Level Of Volatility Research Articles

Price Volatility and Cereal Food Security in Tunisia: Determining the Critical Threshold of Cereal Price Volatility Using the Threshold Model

This study examines the substantial impact of grain price volatility on food security in Tunisia, a nation heavily reliant on cereal consumption. Employing Hansen's (1999) threshold model, we pinpoint the precise level of price volatility that poses a significant threat to cereal food security in Tunisia. A novel application of this methodology, our analysis, spanning 1991-2021, focuses on cereals, constituting over 50% of Tunisia's food imports. The threshold model identifies a critical volatility threshold of 4.65%. Beyond this point, food security is significantly compromised. This finding underscores the study's capacity to quantify the precise level of grain price volatility that exacerbates food security risks in Tunisia. To mitigate these risks, policymakers should prioritize price stabilization measures, enhance risk management and storage mechanisms, and develop targeted adaptation strategies for vulnerable populations. These actions will bolster the resilience of the Tunisian food system.

Exploring market-wide herding behavior in the major stock markets of Latin America

PurposeThis paper investigates market-wide herding behavior among investors in the stock markets of Brazil, Chile, Colombia and Mexico from January 2013 to December 2022.Design/methodology/approachWe analyze a survivor-bias-free dataset of daily stock returns, employing a measure reflecting the cross-sectional deviation of stock returns relative to market consensus.FindingsSignificant anti-herding is observed in Brazil and Mexico, while Chile and Colombia show results consistent with rational asset pricing models. The COVID-19 pandemic generally intensifies anti-herding trends. Additionally, significant asymmetries in herding/anti-herding effects are noted during different market trends and volatility levels. Furthermore, we identify the drivers of this phenomenon, revealing that extreme crude oil price movements are associated with more pronounced anti-herding, and herding/anti-herding effects appear synchronized across all four markets.Practical implicationsOur findings regarding synchronization in herding dynamics suggest challenges in realizing the desired benefits of international diversification in the region.Social implicationsThe significant cross-country effects indicate that herding dynamics may play a crucial role in precipitating regional financial crises.Originality/valueFor the first time, we examine various features of herding behavior in the sample markets, including the impact of the COVID-19 pandemic, psychological and economic drivers of the phenomenon and synchronization in herding dynamics among the four markets.

Algorithm aversion during disruptions: The case of safety stock

Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.

Exploring the principal factors driving the volatility structure of stock trade levels in New Zealand: a Covid-19 perspective

This paper explores factors that drive the volatility structure of New Zealand stock trade levels, with the aim of understanding unusual volatility of stock trade levels that may pose a risk to the critical operations of a trading venue. Weekly number of trades from 2014–2024 for the sampled stocks were examined within principal axis factoring (PAF) and GARCH models. The results showed that three factors accounted for around 60% of volatility in the number of stock trades over the sampled period; they were driven primarily by electricity generation stocks, consumer discretionary stocks, and commercial property stocks respectively. During the sampled period, two subperiods of market shock showed unusual levels of volatility in all principal factors, with the second subperiod, which coincided with the Covid-19 outbreak, being particularly notable. An implication of the study is that given the volatilities of stock trade levels are little studied in the literature, the findings of this study could be a useful complement to existing studies examining stock price volatility for the purposes of mitigating unusual stock trading volatility.

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Exploring the nexus of oil price shocks: Impacts on financial dynamics and carbon emissions in the crude oil industry

This empirical study delves into the intricate interplay between oil price shocks (OPS) and their dual impacts on both carbon emissions and the financial performance of the crude oil industry. Utilizing a sophisticated econometric approach, the study employs a Nonlinear ARDL model to meticulously scrutinize the enduring and immediate associations between oil prices and key financial indicators such as stock prices, dividends, and profit ratios of oil exploration industries. Given the significant fiscal and ecological transformations in China during this period, the chosen factors provide a robust framework for measuring changes at both microeconomic and macroeconomic levels, contributing valuable insights to sustainable economic development strategies. Our findings elucidate a statistically significant influence of OPS on the financial performance of the oil industry, particularly in the short term. The increase in the volatility level of OPS manifests a discernible decline in stock prices and profit ratios. Intriguingly, the study further uncovers a nuanced facet of OPS, revealing a prolonged and beneficial impact on carbon emissions in the long term. More importantly, heightened oil prices serve as a catalyst, motivating oil exploration companies to intensify production and exploration activities, thereby increasing carbon emissions in a direct relationship. The implications of these findings are pivotal for policy making, underscoring the imperative for decision-makers and industry stakeholders to pivot towards cleaner, more sustainable energy sources. The research augments the ongoing discourse surrounding the intricate nexus between energy costs and carbon emissions, furnishing invaluable insights that could steer the development of policies geared towards fostering sustainability and mitigating the adverse effects of climate change.

Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market

AbstractIn this paper, we study the change of Australian electricity price dynamics that was observed before, during and after the two-year period in which a Carbon Pricing Mechanism was in force. We fit a two-states Markov Switching Model, representing a high- and a low-volatility state of the world. To avoid the interference due to periodic patterns, a deseasonalization process accounting for short- and long-term seasonality is carried out prior to the study of volatility. Estimation results highlight that, during the period when the carbon tax applies, the volatility level is lower for both the states of the world. Furthermore, the persistence in the low-volatility state is increased in the presence of the carbon tax. This conclusion is particularly relevant for macroeconomic and investment considerations because the increased uncertainty in electricity prices can significantly influence firms’ investment decisions and shape inflation expectations.

Distributionally robust optimization of a newsvendor model under capital constraint and risk aversion

This paper proposes a capital-constrained newsvendor model with risk aversion under a partially known demand distribution with only knowledge of its mean and variance. We adopt the robust conditional value-at-risk (RCVaR) to characterize the vendor’s risk aversion. Firstly, we obtain the closed-form RCVaR optimal order quantity that depends on the demand volatility level: When demand volatility is low, the vendor has four financing-ordering strategies contingent upon different capital levels. When demand volatility is medium, the vendor does not seek bank loans and is left with two ordering strategies. When demand volatility is high, the vendor does not bother placing an order at all. Then, we investigate the impact of capital constraint, risk aversion and demand volatility on the RCVaR optimal order quantity. Finally, we demonstrate the robustness of the RCVaR optimal ordering policy by numerical experiments based on both randomly generated and real-world data.

Deep learning enhanced volatility modeling with covariates

Exogenous information such as policy news and economic indicators can have the potential to trigger significant movements in financial asset volatility. This article presents a model, called the RECH-X model, that allows incorporating exogenous variables into a recurrent neural network for volatility modeling and forecasting. The RECH-X model can allow for abrupt changes in the volatility level and effectively capture the complex serial dependence structure in the volatility dynamics. We demonstrate in a wide range of applications that the RECH-X model consistently outperforms the benchmark models in terms of volatility modeling and forecasting.

Detecting rough volatility: a filtering approach

In this paper, we focus on filtering and parameter estimation in stochastic volatility models when observations arise from high-frequency data. We are particularly interested in rough volatility models where spot volatility is driven by fractional Brownian motion with Hurst index H < 1 2 . Since volatility is not directly observable, we rely on particle filtering techniques for statistical inference regarding the current level of volatility and the parameters governing its dynamics. In order to obtain numerically efficient and recursive algorithms, we use the fact that a fractional Brownian motion can be represented through a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes). We analyze the performance of our approach on simulated data and we compare it to similar studies in the literature. The paper concludes with an empirical case study, where we apply our methodology to high-frequency data of a liquid stock.

Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation

Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.

The interaction between equity-based compensation and debt in managerial risk choices

This paper examines the risk incentives of traditional and non-traditional call options in the context of a levered firm where managers under-invest due to risk aversion. Our results contrast with those presented in the literature inasmuch as lookback calls do not always induce higher risk taking than regular calls, and managers always prefer a combination of regular calls and shares of stock in their compensation package as opposed to only company shares. We also show that Asian options outperform both plain-vanilla and other nonstandard options in inducing higher risk taking and, thereby, are a superior remedy for alleviating the agency costs of deviating from the optimal volatility level. Finally, we shed new insights that better clarify the incorrect arguments found in the literature regarding the delta of regular and lookback calls.

