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In this paper, we explore the applications of fractional stochastic volatility (FSV) models within the realm of market microstructure theory and optimal execution strategies. FSV models extend traditional stochastic volatility frameworks by incorporating fractional differentiation, allowing for more flexible and realistic representations of asset price dynamics over time. Our investigation begins with an introduction to FSV models, highlighting their ability to capture long-memory effects and volatility clustering observed in financial markets. These models provide a robust framework for understanding market microstructure dynamics, including order flow behavior, price impact functions, and liquidity provision mechanisms. Furthermore, we discuss recent advancements and empirical findings using FSV models, emphasizing their role in uncovering intraday volatility patterns and their implications for trading strategies under varying market conditions. By incorporating these nuanced volatility dynamics, FSV models contribute to the development of optimal execution algorithms that enhance transaction cost efficiency and market stability. The FSV model, when the Hurst exponent H is set to 0.5, effectively reduces to a standard stochastic volatility model. THis nested relationship can be formally demonstrated by considering the FSV model's general form and showing that for H = 0.5, the fractional Brownian motion BH(t) becomes a standard Brownian motion Wt, thereby aligning the FSV model with traditional models. Overall, our analysis underscores the significance of FSV models in advancing both theoretical insights and practical applications in modern finance, offering new avenues for research in high-frequency trading strategies and market efficiency. [ABSTRACT FROM AUTHOR]

Twenty-five machine learning (ML) methods and ordinary least squares regression (OLS) are trained to detect in-sample U.S. annual inflation rates up to a year in advance. The FRED-MD monthly dataset with 134 economic and financial variables from 1959 to April 2022 is used for training, validation, and forecasting. Out of these twenty-five ML methods, top-ten (by root mean square error or RMSE) are chosen to forecast the out-of-sample annual inflation rate. The ML methods are more accurate than the OLS in forecasting the annual inflation rate. The OLS does not appear in the top-10 list in any forecasting period. The ML methods robustly classify the labor market as the top factor in forecasting inflation. The labor market has a significantly higher impact on inflation than the housing or stock market. [ABSTRACT FROM AUTHOR]

The relevance of financial volatility forecasting in efficient decision making regarding risk-related assets cannot be subdued. In the financial world, asset price volatility plays a pivotal role in investment decision making and portfolio setups. The prediction of these volatilities usually deal with noisy and non-stationary data bearing heteroscedastic nature. This paper introduces the volatilityforecastingpackage for financial volatility modelling, forecasting and visualization using state-of-the art algorithms. This package allows recourse to algorithms through a user friendly interface supported by the Mathematica framework, that provides easy access to models for high and low frequency data, while accessibly generating forecasts, estimating errors and generating plots. The package also allows analysis of user data and based on the results, a set of models appropriate for the data is suggested for eventual use. [ABSTRACT FROM AUTHOR]

In this paper, we explore the applications of fractional stochastic volatility (FSV) models within the realm of market microstructure theory and optimal execution strategies. FSV models extend traditional stochastic volatility frameworks by incorporating fractional differentiation, allowing for more flexible and realistic representations of asset price dynamics over time. Our investigation begins with an introduction to FSV models, highlighting their ability to capture long-memory effects and volatility clustering observed in financial markets. These models provide a robust framework for understanding market microstructure dynamics, including order flow behavior, price impact functions, and liquidity provision mechanisms. Furthermore, we discuss recent advancements and empirical findings using FSV models, emphasizing their role in uncovering intraday volatility patterns and their implications for trading strategies under varying market conditions. By incorporating these nuanced volatility dynamics, FSV models contribute to the development of optimal execution algorithms that enhance transaction cost efficiency and market stability. The FSV model, when the Hurst exponent H is set to 0.5, effectively reduces to a standard stochastic volatility model. THis nested relationship can be formally demonstrated by considering the FSV model’s general form and showing that for H = 0.5, the fractional Brownian motion BH(t) becomes a standard Brownian motion Wt, thereby aligning the FSV model with traditional models. Overall, our analysis underscores the significance of FSV models in advancing both theoretical insights and practical applications in modern finance, offering new avenues for research in high-frequency trading strategies and market efficiency.

