper proposes a method of estimating the market price of volatility risk by incorporating both conditional heteroscedasticity and nonlinear effects in market returns, while accounting for asymmetric shocks. We develop a model that allows dynamic risk premiums for the underlying asset and for the volatility of the asset under the physical measure. Specifically, a nonlinear in mean time series model combining the asymmetric autoregressive conditional heteroscedastic model with leverage (NGARCH) is adapted for modeling return dynamics. The local risk-neutral valuation relationship is used to model investors’ preferences of volatility risk. The transition probabilities governing the evolution of the price of the underlying asset are adjusted for investors’ attitude towards risk, presenting the asset returns as a function of the risk premium. Numerical studies on asset return data show the significance of market shocks and levels of asymmetry in pricing the volatility risk. Estimated premiums could be used in option pricing models, turning options markets into volatility trading markets, and in measuring reactions to market shocks.

Investment behavior during COVID pandemic: An evidence from emerging Asian Markets

This study investigates the investor behavior in Asian emerging markets during the COVID pandemic period and assesses the asymmetric patterns and the influence of global and regional return dynamics on local investor behavior. It uses daily firm-level data from January 2001 to December 2020 for nine Asian emerging countries: China, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Taiwan and Thailand to estimate the impact of extreme price movements, market liquidity and US and Chinese market returns on local return dispersions using a non-linear specification. The findings indicate that rational price behavior prevailed in all markets over the COVID period. Though herding manifested itself occasionally during the low trading volume days in the pandemic period; overall, herd formation was not observed conditioned on domestic or non-domestic factors. Infact, our comparison of pre-COVID and COVID periods shows a significant shift in investment behavior away from herding and in favor of market efficiency for emerging markets of China, India and Korea. The findings on prevalence of rational price behavior are unique and reflective of improved informational environment in Asian emerging markets.

Portmanteau test for the asymmetric power GARCH model when the power is unknown

It is now widely accepted that, to model the dynamics of daily financial returns, volatility models have to incorporate the so-called leverage effect. We derive the asymptotic behaviour of the squared residuals autocovariances for the class of asymmetric power GARCH model when the power is unknown and is jointly estimated with the model’s parameters. We then deduce a portmanteau adequacy test based on the autocovariances of the squared residuals. These asymptotic results are illustrated by Monte Carlo experiments. An application to real financial data is also proposed.

A network autoregressive model with GARCH effects and its applications.

In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR model with the Granger causality test and Pearson’s correlation test with sharp price movements is used to capture the joint effects caused by other indices with the most updated market information. The NAR-GARCH model is designed to depict the joint effects of nonsynchronous multiple time series in an easy-to-implement and effective way. The returns of 20 global stock indices from 2006 to 2020 are employed for our empirical investigation. The numerical results reveal that the NAR-GARCH model has satisfactory performance in both fitting and prediction for the 20 stock indices, especially when a market index has strong upward or downward movements.

The Role of Investor Sentiment and Valuation Uncertainty in the Changes around Analyst Recommendations: Evidence from U.S. Firms

The authors investigate the empirical relation among investor sentiment, valuation uncertainty, and announcements of changes in analyst recommendation decisions among U.S. firms. Recent behavioral finance evidence shows market sentiment to have predictive content that affects the classical relationship between analyst recommendations and stock return dynamics. Contrary to this evidence, the authors find that degree of valuation uncertainty is associated to the impact of investor sentiment when examining a likelihood of consensus recommendation upgrade or downgrade. While not totally eliminating the significant investor sentiment effect under high valuation uncertainty, the investor sentiment does not powerfully explain the stock market reactions to analyst recommendation changes under low valuation uncertainty. Furthermore, the authors show that analyst recommendations provide significant buy or sell signals if valuation uncertainty is great, referring to the market being highly competitive. However, in less competitive markets, analyst reports become less informative. Overall, the authors demonstrate that magnitude of valuation uncertainty is an important complement to investor sentiment for further understanding analyst recommendations.