A Graphical and Beta Analysis of the Effect of Increased Ethanol Production on the Volatility of Corn Prices

Increased demand for corn-based ethanol puts upward pressure on prices of corn and other commodities, such as soybeans, and possibly worsens their price volatility. The paper investigates the changes in agricultural commodities' standard deviation and beta sizes due to ethanol production in the US. Standard deviations and beta estimations are compared for the ethanol pre-expansion and expansion periods. The results indicate a high level of price volatility in the second period, which could be attributed to ethanol expansion.

Risk connectedness between international oil and stock markets during the COVID-19 pandemic and the Russia-Ukraine conflict: Fresh evidence from the higher-order moments

Quantifying the connectedness across the oil and stock markets is vital for global asset allocation, systemic risk regulation, and prudent policy making. However, existing studies are mostly confined to return (first moment) and volatility (second moment) levels. This paper integrates the GARCHSK model with the TVP-VAR extended joint connectedness approach to examine higher-order moment risk connectedness between international oil and stock markets in both G7 and BRICS countries, thereby encompassing a wide range of global markets. This paper also discusses the impacts of geopolitical risks, acts, and threats on the total spillovers of volatility and higher-order moments based on the wavelet coherence method. Empirical results demonstrate that the risk spillovers between oil and stock markets are moment-dependent with the total kurtosis spillovers being stronger than the volatility and skewness spillovers. WTI, Brent, INE oil futures, and the Chinese and Japanese stock markets are consistent net receivers of spillovers whereas the remaining G7 stock markets are net transmitters. G7 and the Russian stock markets transmit significant net spillovers of higher-order moments to oil futures. The dynamic spillovers of volatility, skewness, and kurtosis are all time-varying and highly sensitive to the outbreak of major crisis events, especially the China-US trade war, the COVID-19 outbreak, and the Russia-Ukraine conflict. Driving factor analysis shows that the impacts of geopolitical risks, acts, and threats on total spillovers vary over time and across various frequency bands. Geopolitical risks strongly impact the total skewness spillovers during the crisis periods. Geopolitical acts exert a stronger influence on total kurtosis spillovers than geopolitical risks or threats. These findings can offer practical implications for cross-market investors, portfolio managers, market regulators, and policymakers.

Spike and hike modeling for interest rate derivatives: with an application to SOFR caplets

After the replacement of Libor with alternative reference rates across the world, market participants must now adjust to a variety of changes to both contract terms and market rate dynamics. Focusing mostly on the new SOFR rate in the US, this paper considers the interaction of two important characteristics of SOFR derivatives: the backward-looking settlement style of SOFR floating rate payments; and the historically ‘jagged’ nature of SOFR time series. We introduce mechanisms that allow us to modify existing term structure models to incorporate rich dynamics for both ‘surprising’ and anticipated spikes and hikes in overnight rates. Numerical tests on SOFR-style caplets show that such model enhancements can produce significant effects on implied caplet volatility levels and skews. Besides establishing a practitioner-friendly framework for derivatives pricing, our paper is broadly applicable to scenario generation and risk management in any market with discontinuous rates dynamics.

Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. First, we consider the conventional GARCH-based volatility models. Next, we extend the corresponding GARCH-based forecasting and calculate a specific probability associated with the predicted volatility levels. As the probability evaluation is based on a stochastic model, we develop an advanced data-driven estimation of this probability. The novel statistical estimation we propose uses real market data. The obtained analytical results for the statistical probability of the levels are also discussed in the framework of the integrated volatility concept. The possible application of the established probability estimation approach to the volatility clustering problem is also mentioned. Our paper includes a concrete implementation of the proposed volatility prediction tool and considers a novel trading and volatility estimation module for crypto markets recently developed by the 1ex Trading Board group in collaboration with GoldenGate Venture. We also briefly discuss the possible application of a model combined with the data-driven volatility prediction methodology to financial risk management.