The problem addressed in this study is the profound impact of the COVID-19 pandemic on the tourism economies of Kosovo (KOS) and Albania (AL), which led to economic–financial stagnation and price increases. The aim was to analyze the financial frontier challenges facing the tourism industry during COVID-19 and beyond and propose effective strategies for shaping a sustainable future for countries within Europe with great potential for tourism development in the current decade. The survey was conducted in 102 locations, including cities, municipalities, regions, villages, and neighborhoods in both countries over the years 2020–2023, while data analysis was performed using a cluster analysis (K-means and hierarchical) and the multidimensional scaling method (Alscal). The results highlighted (a) the severe impact of COVID-19 on both the population and businesses in the tourism sector, which will persist beyond the pandemic, (b) the indispensable role of government intervention in alleviating the financial crisis, (c) the need for innovative approaches and accurate financial management by both the country and businesses to attract tourists, and (d) the importance of control and management for financial sustainability. This paper is of significant importance to tourism destinations as it provides insights into the severe impact of COVID-19 on both the population and businesses in the tourism economies. By highlighting the indispensable role of government intervention, the need for innovative approaches and accurate financial management, and the importance of control and management for financial sustainability, the study offers valuable guidance for tourism destinations in navigating the current crisis and attracting tourists. Furthermore, the paper emphasizes the need for future studies to explore opportunities for long-term financial resilience and growth, contributing to the development of sustainable tourism destinations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Abant Social Sciences / Abant Sosyal Bilimler Dergisi is the property of Journal of Abant Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes. [ABSTRACT FROM AUTHOR]

This study proposes two novel tests for security analyst herding based on binomial correlation and forecast error volatility scaling, and applies it to investigate herding patterns in analyst target prices in 2008–2020 in the UK. Analysts robustly herd in their valuations, with results consistent across years, sectors, in terms of panel fixed effect, quantile, instrumental variable regressions, and when controlled for optimism and conservatism. Herding becomes prominent for stocks followed by at least five analysts and towards the long sides of Fama-French sorts, reinforcing its non-spurious and behavioral nature.

Research background: The global interdependence of financial markets due to globalization has resulted in standardized trading conditions for agricultural commodities, reducing the advantages of portfolio diversification. Recent events between 2020 and 2023 underscore the growing importance of real-time information for investors to make informed decisions in this interconnected financial landscape. Purpose of the article: This article aims to conduct a bibliometric review of the literature about market interdependence. We investigate the contagion effect on agricultural commodities and identify commodities and methods used in the most cited publications from 1997 to 2022. Methods: A bibliometric analysis was developed, for this, the SCOPUS database was used, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project to extract bibliometric information from the database. Findings & value added: The analysis highlights the prominent role of certain countries in contributing to scientific research on this topic, with China and the United States being leaders, collectively producing 24.57% of all publications in the examined periods. The research underscores the global concern for sustainable development, emphasizing the scientific growth linked to this topic and its intersection with energy sources. Unlike other bibliometric studies, this research consolidates relevant methodologies employed in analyzing interdependence and contagion effects in agricultural commodities over the past decades. Additionally, it identifies the most studied commodities in these works. As the world grapples with the challenges of market interdependence, particularly in the wake of recent events between 2020 and 2023, this study underscores the importance of real-time information for informed decision-making. The study suggests a shift towards cleaner and renewable energy sources in the coming years, anticipating increased investments in research and development. [ABSTRACT FROM AUTHOR]

There is good reason to expect crude oil prices to follow nonlinear models. However, previous research has considered the linear assumption to investigate the existence of a unit root. Unit root linear tests such as ADF, PP, and KPSS are provided for linear models. These tests are not suitable for nonlinear time series. Because the model deviation from the linear state may be considered as a random permanent deviation. The purpose of this article is to test the nonlinear unit root of crude oil prices, specifically Brent and WTI oil in the period 2019-2020 daily. For several decades now, various classes of nonlinear models have been introduced. These models introduce a wider range of dynamics than linear models in time series. A special type of these models that economists pay attention to are TAR models. In these models, as in linear models, valid statistical analysis requires distinguishing between the deterministic trend and the stochastic trend. In this study, the Bayesian unit root test for the general SETAR (1) model has been used with respect to the necessary and sufficient conditions for the maintenance of SETAR processes based on the article by Petrocyl and Wolford (1984). A nonlinear unit root test was performed using Bayesian validity interval. The results show that Brent crude oil prices in both regimes contain a unit root that is consistent with similar findings for the production or consumption of crude oil..

