High Volatility Research Articles

Consistent asset modelling with random coefficients and switches between regimes

We explore a stochastic model that enables capturing external influences in two specific ways. The model allows for the expression of uncertainty in the parametrisation of the stochastic dynamics and incorporates patterns to account for different behaviours across various times or regimes. To establish our framework, we initially construct a model with random parameters, where the switching between regimes can be dictated either by random variables or deterministically. Such a model is highly interpretable. We further ensure mathematical consistency by demonstrating that the framework can be elegantly expressed through local volatility models taking the form of standard jump diffusions. Additionally, we consider a Markov-modulated approach for the switching between regimes characterised by random parameters. For all considered models, we derive characteristic functions, providing a versatile tool with wide-ranging applications. In a numerical experiment, we apply the framework to the financial problem of option pricing. The impact of parameter uncertainty is analysed in a two-regime model, where the asset process switches between periods of high and low volatility imbued with high and low uncertainty, respectively.

A link between the size and composition of comets

ABSTRACT All cometary nuclei that formed in the early Solar System incorporated radionuclides and therefore were subject to internal radiogenic heating. Previous work predicts that if comets have a pebble-pile structure internal temperature build-up is enhanced due to very low thermal conductivity, leading to internal differentiation. An internal thermal gradient causes widespread sublimation and migration of either ice condensates, or gases released from amorphous ice hosts during their crystallization. Overall, the models predict that the degree of differentiation and re-distribution of volatile species to a shallower near-surface layer depends primarily on nucleus size. Hence, we hypothesize that cometary activity should reveal a correlation between the abundance of volatile species and the size of the nucleus. To explore this hypothesis, we have conducted a thorough literature search for measurements of the composition and size of cometary nuclei, compiling these into a unified data base. We report a statistically significant correlation between the measured abundance of CO/H2O and the size of cometary nuclei. We further recover the measured slope of abundance as a function of size, using a theoretical model based on our previous thermophysical models, invoking re-entrapment of outward migrating high volatility gases in the near-surface pristine amorphous ice layers. This model replicates the observed trend and supports the theory of internal differentiation of cometary nuclei by early radiogenic heating. We make our data base available for future studies, and we advocate for collection of more measurements to allow more precise and statistically significant analyses to be conducted in the future.

ARIMA Models For Kijang Emas Price Forecasting: Pre- and Post-COVID Analysis

Gold has significant economic value in a country's economic landscape, serving as a hedge against inflation, particularly during financial turmoil. In Malaysia, gold is known as Kijang Emas and serves as the official bullion gold coin. The price of gold also impacts stock market dynamics, making understanding its fluctuations essential for risk-averse investors. However, the credibility of gold as an investment has been called into question due to price volatility caused by various factors, including the recent upheaval caused by the COVID-19 pandemic. The goal of this study is to explore the effectiveness of the ARIMA model in modelling and forecasting daily Kijang Emas prices in Malaysia from 2012 to 2022, divided into two phases: pre-COVID-19 and post-COVID-19. Model performance was assessed using metrics such as AIC, MAE, and MAPE. The results that the ARIMA model can analyse and forecast Kijang Emas prices, particularly on post-COVID-19 data with high volatility and uncertainty. This insight is valuable for investors seeking to understand market trends and develop future strategies.

Simultaneous determination of seawater trimethylamine and methanol by purge and trap gas chromatography using dual nitrogen-phosphorus detector and flame-ionization detector

Compounds containing one carbon atom or no carbon-carbon bond (C1 compounds), such as trimethylamine and methanol, are important climate relevant gases in the atmosphere and play key roles in global warming. The ocean is a significant source or sink of such compounds, while the concentrations of trimethylamine and methanol in seawater remain largely unconstrained due to the analytical challenges involved. Therefore, it is necessary to establish a continuous, rapid and sensitive method for the determination of these compounds with high polarity, volatility or solubility at low seawater concentrations. Here we developed a purge and trap system, coupled to a gas chromatography equipped with dual nitrogen phosphorus detector (NPD) and flame ionization detector (FID) for the simultaneous online analysis of trimethylamine and methanol at nanomolar range using a small sample volume (~ 10 mL). The dual detection of trimethylamine and methanol with NPD or FID was achieved by installing a capillary flow splitter between the capillary column and detectors. After modification and optimization of the setup and conditions, excellent linearity (R2 > 0.99) and repeatability (< 6%) were obtained for both compounds; the detection limits for trimethylamine and methanol were 0.3 nM and 17.6 nM, respectively. Using this method, water samples collected from coastal and open ocean were analyzed; trimethylamine and methanol concentrations ranged from 0.6 to 18.8 nM and 26.0 to 256.2 nM, respectively. Collectively, this method allowed for online, rapid, sensitive and simultaneous quantification of trace trimethylamine and methanol concentrations with low-cost instrumentation and small sample volume, which makes it promising for further application in volatile compounds analysis in marine environments.