Do remittances increase tax revenues in developing countries? Evidence from the threshold regression models

ABSTRACT This paper provides original econometric evidence on the effect of remittances on tax revenues in developing countries. In applying the system generalized methods of moments (SGMM) and the panel threshold regression method to a sample of 83 developing countries over the period 1990–2019, we find two major results. First, remittances positively and significantly affect tax revenues in developing countries. Second, as far as the nonlinear relationship is concerned, we find two extreme regimes with a smooth shift characterizing the remittances-tax revenue nexus, with respect to conditional variables; remittance effects are positive and significant under the first regime and negative and significant under the second. Furthermore, our findings show that the nonlinear relationship between remittances and tax revenues depends on the levels of informal economy, corruption, and growth volatility.

COMPARATIVE ANALYSIS OF FX AND GOLD RESERVES AMONG MAJOR ECONOMIES: A STUDY FROM Q1 2020 TO Q1 2024

In order to identify fluctuations, management approaches, correlations, and trends, this research examines the foreign currency (FX) and gold reserves of China, Japan, Switzerland, the United States, and India from Q1 2020 to Q1 2024. We found notable fluctuations in Switzerland's foreign exchange reserves via analysis of quarterly data, whereas the United States gold holdings remained steady. We discovered significant variations in resource management techniques using descriptive statistics and ANOVA. We also observed high cross-national correlations, indicating common impacts and interrelated economic policies. The results show that whereas China's changing gold holdings show a more flexible response to changes in the economy, the USA and Switzerland maintain steady gold reserves, indicating cautious financial tactics. According to the report, nations with high levels of volatility should diversify their reserve assets, adopt cautious approaches, improve international economic coordination, evaluate policies on a regular basis, and use predictive analytics to foresee changes in the economy. This research highlights the necessity for flexible and coordinated policies to sustain economic stability in a globally linked financial system. Overall, it offers insightful information on the reserve management procedures of major countries. By maximizing reserve management, policymakers may reduce risks and seize opportunities in a changing economic landscape. This is the goal of the proposals made here.

Quantifying the volatility spillover dynamics between financial stress and US financial sectors: Evidence from QVAR connectedness

This study uses quantile vector-autoregressive to examine volatility connectedness among a global financial stress index (including five categories: credit, equity valuation, funding, safe assets, and volatility) and US financial sectors under low, moderate, and extreme volatility conditions. The dataset includes the special periods covering the global financial crisis, China crisis, COVID-19 pandemic, Russian–Ukrainian war, Silicon Valley Bank failure, and Credit Suisse bank crisis. The findings imply that spillover effects among the series are higher during extreme volatility than during low and moderate volatility periods. During periods of low volatility, the credit category of the financial stress index and the US financial sector indices are net shock transmitters, but during extreme volatility periods, the US financial sectors become net shock receivers alongside the credit and funding categories of the financial stress indices. US financial sectors also exhibit net shock recipient roles at extreme volatility levels during those special periods.

Fortification of Pea and Potato Protein Isolates in Oat-Based Milk Alternatives; Effects on the Sensory and Volatile Profile.

Oat-based milk alternatives (OMAs) are an important alternative to bovine milk, with prevalence of lactose intolerance, as well as soy and nut allergies limiting consumers options. However, OMAs are typically lower in protein content than both bovine milk and soy-based alternatives, with protein quality limited by low lysine levels, which can reduce protein digestibility. Addition of alternative plant proteins may increase the quantity of protein, as well as balancing the amino acid profile. However, plant-based proteins have additional sensory qualities and off-flavours, which may lead to undesirable characteristics when introduced to OMAs. This study aimed to assess the effect of pea and potato protein addition on the sensory profile, volatile profile, colour, and particle size in an OMA control product. Results demonstrated that pea protein contributed to a bitter and metallic taste, astringent aftertaste, and a significantly increased overall aroma correlated with higher levels of key volatiles. Whilst potato protein resulted in less flavour changes, it did lead to increased powdery mouthfeel and mouthcoating supported by a substantially increased particle size. Both protein fortifications led to detectable colour changes and a staler flavour. Fortification of OMA product with the pea protein led to significant sensory, volatile and physical changes, whilst the potato protein led to predominantly physical changes. Further investigation into alternative plant-based proteins is necessary to optimise sensory qualities whilst increasing protein content and the amino acid profile.