Periods Of Volatility Research Articles

Investors’ reaction under uncertainty

ABSTRACT This study investigates investors’ reaction to good/bad earnings news when faced with market- and industry-wide uncertainties. Our results provide little support for the discount rate explanation that investors’ reaction to good news is dampened during high market volatility. However, the results strongly support the learning hypothesis that earnings news provides value-relevant information for investors during periods of high-market volatility, but that investors cannot learn as much from earnings news under industry-wide uncertainty. These findings also support the conservation hypothesis that investors react more strongly to bad earnings news when faced with market-wide uncertainty.

Term structure of risk factor premiums: evidence from international equity markets

Subject. Factor models are among the most popular tools for the analysis of stock market risks. For a long time, factor models were developed mainly by the formation of additional risk factors. However, static linear specifications are usually not sufficiently sensitive to structural changes in the market and to changes in the operational regimes of markets and in this regard they have a limited number of functions. The idea of regime switching is natural for markets and is intuitively comprehensible, therefore, in order to take into account the non-linear dependencies in factor models of risk analysis, it is advisable to use the mechanism of Markov regime switching with them. Objectives. Statistical analysis of the dynamic properties of the factor structure of risk premiums for transactions with stock market financial instruments based on a modification of the Fama & MacBeth procedure that takes into account the current market regime. Methods: In our study, we used parametric methods of data analysis and machine learning methods, description, analysis, synthesis, induction, deduction, comparison, and grouping method. An EM algorithm was used for the parametric identification of models with Markov regime switching. Results: The paper provides convincing evidence that the relationship between the excess return of financial instruments and factor loads is influenced by the current regime in the market. The main feature of the low volatility regime is the presence of a positive statistically significant risk-adjusted premium. The change in the current regime in the market is accompanied by the transformation of the factor structure of the risk premium. This fact indicates that it is reasonable to divide risk factors into speculative and protective risk factors. Speculative factors include market risks, the risk related to the size of the issuing company, and the momentum effect risk. They have a positive and significant premium in the low volatility regime. The protective factors include risk factors related to value, capital investment, and return on equity. The protective factors are characterised by a positive and significant premium only during periods of high volatility.

Forecasting NIFTY 50 in Volatile Markets Using RNN–LSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic

The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period.

Oil beta uncertainty and global stock returns

This paper documents an economically and statistically significant positive premium for oil beta uncertainty in the cross-section of global equity returns. Using a battery of market and portfolio level tests, we show that oil beta uncertainty, measured by the total range spanned by the 95% confidence interval for estimated oil betas, carries a significant risk premium as high as 9.72% on an annual basis, even after controlling for global systematic risk factors. While most developed stock markets including Australia, Canada, Switzerland, the US and the UK consistently place in the low oil beta uncertainty portfolio, emerging stock markets including Turkey, China, Egypt, Pakistan and Vietnam are found to be consistently exposed to higher oil beta uncertainty. We show that the risk premium associated with oil beta uncertainty cannot be explained by a stock market's exposure to market beta, oil beta or idiosyncratic volatility and is stronger during high volatility periods, bullish market states as well as periods of favorable economic conditions. Further analysis suggests that aggregate oil beta uncertainty also captures significant predictive information regarding future world market excess returns, particularly at the medium and longer forecast horizons. We argue that oil beta uncertainty serves as a proxy for disagreement (or ambiguity) on the sensitivity of stock markets to global economic conditions, captured by oil market exposures, which in turn contributes to a risk premium associated with oil beta uncertainty. The findings present a new, behavioral channel in which oil market uncertainty drives the cross-section of global stock market returns.