Programs of study and earnings dynamics

University programs differ in the subsequent earnings processes of their enrollees, including many features that students might care about to differing degrees such as the level of average earnings, earnings growth, and volatility. Do the earnings features of a university program’s enrollees reflect the causal effect of enrolling in that program or the self-selection of students into that program? Would students experience a different earnings process if they enrolled in a different program of study? To estimate the causal impact of enrolling in a program of study on the enrollees’ future earnings process, we exploit a discontinuity built into the Danish national university admissions system, which provides quasi-random assignment of similar applicants to different programs. We leverage the rich cross-program variation in the enrollees’ future earnings processes to measure the impact of entering a program whose enrollees experience high earnings levels, growth, and volatility on their own subsequent earnings level, growth, and volatility. We find that a student’s subsequent earnings levels and volatility – but not their earnings growth – are caused by entering programs of study whose enrollees have those features.

In vitro cholinesterase, lipoxygenase inhibition evaluation of rosemary and coriander essential oil nanoemulsion and characterisation

IntroductionThe low water solubility, high volatility, and instability of essential oils limit the application of various formulations, which can be managed by essential oils nanoemulsions. In this present study, in vitro anti-inflammatory and anticholinesterase activities were evaluated by designing nanoemulsions of rosemary and coriander essential oils with their combinations. MethodsThe chemistry of Rosmarinus officinalis L. (Rosemary, Lamiaceae) and Coriandrum sativum L. (Coriander, Apiaceae) essential oils was confirmed by GC-FID and GC/MS methods. Oil-in-Water (O/W) emulsions of essential oils and their 1:1 (w:w) combination were prepared by water titration. The essential oil/tween 20 ratio of the emulsions were 1:9, 2:8, and 3:7 (w:w) respectively. Characterisation studies were performed by measuring the droplet size, polydispersity index (PDI), zeta potential, and viscosity of the emulsions. Also, resistance to centrifugation, freezing, and heating was determined as part of the stability tests. Anti-inflammatory effects of the essential oils and emulsions were determined by in vitro 5-Lipoxygenase (LOX) inhibition. In addition, in vitro Acetylcholinesterase and Butyrylcholinesterase (AChE and BuChE) effects were evaluated. ResultsThe rosemary (1,8-cineole; 44.1%), coriander (linalool; 74.9%) essential oils, and their nanoemulsion formulations prepared were observed as stable. The anti-inflammatory activity of coriander essential oil was relatively increased by nanoemulsion preparations. However, there was no significant difference in the cholinesterase inhibition effects, when compared. To the best of our knowledge this is the first combination study of bioactivity evaluation of rosemary and coriander nanoemulsion preparations. ConclusionRosemary and coriander essential oil nano emulsifications is a promising application for increasing stability and biological activity as well.

MASTER: Market-Guided Stock Transformer for Stock Price Forecasting

Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and then mixes up temporal representations to establish stock correlations. However, they only consider time-aligned stock correlations stemming from all the input stock features, which suffer from two limitations. First, stock correlations often occur momentarily and in a cross-time manner. Second, the feature effectiveness is dynamic with market variation, which affects both the stock sequential patterns and their correlations. To address the limitations, this paper introduces MASTER, a MArkert-guided Stock TransformER, which models the momentary and cross-time stock correlation and leverages market information for automatic feature selection. MASTER elegantly tackles the complex stock correlation by alternatively engaging in intra-stock and inter-stock information aggregation. Experiments show the superiority of MASTER compared with previous works and visualize the captured realistic stock correlation to provide valuable insights.