Modelling and Forecasting Volatility of Crude Oil Returns in Nigeria based on Six Error Innovations

Modeling price volatility of crude oil (PVCO) is pertinent because of the overbearing impact on any oil-producing economy. This study aimed at evaluating the performance of some volatility models in modeling and forecasting crude oil returns. Utilizing daily returns data from October 23, 2009, to March 23, 2020, this study attempted to capture the dynamics of crude oil price volatility in Nigeria using a symmetric and asymmetric GARCH models. In our research, we considered the generalized autoregressive conditional heteroscedastic model (GARCH), Exponential (E-GARCH), Glosten, Jagannathan and Runkle (GJR-GARCH) and Asymmetric Power (AP-ARCH) under six error innovations that include the skewed variant of the student-t, generalized error and normal distribution. From the results obtained, it was discovered that the AP-ARCH (1, 1) model performed better in the fitting and performance evaluation phase. The skew Student’s t-distribution (SStD) was also reported to be the best performing error innovation in most of the models. Based upon these results, we conclude that the AP-ARCH (1, 1)-SStD model is the best model for capturing the dynamics of crude oil returns in Nigeria.

Dynamics of Cost, Returns and Profitability of Soybean across Leading States of India

The results revealed that cost concepts of soybean were increased at highly significant growth rates. Net income was positive, and the B.C. ratio was more than one in the base year, which indicated that soybean was profitable in the base year only. The differential yield of soybean was positive, which denoted that farmers managed to produce above the break-even level (profit zone). The difference between the cost of production and minimum support price was positive, indicating that MSP fixed by the government was less than the cost of production (Cost C ), indicating no assurance of profit if the market prices also tumble below the cost of 2 production.

Rare earth and allied sectors in stock markets: extreme dependence of return and volatility

ABSTRACT This study examines the dynamics of return and volatility connectedness between the rare earth stock index and the indexes of clean energy, consumer electronics, telecommunications, healthcare equipment, and aerospace & defence. Using daily data from 25 March 2010 to 25 August 2020, a quantile-based connectedness approach is applied to uncover both average and tail-based connectedness while considering the full sample period and the COVID-19 pandemic days. The results suggest that the interdependence among these indexes changes dramatically at the lower and upper quantiles, suggesting a strong influence of extreme market scenarios on both returns and volatility connectedness dynamics. Higher integration of sectoral indexes is observed during 2010–2012 and the COVID-19 pandemic period. Health care and telecommunication indexes have been consistent transmitters of return and volatility spillovers to other indexes during the full sample period. Consumer electronics and clean technology indexes switch their roles from a net receiver to a net transmitter during pandemic days. The rare earth remains on the recipient’s side consistently. The findings indicate that the ongoing U.S.–China trade embargo has not impacted the return and volatility dynamics of the five sectoral indexes superseding the demand-driven dynamics for rare earth.

Time-Varying Volatility in Bitcoin Market and Information Flow at Minute-Level Frequency

In this article, we analyze the time series of minute price returns on the Bitcoin market through the statistical models of the generalized autoregressive conditional heteroscedasticity (GARCH) family. We combine an approach that uses historical values of returns and their volatilities—GARCH family of models, with a so-called Mixture of Distribution Hypothesis, which states that the dynamics of price returns are governed by the information flow about the market. Using time series of Bitcoin-related tweets, the Bitcoin trade volume, and the Bitcoin bid–ask spread, as external information signals, we test for improvement in volatility prediction of several GARCH model variants on a minute-level Bitcoin price time series. Statistical tests show that GARCH(1,1) and cGARCH(1,1) react the best to the addition of external signals to model the volatility process on out-of-sample data.

Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk

We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.

Wavelet coherence analysis of returns, volatility and interdependence of the US and the EU money markets: Pre & post crisis

This research analyse the US and the EU money markets interdependence from 2004 to 2018. The study explains to what extent the volatility of the chosen money markets instruments in two regions is inter-correlated before, during and after the financial crisis of 2008. We apply the econometric analysis and estimate time-series models of class GARCH to study the historical dynamics of interbank rates and bond returns. The study demonstrates that correlation between returns of analogous money market instruments in the EU and US is not stable over time. We find that correlation rises in periods when countries are exposed to the same external shocks as global financial crisis. Wavelet coherence analysis suggests that investors do not get any advantages of portfolio diversification investing only in US treasuries with different maturities for more than 256 days and do not get any advantages at all investing only in European bonds.

Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?