Estimating tail-risk using semiparametric conditional variance with an application to meme stocks

In this paper, we propose using Mishra, Su, Ullah’s (2010) semiparametric variance to estimate Value at Risk (VaR) and Expected Shortfall (ES). Although the variance estimate is established in the literature, it has not been applied to VaR and ES estimation. Here the returns’ variance is estimated using Mishra, Su, Ullah’s (2010) conditional semiparametric estimator, and the standardized residuals’ distribution is fitted nonparametrically. In order to reduce possible misspecification bias, the proposed estimators decouple the assumption of the returns’ distribution and the variance estimation. Empirical and simulated data are used to compare the performance of the new estimators against existing parametric and nonparametric VaR and ES models, both conditional and unconditional. The estimators are further applied to daily returns of certain meme stocks to estimate tail risk and test the performance of the estimators during periods of extreme volatility. Compared to the models studied, the proposed conditional, semiparametric VaR model produces fewer violations, and violations without a recognizable pattern - upholding the regulatory requirements. The expected shortfall estimated by the conditional, semiparametric model is also closest to the observed mean of the violations.

Fiscal sustainability in highly indebted countries: evidence from Jamaica

PurposeThis study aims to explore the sustainability of Jamaica’s public debt over a highly volatile period of time.Design/methodology/approachThe authors use a suite of econometric tools, including, unit root testing, cointegration testing and estimating a fiscal reaction function. The authors control for structural breaks in the regression analysis.FindingsThe authors find that whilst reschedulings might be indicative of cash-flow problems in Jamaica, fiscal policy has responded effectively to increase the public debt, thereby making the debt sustainable. Notwithstanding the political economy and social demands of the population prior to the impact of the pandemic, the implications of higher debt stocks (higher debt-servicing and lower social expenditures) might make this approach to fiscal policy and debt management infeasible. As a result, the authors recommend that the government will need to take an active approach in managing its debt position to facilitate responses to shocks and provide conditions within which maintaining fiscal discipline is feasible.Originality/valueTo the best of the authors’ knowledge, this is the first study to explore fiscal sustainability in Jamaica over this time period whilst taking into consideration structural breaks caused by the global financial crisis and debt restructurings. The authors also take into consideration variables such as exchange rates and the occurrence of elections, which have not been included in previous studies.

Crude Oil Price Shocks and European Stock Markets during the COVID-19 Period

This paper investigates the interrelations between stock returns and crude oil prices for European oil-importing/exporting countries. A vector autoregression (VAR) model is applied to estimate the significance of stock market responses to changes in oil prices during the pandemic period 2019–2021. A Granger causality test is applied to find the direction and the intensity of the relation between crude oil and the indices of the European stock markets. The findings of this paper hold with or without the COVID-19 pandemic episode and reveal the interaction between the European stock markets and the crude oil prices. The results indicate that in steady periods, before the COVID-19 outbreak and after the announcement of vaccinations, there is no interdependence between crude oil and stock prices, whereas in high volatility periods, the causality from stock markets to oil prices increases and both oil-exporting and -importing countries are equally influenced. These findings have implications both for investors and fund managers.

The non-linear trade-off between return and risk and its determinants

We estimate a discrete approximation of the risk-return trade-off for the US market by using the whole universe of stocks from July 1963 to September 2017. We find the relationship between return and total risk to be time-varying and also dependent on the level of risk considered. The proposed positive trade-off is mainly observed during low volatility periods and when we move from low risk up to medium-high risk investments. However, the direction of the trade-off is inverted for the highest risk alternatives especially during high volatility periods. The temporal variation of the risk-return trade-off can be explained by a series of sentiment, macro, credit risk, liquidity and corporate variables. All these determinants suggest that the positive relationship between return and risk is more evident during periods where economic, financial and market conditions improve.

Introduction

Violent conflict and displacement reconfigure societies in abrupt, dramatic, and often contradictory ways. Power relations are often shaken up, with new social hierarchies emerging. Artists play a central role in periods of uncertainty and volatility, both as commentators of events and as inspirators for change. This special section explores the role of art practice in transformation in contexts of violent conflict and displacement. The articles focus on artists that either create in the context of oppression and control or respond to these contexts by creating spaces of resistance, life in and with violent conflict, transformation, and inspiration. The articles discuss a range of initiatives and artistic practices that take place in a variety of contexts, from artists involved in societal transformation in Afghanistan, Cambodia, and Syria, to artists working in Palestine, Chad, Sri Lanka, and Bosnia and Herzegovina.