Returns and Risks in Dynamic Stocks

In the current scenario of high volatility in the stock exchange, it's crucial for investors to make informed decisions. The surge of millennial investors into the market, aiming for substantial returns, highlights the importance of a solid grasp of statistical tools and measures for sound decision-making. This study delves into the risk and return analysis of various stocks alongside their benchmark indices to decode the stock movements in relation to the market. The primary goal for any investor is to amplify profits while minimizing risks. This research aids investors by offering insights into various statistical methods for analyzing stock risks and returns. The research concentrated on gathering daily data, including the closing prices of stocks from 2019 to 2023, from the stock market. It calculated the daily returns of these stocks and measured their standard deviations, focusing on key sectors such as Automobile, Banking, Finance, FMCG, and IT. These sectors are pivotal in reflecting the country's economic health. Further analysis was conducted using beta and regression analysis to assess the risk associated with the indices compared to market indices like NIFTY AUTOMOBILE, BANK NIFTY, NIFTY FINANCE, NIFTY FMCG, and NIFTY IT Service. This helped in understanding the indices' performance versus the benchmark indices and integrating various statistical tools to differentiate the risk and return profiles across sectors. The study aims to identify which sector presents a favorable investment opportunity in terms of risk and return.

A Comparative Analysis of Risk and Return Among chosen companies against the Benchmark Index in NSE.

In the current scenario of high volatility in the stock exchange, it's crucial for investors to make informed decisions. The surge of millennial investors into the market, aiming for substantial returns, highlights the importance of a solid grasp of statistical tools and measures for sound decision-making. This study delves into the risk and return analysis of various stocks alongside their benchmark indices to decode the stock movements in relation to the market. The primary goal for any investor is to amplify profits while minimizing risks. This research aids investors by offering insights into various statistical methods for analyzing stock risks and returns.

Bank Crisis Boosts Bitcoin Price

Bitcoin (BTC) represents an emerging asset class, offering investors an alternative avenue for diversification across various units of exchange. The recent global banking crisis of 9 March 2023 has provided an opportunity to reflect on how Bitcoin’s perception as a speculative asset may be evolving. This paper analyzes the volatility behavior of BTC in comparison to gold and the traditional financial market using GARCH models. Additionally, we have developed and incorporated a bank index within our volatility analysis framework, aiming to isolate the impact of financial crises while minimizing idiosyncratic risk. The aim of this work is to understand Bitcoin’s perception among investors and, more importantly, to determine whether BTC can be considered a new asset class. Our findings show that in terms of volatility and price, BTC and gold have responded in very similar ways. Counterintuitively, the financial market seems not to have experienced high volatility and significant price swings in response to the March 9th crisis. This suggests a consumer tendency to seek refuge in both Bitcoin and gold.

Fabricating modified cotton wound dressing via exopolysaccharide-incorporated marine collagen nanofibers

One of the challenges in regenerative medicine is the design and production of advanced wound dressings that are simple to use. Tissue engineering and regenerative medicine have focused their research efforts on creating natural biomaterials for wound healing. Biomaterials derived from aquatic species and their waste or by-products are deemed renewable biosources, offering potential applications in pharmacology, biomedicine, and other fields due to their high volatility and energy efficiency. This research combines the strong mechanical stability of cotton gauze and the outstanding biocompatibility of exopolysaccharide(EPS)-incorporated marine collagen nanofibers to fabricate an easily applicable wound healing scaffold. In the current study, collagen-EPS nanofibers were successfully electrospun on a cotton gauze (Col/EPS-Cotton) wound dressing. Then, several techniques such as scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, hydrophilicity, and tensile properties were performed to characterize the scaffolds. The in vitro properties were then evaluated using degradation, cell viability, and cell adhesion tests. The results showed that the scaffold is degradable over fourteen days and samples containing exopolysaccharide showed higher biocompatibility. Finally, in vivo testing on animal models was undertaken to establish that the obtained scaffolds could accelerate wound regeneration.

Empirical Distribution of the U.S. Housing Market during the Great Recession: Nonlinear Scaling Behavior after a Major Crash

This study focuses on the real estate bubble burst in the US housing market during 2007–2008. We analyze the dynamics of the housing market crash and the after-crash sequence during the Great Recession. When a complex system deviates away from its typical path by the occurrence of an extreme event, its behavior is strongly characterized as nonstationary with higher volatility. With the utilization of a robust method, we present the characteristics of the aftershock period and provide useful information about the spatial distribution and the decay process of the aftershock sequence in terms of time. The returns of the housing price indices are well approximated by the empirics of a power law. Although we deal with low-frequency data, a time power-law relaxation pattern is identified. Our findings align with those in geophysics, indicating that the value of the relaxation parameter typically hovers around one and varies across different thresholds.