The COVID-19 pandemic has introduced significant uncertainty across various economic sectors, most notably in the industrial sector due to the high volatility in copper futures markets. These markets play a crucial role in the construction, electrical networks, electronic products, and industrial machinery industries. Therefore, the aim of this study is to evaluate the impact of the COVID-19 pandemic on the volatility of copper futures returns from 2 January 2018 to 30 December 2022 using an extended ARMA(m,n)–GARCH(p,q) framework. According to the results, the occurrence of the pandemic nearly doubled the long-run equilibrium volatility of returns compared to the values obtained in the pre-pandemic period. Hence, we conclude that the COVID-19 pandemic has a significant influence on the volatility in the copper futures market. [ABSTRACT FROM AUTHOR]

As the competitiveness of the banking sector has a direct effect on the steady state growth rate, through a process of well-functioning educational system, it is expected that capital market sector responds positively to steady economic growth. This is a puzzle that this study identified and put into empirical test. Data on financial market intermediation (as measured by market capitalization, money supply, credit to the private sector, and total reserve) and economic growth (as measured by Real GDP) were obtained from a secondary sources, namely the Central Bank of Nigeria Statistical Bulletin (2017), National Bureau of Statistics (2017) and World Bank Development Index. The study therefore applied stationarity tests (Augmented Dickey Fuller and Phillip Perron) and structural vector autoregressive (SVAR) as analytical techniques in a bid to examine the effects of financial market intermediation on economic growth in Nigeria over a thirty-seven (37)-year period, from 1981 to 2017. Based on the evidence of statistical result, it was established that the estimated parameters attained stationarity at first difference, that is, I(1), the result further expressed that market capitalization has a positive and significant effect on economic growth (z=26.62778; p0.05); money supply has a positive and significant effect on economic growth (z=4.914683; p0.05); credit to the private sector has an inverse and significant effect on economic growth (z=2.707756; p0.05); and lastly, the result disclosed that total reserve has a negative and significant effect on economic growth in Nigeria (z=12.63565; p0.05). According to the study, financial market intermediation has a significant impact on economic growth in Nigeria and can be used to predict economic growth in the Nigerian economy as in other developing economies of the world. The policy implication in the study is that governments need to intensify effort to ensure appropriate policy mix for harmony and proper coordination of economic policies and greater attention directed to money supply that is, greater emphasis should be on the improvement of monetary policies, instruments and institutions in Nigeria to ensure effective and efficient monetary system. The study also implies that government need to strengthen capital market the more in order to remain competitive. [ABSTRACT FROM AUTHOR]

The stochastic volatility (SV) model and its variants are widely used in the financial sector, while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of deep learning. We combine these two methods in a nontrivial way and propose a model, which we call the statistical recurrent stochastic volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects, for example, nonlinearity and long-memory auto-dependence, overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: the German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the U.S. stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the article are available at . [ABSTRACT FROM AUTHOR]

This paper discusses the application of ensemble techniques for the prediction of time series, presenting an in‐depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches. We also discuss the theoretical foundations of the ensemble‐based models, presenting measures of model stability and the main aggregation methods to combine the forecasts of the individual models, as well as recommendations for future developments for related research agendas. [ABSTRACT FROM AUTHOR]

We assess the finite sample performance of simulation estimators that are used to estimate the parameters of dynamic corporate finance models. We formulate an external validity specification test and propose a new set of statistical benchmarks that can be used to estimate and evaluate these models. These benchmarks are based on model policy functions. Our Monte Carlo simulations show that the estimators are largely unbiased with low root mean squared errors. When computed with an optimal weight matrix, the specification tests associated with the estimators are close to correctly sized. These tests have excellent power to detect misspecification. [ABSTRACT FROM AUTHOR]