Determinants of cryptocurrency returns: A LASSO quantile regression approach

We consider a relatively large set of predictors and investigate the determinants of cryptocurrency returns at different quantiles. Our analysis exclusively focuses on the highly volatile period of COVID-19. The innovation in the paper stems from the fact that we employ the LASSO penalty in a quantile regression framework to select informative variables. We find that US government bond indices and small company stock returns, a new predictor introduce in this study, significantly impact the tail behavior of the cryptocurrency returns.

On the Economic Significance of Stock Return Predictability

AbstractWe study the effects of time-varying volatility and investment horizon on the economic significance of stock market return predictability from the perspective of Bayesian investors. Using a vector autoregression framework with stochastic volatility (SV) in market returns and predictor variables, we assess a broad set of twenty-six predictors with both in-sample and out-of-sample designs. Volatility and horizon are critically important for assessing return predictors, as these factors affect how an investor learns about predictability and how she chooses to invest based on return forecasts. We find that statistically strong predictors can be economically unimportant if they tend to take extreme values in high volatility periods, have low persistence, or follow distributions with fat tails. Several popular predictors exhibit these properties such that their impressive statistical results do not translate into large economic gains. We also demonstrate that incorporating SV leads to substantial utility gains in real-time forecasting.

Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order P is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.

Asset allocation of Australian superannuation funds: a markov regime switching approach

We extend an observable Markov Regime Switching framework to assess the switching behaviour of asset classes of Australian superannuation funds across different fund sizes. We identify the most prominent asset class which contributes to the performance of the investment options and what factors trigger funds’ decisions on rebalancing their portfolio. We find that smaller funds tend to be more active in switching to aggressive options and the larger funds are more conservative. However, in periods of volatility, the large funds are the risk seekers and tend to switch their asset classes and hence their investment strategies. The asset classes whose values add to the performance of the investment options are equity markets and bond markets with the domestic equity market having better performance than international equity market. The switch for the larger funds is driven by volatility of the equity market.

Hype as a Factor on the Global Market: The Case of Bitcoin

The impact of Bitcoin-related Google queries, Facebook likes, reposts and comments on Bitcoin price is analyzed with the help of ARDL and GARCH models. Our results have led us to the following conclusions. Firstly, a sharp increase in Bitcoin’s popularity or hype, which manifested itself through a rise in the number of Bitcoin-related Google queries, has resulted in an increase in Bitcoin price. This effect corresponds to the description of the ‘collective hysteria’ that spread in the online community and was triggered by the increasing volatility of the Bitcoin market. Secondly, we found that Bitcoin’s popularity among ordinary Internet users has a positive impact in low-volatile and highly volatile rising markets but a negative one in a highly volatile falling market. Thirdly, Bitcoin’s popularity among informed Internet users has a negative impact on Bitcoin price in a period of low volatility. Fourthly, uninformed users’ trust in Bitcoin has a positive influence on Bitcoin price in low-volatile and highly volatile falling markets. Finally, the main factors that shape the Bitcoin market are trust and popularity.

Primeira circum-navegação brasileira e primeira missão do Brasil à China (1879)