Hedging effectiveness in EU energy market: A case study

The current conditions in the European Union energy market are closely linked to significant fluctuations in both demand (due to the marked reduction in economic activity across the EU region due to the implementation of protection measures against COVID-19) and supply (characterized by high volatility in electricity pricing due to irregular fluctuations in fossil fuel prices). Given these challenging conditions, energy companies are forced to adopt hedging strategies to navigate the complexities associated with the extensive market risks. This study empirically examines the impact of the implementation of hedging strategies (measured by the value of financial derivatives relative to the total assets of company values) on financial value (assessed using the Tobin Q ratio) of power generation companies operating in the EU region for 2016 - 2021. The results, derived from an econometric study using panel data analysis, revealed that a potential increase in the value of financial derivatives as a percentage of total assets held by power generation firms in the EU region positively affects the Tobin Q index and, consequently, enhance their financial value. There is no statistically significant evidence to support the relationship between the growth of the domestic forward electricity market and the Tobin's Q index.

An interpretable deep learning multi-dimensional integration framework for exchange rate forecasting based on deep and shallow feature selection and snapshot ensemble technology

Accurate exchange rate forecasting is important for the better realization of international economic transactions and international currency investments. However, it is challenging to forecast exchange rates due to their high volatility, nonlinearity, and noisy characteristics. This paper creatively proposes a novel interpretable deep learning multi-dimensional integration framework for foreign exchange rate forecasting based on shallow and deep feature selection and snapshot ensemble technology. The main factors affecting exchange rates are considered at a deep and shallow level, and the feature selection project is implemented. A cluster-based multidimensional learning paradigm is proposed. The framework clusters data and constructs predictive sub-models and then embeds snapshot ensemble techniques into models to improve the robustness of single models. Finally, the results are nonlinearly integrated by a deep learning model. In addition, three datasets and twelve comparative models were used to demonstrate the performance of the models. The empirical results show that the proposed model has the smallest mean absolute percentage error (0.407859%, 0.47134%, and 0.470167%, respectively).

Narratives from GPT-derived networks of news and a link to financial markets dislocations

AbstractWe introduce a novel framework to study the dynamics of news narratives, by leveraging GPT3.5 advanced text analysis capabilities and graph theory. In particular, we focus on a corpus of economic articles from The Wall Street Journal and dynamically extract the main topics of discussion over time, in a completely systematic and scalable fashion. As a simple application of the suggested approach, we show how the structure of such topics of discussion has a statistically significant relationship with the contemporaneous state of financial markets, which can be used to construct an investment strategy or monitor financial risks. Our work is based on the intrinsic ability of GPT models to track the context of sentences within a document, thanks to which we can accurately extract a ranking of the most important entities discussed within each article, and evaluate their entity-specific sentiments. Then, we create a graph for each week of data, in which nodes are the entities retrieved and edges are built from the co-occurrence of such entities within articles. Graph centrality measures are computed over time to track the most representative keywords of topics of discussion, which result in an accurate summary view of the evolution of economic narratives. Fuzzy community detection is finally used to cluster linked entities into a more detailed representation of topics. Such groups of entities are mapped to the related journal articles, which are in turn summarised to reach a highly nuanced and interpretable view of the topics discussed within each week. Linking the features of these topics to the relevant financial market time series, we find that high fragmentation within our networks’ communities relates to moments of financial markets dislocations (i.e. dates with unusually high volatility across asset classes). This result should thus motivate stronger effort within financial research to move beyond ubiquitous sentiment analysis of news and delve deeper into broader and more holistic studies of textual data.

A novel prediction model to evaluate the dynamic interrelationship between gold and crude oil

AbstractGlobal events, such as the pandemic and European conflicts, have caused significant inflation and high volatility in gold and crude oil prices. This has garnered substantial international attention while banks, governments, and businesses are devoted to developing strategies to hedge against the potential impacts of economic uncertainties. Focused on addressing economic uncertainties, this study delves into the crucial role of the relationship between gold and crude oil in shaping global financial and economic dynamics. The primary objective of this study is to conduct a comprehensive analysis and construct a rolling SARIMAX model for predicting the rolling 12-months correlation of the Gold–WTI and Gold–Brent relationship. Monthly data on Gold spot prices, WTI futures, and Brent futures is collected from May 1983 to December 2022. Three sophisticated data analysis techniques, the Rolling Correlation method, the SARIMAX model, and the Rolling Model are integrated to develop the Rolling SARIMAX module. R-square values of this newly developed model achieved 89.8% and 88.4% for predicting the rolling correlation for Gold Spot Price-WTI Futures and Gold Spot Price-Brent Futures, respectively, while the mean absolute percentage error was 10.33% and 10.84%, respectively. The higher accuracy in correlation prediction between gold and crude oil prices can present critical insights for risk management, economic planning, strategic investment, economic cycles, and global economic outlook. This newly developed prediction model adeptly handles both linear and non-linear relationships while adapting to external variables in dynamic market scenarios. Its judicious balance between complexity and practicality positions it as a sophisticated analytical tool with real-world applicability, setting a new benchmark in financial market analysis.