This paper examines the dynamics between the energy markets and uncertainty indices from January 1st, 2001 till July 1st, 2020, using the time-domain TVP-VAR-based connectedness approach of Antonakakis and Gabauer (2017). In particular, we examine whether Economic Policy Uncertainty (EPU), Geopolitical Risk (GPR), World Trade Uncertainty (WTU), and Equity Market Uncertainty (EMU) have an impact on the dynamics of returns of oil, gas, and coal markets. Results suggest that the average influence of market uncertainty on energy markets is approximately 53%. Second, we find that the EPU contributes the most to the energy markets, followed by the World Trade Uncertainty index. Third, considering the energy markets, we find that the oil markets contribute the most to other markets. Fourth, we find that the EMU receives the most contribution from other markets, followed by the EPU. Finally, we observe that the total connectedness index is relatively high, coinciding with the Global Financial Crisis of 2008 and the COVID-19 pandemic outbreak. Further analysis of the possible effect of the investor sentiment on the dynamic connectedness shows that the consumer sentiment index (CSI) has a negative (positive) effect on the uncertainty (energy) net connectedness. Our findings have important implications for risk management in energy markets and entail some policy implications for regulators.

Volatility transmission and spillover dynamics across financial markets: the role of geopolitical risk

This paper examines the effect of geopolitical risk (GPR) on return and volatility dynamics in Middle East and North African (MENA) countries by using an ADCC-GARCH model and a spillover approach. Unlike previous studies, we include the GPR index to capture risk associated with wars, terrorist acts, and political tensions. Moreover, we test for both static and dynamic analysis using a rolling window. In brief, the findings highlight that GPR does not contribute to the return spillovers among MENA financial markets. However, the dynamic analysis provides evidence of the high level of responsiveness of the total spillover index to major political events (e.g., the Arab Spring uprising and political tension between Qatar and other Gulf Cooperation Council countries). More interestingly, Qatar, Kingdom of Saudi Arabia, and the United Arab Emirates are identified as the main transmitters of return spillovers to the rest of the MENA markets. Overall, our results are essential in understanding the impact of the GPR on return spillover among MENA countries, and are of particular importance to policymakers, market regulators, portfolio managers and investors.

On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia

This paper categorizes Australian listed cryptocurrency-linked stocks (CLS) by their involvement as a user, developer and diffuser, and investor of blockchain technology and cryptocurrencies based on company announcements and published information on the company websites. By distinguishing CLS engagement with blockchain technology, we examine their returns and volatility spillover with the cryptocurrency market over the period 1 September 2017 to 7 June 2018, spanning important episodes and dynamics in the cryptocurrency market in 2017-2018, and the emergence of Australian CLS. Utilizing the Diebold and Yilmaz (2012) spillover methodology, we find significant unidirectional return spillover and weak volatility spillover from the cryptocurrency market to CLS, after controlling return dynamics of the Australian dollar, Gold and commodity. However, CLS with high involvement in blockchain technology displays stronger connectedness to the cryptocurrency market through return spillover relative to low involvement CLS. Our findings indicate that investors incorporate the price dynamics of cryptocurrencies into their trading decisions for CLS.

Economic policy uncertainty and gold return dynamics: Evidence from high-frequency data

This paper examines the dynamic effect of economic policy uncertainty (EPU) on return and volatility in gold futures via the time varying parameter VAR (TVP-VAR) model with stochastic volatility applied to high-frequency data. We show that the impulse responses of gold returns and volatility to EPU shocks are time-varying and exhibit asymmetric patterns alternately over the sample period. While the effect of EPU on gold volatility is generally stronger in the intermediate to long horizons, the opposite is observed in the case of gold returns, underscoring the additive effects of uncertainty shocks on gold market volatility. The effect of EPU on gold volatility is found to be asymmetric with respect to the nature of shocks and stronger in times of crisis or major events, with such events implied by high EPU values associated with positive volatility jumps in the gold market. Our findings have significant implications for the valuation of gold derivatives and the effectiveness of gold as a potential hedge for financial investors and suggest that investors may risk mis-valuations and consequently, ineffective and costly hedges in response to uncertainty shocks if the time-variation in the response of gold return dynamics to uncertainty shocks is ignored.