When considering transpacific relations between Asia and Latin America, scholars have begun to pay closer attention to the centuries-long historical connections between China and Brazil. The global circulation of goods, people, natural resources, and ideas between Brazil and China began in the early sixteenth century when Portuguese explorers, in competition with other European empires to find the quickest oceanic routes to the silk and spice trade, connected a global trade network that linked port cities in Europe, Africa, Asia, and the Americas. The nineteenth-century history of China-Brazil relations had a contingent relationship to earlier histories of the movement in human labor, goods, languages, cultures, and concepts. In Brazil, the gradual end of African slavery led many intellectuals, politicians, and agriculturalists to look to China for possibilities of setting up new trade networks and Chinese contract labor as a possible substitution for slave labor. These economic concerns were entangled with visions of Brazilian political modernity and debates about how the colonial empire could enter the world stage as a modern republican state.Journalist Marli Cristina Scomazzon and coauthor Jeff Franco make a significant contribution to this critical and volatile period in Brazilian political and economic history. Their book, Primeira circum-navegação brasileira e primeira missão do Brasil à China (1879), details the Brazilian Navy's first trip around the world and the first Brazilian mission to China. The book is sponsored by the Brazilian Navy's Department of Publication and Dissemination and endorsed by José Carlos Mathias, vice admiral and director of historical patrimony and documentation of the Navy, and Pierre Paulo da Cunha Castro, captain of sea and war, who author the book's prologue and preface, respectively. Organized like a navy museum exhibition catalog with a patriotic tone, the book provides an exposition of the global voyage of the corvette Vital de Oliveira, which circumnavigated the world in 430 days from November 19, 1879, to January 21, 1881. The book's two main parts follow the circumnavigation route of the Vital de Oliveira. The first part is further subdivided according to a list of the port cities, regions, and countries where the corvette made stops, including Gibraltar, France, Malta, Egypt, Yemen, Sri Lanka, Singapore, China, Japan, the United States, Mexico, Chile, Patagonia, and Uruguay. The second part exhibits archival materials about the first Brazilian delegation to China. The book's organization and methodology reconstruct a military history about the two main goals of the Brazilian Navy for the trip around the world: to provide professional instruction on long sea voyages, and to fulfill a diplomatic mission in China with the intent to “buscar mão de obra chinesa para substituição do trabalho escravo no Brasil” (p. 39). The sections of the first part largely retrace the voyage through providing dates and times of departure and arrival, followed by long citations of the travel writings from people aboard the warship or accounts printed contemporaneously in Brazilian newspapers. The book is composed of a diverse archive of illustrations, musical scores, European and Chinese diplomatic missives, photographs, and passenger lists, but the book's archive is compiled from an uneven grouping of sources, ranging from personal souvenirs and national archives to portraits of Qing officials taken from Wikipedia. The second part of the book is dedicated to the first Brazilian diplomatic mission to China and is similarly organized according to the cities that the delegation visited. The sections include long quotations taken from Brazilian diplomatic writings or Brazilian newspapers, which are presented as facts without questioning the politics of the newspapers, journalists, travel writers, or diplomatic perspectives. These shortcomings need not be limitations since the book presents many interesting details when approached with caution and a critical lens. It will be of interest to historians in general and opens opportunities for further research on the long and connected histories of China and Brazil.

Modeling Empirical Stock Market Behavior Using a Hybrid Agent-Based Dynamical Systems Model

We describe the development and calibration of a hybrid agent-based dynamical systems model of the stock market that is capable of reproducing empirical market behavior. The model consists of two types of trader agents, fundamentalists and noise traders, as well as an opinion dynamic for the latter (optimistic vs. pessimistic). The trader agents switch types stochastically over time based on simple behavioral rules. A system of ordinary differential equations is used to model the stock price as a function of the states of the trader agents. We show that the model can reproduce key stylized facts (e.g., volatility clustering and fat tails) while providing a behavioral interpretation of how the stock market itself can cause periods of high volatility and large price movements, even when the economic value of the stock grows at a constant rate.

Key factors affecting carbon prices from a time-varying perspective.

For humankind to sustain a livable atmosphere on the planet, many countries have committed to achieving carbon neutralization. Countries mainly reduce carbon emissions by regulations through a carbon tax or by establishing a carbon market using economic stimuli. In this paper, we use the least absolute shrinkage and selection operator (LASSO) method to select the key determinants of a carbon market and then use the Markov switching vector autoregression (MSVAR) model to study the market’s driving factors and analyze its time-varying characteristics. The results show that there are perceptible time-varying characteristics and notable differences among markets. During COVID-19, energy factors had a long-term shock on the carbon market, economic factors had a short-term shock on the carbon market, and the economic recession has led to fluctuations in the carbon market. In addition, through MSVAR, the results show that the energy market has a negative effect on the carbon market, and the stock market has a positive effect on the carbon market. In periods of low volatility, compared with the natural gas market and coal market, the oil market has a stronger shock on the carbon market. In periods of high volatility, the coal market has a stronger shock on the carbon market. In terms of emission reduction, countries around the world would be wise to change their energy consumption structure, reduce coal use, and shift to a cleaner energy consumption structure.