Expected idiosyncratic entropy

PurposeWe propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.Design/methodology/approachWe estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.FindingsWe find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.Originality/valueWe propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Monitoring and predicting dissolved oxygen are an important tool for water quality management, which helps to effectively maintain water ecological balance and prevent environmental problems. A single model cannot describe the dynamic characteristics of dissolved oxygen sequence, which affects the prediction accuracy. In order to obtain more accurate dissolved oxygen prediction results, decomposition techniques are commonly used to extract the main fluctuations and trends of water quality sequences. However, the high-frequency modes obtained from decomposition are still unstable. To solve this problem, this paper proposed a hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit. Firstly, dissolved oxygen sequence is preliminarily decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and obtain several intrinsic mode functions (IMF). The fuzzy entropy (FE) is calculated to quantify the complexity of the IMF. Then, variational mode decomposition improved by northern goshawk optimization is used to decompose the IMF with higher entropy. The nonlinearity and instability of the sequence are further weakened. Finally, the bidirectional gate recurrent unit (BiGRU) neural network is used to predict each IMF component, and the final prediction result is obtained by reconstructing the prediction results of each component. In order to verify the effectiveness of the proposed model, this paper selects the dissolved oxygen data of Xin'anjiang Reservoir as the research object. The experimental results show that the RMSE, MAE, MAPE, and R2 of the proposed model are 0.1164, 0.0894, 1.0403%, and 0.9939, respectively, which is best among other comparative prediction models (BP, LSTM, GRU, BiGRU, EMD-BiGRU, CEEMDAN-BiGRU, VMD-BiGRU, and GNO-VMD-BiGRU). Therefore, this model effectively deals with high volatility and nonlinear dissolved oxygen data and provides reference for water environment management and ecological protection.

The Investigation of LSTM-Random Search with Various Standardization and Normalization Technologies

The stock market, being a complex and chaotic system, presents a formidable challenge for prediction. This paper explores the intricacies of stock prediction by focusing on data preprocessing, specifically investigating the impact of different standardization and normalization techniques on LSTM structures. To enhance predictive accuracy, this study employed a stochastic search technique in this study. It is tested using 1520 data points of the closing prices of Bitcoin and Amazon that were obtained from Kaggle between May 1, 2013, and May 14, 2019. It is discovered that the prediction performance under the LSTM framework is significantly impacted by various data preprocessing techniques. The normalization techniques employing MinMaxScaler and MaxAbsScaler performed better for AMZN, a stock with stable prices, and the RobustScaler normalization method yielded the best results for Bitcoin stock prediction, suggesting that it is more useful for datasets with higher volatility. These results provide a useful guide for future research on stock price prediction by highlighting the significance of selecting the right preprocessing technique based on data characteristics and highlighting the benefits of dynamically modifying the lstm structure for stochastic searches.

Climate and sovereign risk: The Latin American experience with strong ENSO events

Using monthly panel data over the period 2007–2019 for seven Latin American countries, we empirically test the impact of climate shocks, here strong ENSO events (El Niño Southern Oscillations), on sovereign risk. Local Projections are computed to assess the dynamic response of sovereign spreads to ENSO events. Results show that strong El Niño and La Niña shocks lead to a significant increase in sovereign spreads, but with different timing. Strong El Niño shocks are associated with a significant short-term increase in sovereign spreads, while strong La Niña events are associated with a delayed but significant increase in sovereign spreads after a short-term decrease. Thus, our results suggest a potential asymmetry in the effect of these strong ENSO events on sovereign risk. We also highlight high volatility in the dynamics of sovereign spreads, which may reflect an overreaction of investors faced with the high degree of uncertainty generated by the economic and financial consequences associated with strong ENSO events. Complementary time-series estimates suggest that Costa Rica and Peru are especially subject to these effects. Overall, our results provide a warning about the fact that, in the case of Latin American countries, weather shocks associated with strong ENSO events have adverse macroeconomic and financial consequences that can lead to an increase in sovereign risk, hinder their government's ability to act as a ‘climate rescuer’ of last resort, and may be aggravated in the future by climate change.