Does the Introduction of Stock Index Futures Reduce the Stock Market Volatility

Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroscedasticity (ARCH). The aim of this paper is to estimate conditional volatility models in an effort to capture the salient features of stock market volatility in India and evaluate the models in terms of out-of sample forecast accuracy. The paper also investigates whether there is any change in volatility after the introduction of futures. The estimation of volatility is made at the macro level on a major market index, namely, S&P CNX Nifty. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroscedastic behaviour of the Indian stock market at the micro level. The volatility is estimated by fitting different models to the market indices by dividing the study duration into two different time period's, one pre-future and another post-futures -: • Historical moving average model • Standard Generalized autoregressive conditional heterosedasticity GARCH (1, 1) model. The paper found: • A strong evidence of time-varying volatility. • A tendency of the periods of high and low volatility to cluster • A high persistence and predictability of volatility.

Herding behavior and financial market price behavior under the COVID-19 pandemic: implications for the Amman Stock Exchange

Purpose — This study investigates herding behavior and the link between herding behavior with the liquidity and volatility of financial markets amid the COVID-19 pandemic.Research method —This study employed the Cross-Sectional Absolute Deviation (CSAD) approach technique for each of the previous and COVID-19 phases, as well as for the complete sample period. The sample includes 172 securities that have been traded in the Amman Stock Exchange from January 2006 to February 2022. The full sample data was separated into three subsamples: 1) the whole period from January 1, 2006, to February 28, 2022; 2) prior the COVID-19 outbreak from January 1, 2006 to February 28, 2020; and 3) during the COVID-19 outbreak from March 1, 2020, to February 28, 2022.Result — The results for the whole sample period show that herding behavior may be detected in the Amman Stock Exchange during down-market intervals, and the result does not change before or during the COVID-19. Furthermore, both down liquidity and down volatility periods exhibit contrary herding behavior. The study found that financial market price behavior is an important factor that may contribute to herding behavior, which occurs when volatility increases and liquidity decreases in the Amman Stock Exchange, and the result does not change prior to the COVID-19 period. While liquidity has a negative and large influence throughout the COVID-19 period, herding tendency does not increase when volatility changes.Recommendation — This research assists traders in planning their purchasing and selling strategies in the COVID-19 various scenarios.

COVID Asymmetric Impact on the Risk Premium of Developed and Emerging Countries’ Stock Markets

We estimated the stock market risk premium during the COVID-19 pandemic with a GARCH-in-Mean (GARCH-M)(1,1) model. The analysis then explored the presence of regime changes using a two-regime Markov-Switching GARCH (MS GARCH)(1,1) model. The sample we used included the stock market indexes of nine countries from three geographical regions, including: North America (Canada, USA, and Mexico), South America (Brazil and Argentina), and Asia (Japan, South Korea, Hong Kong, and Singapore), over two periods: (a) pre-COVID (from 1 January 2015 to 31 December 2019); and (b) COVID (from 1 January 2020 to 31 December 2021). Our GARCH-M(1,1) estimation results indicate that the more developed countries’ stock markets experienced an important increase in their risk premium during the COVID period, likely explained by the massive government anticyclical policies. By contrast, developing countries’ stock markets, particularly in Latin America, experienced a reduction, and in some cases, even a total loss of the risk premium effect. From the perspective of investors and portfolio risk managers, the identification of high and low volatility periods and their estimated probability of occurrence is useful for the characterization of stress scenarios and the design of emerging strategies. For governments and central bankers, the implementation of different policies should respond to the more likely scenarios but should also be prepared to respond to other less likely scenarios. Institutional preparedness to respond to as many different scenarios as may be identified with the use of MS GARCH models can make their interventions more successful. This work presents an objective example of how the use of MS GARCH models may be of use to practitioners in both the financial industry and government. We confirmed that the results of a two-regime MS GARCH model are superior to those obtained from a single-regime model.