Copyright of China Terminology is the property of China Terminology Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic has introduced significant uncertainty across various economic sectors, most notably in the industrial sector due to the high volatility in copper futures markets. These markets play a crucial role in the construction, electrical networks, electronic products, and industrial machinery industries. Therefore, the aim of this study is to evaluate the impact of the COVID-19 pandemic on the volatility of copper futures returns from 2 January 2018 to 30 December 2022 using an extended ARMA(m,n)–GARCH(p,q) framework. According to the results, the occurrence of the pandemic nearly doubled the long-run equilibrium volatility of returns compared to the values obtained in the pre-pandemic period. Hence, we conclude that the COVID-19 pandemic has a significant influence on the volatility in the copper futures market.

The FinTech industry has exhibited very high growth levels since the Global Economic and Financial Crisis of 2008. The sector growth has been accelerated because of the disruption caused by COVID-19 and that derived in the global health crisis, a crisis with significant implications for global economic stability. To examine the risk profile of FinTech firms, the CRISP-DM methodology was followed to aid in the implementation of clustering and classification algorithms, combined with time series regression models. This research paper offers insights on financial risk assessment by combining machine learning techniques and traditional econometric modeling to acknowledge challenges associated with the analysis of time series in the financial context and framed in the US FinTech sector. The main findings revealed a lack of significant differences between the FinTech and Non- FinTech firms in the US stock market. The results were surprising as the FinTech sector’s speed of development and fast changes in financial innovation have led to the emergence of significant risks that do not seem to be captured by the examined market and firm-specific data sets. The research outcomes point to a substantial vacuum on the regulatory framework at both national and international levels to ensure efficient FinTech governance and adequate industry development amid very ambitious growing prospects. [ABSTRACT FROM AUTHOR]

We consider directional volatility connectedness among energy markets and financial markets over time and frequencies simultaneously during the period 2007–2018. We utilize and expand Barunik and Krehlik (J Financ Econom 16:271-296, 2018) connectedness measurements using HVAR in order to achieve a better perspective of energy markets. Our results indicate that during a crisis, the connectedness among markets increases dramatically. Furthermore, our findings support that markets are mostly driven by short-term factors and are highly speculative. Among energy markets, Natural Gas Futures contribute the least to other markets in all time frames. Besides, London Gas Oil Futures and Heating Oil Futures collaborate. Currencies and Natural Gas Futures are suitable choices for portfolio managers to hedge their risks especially in the long run. The findings of this article can offer new insights to policymakers about the mechanism of connectedness among different markets and international investors. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal For Economic Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional finance explains all human activity on the ground of rationality and suggests all decisions are rational because all current information is reflected in the prices of goods. Unfortunately, the development of information technology and a growth of demand for new, attractive possibilities of investment caused the process of searching new, unique signals supporting investment decisions. Such a situation is similar to risk-taking, so it must elicit the emotional reactions of individual traders.The paper aims to verify the question that the market risk may be the determinant of traders’ emotions, and if volatility is a useful tool during the investment process as the measure of traders’ optimism, similarly to Majewski’s work (2019). Likewise, various econometric types of models of estimation of the risk parameter were used in the research: classical linear using OLS, general linear using FGLS, and GARCH(p, q) models using maximum likelihood method. Hypotheses were verified using the data collected from the most popular world stock exchanges: New York, Frankfurt, Tokyo, and London. Data concerned stock exchange indexes such as SP500, DAX, Nikkei, and UK100.

The objective of this paper was to verify the interrelationships between the stock returns of 33 Brazilian companies that make up the São Paulo Stock Exchange Index (IBOVESPA), from January 2006 to June 2018, using the principal components analysis (PCA), applied on the residuals of the VAR-GARCH model. In general, the results of this study revealed the presence of interrelation between the stock returns which compose the IBOVESPA, and that the interdependence and the correlation pattern vary over time, which can directly impact the investment decisions of economic and financial agents, especially concerning the diversification of their asset portfolios.

This paper has tried to explain the presence of integration among the stock markets of selected SAARC countries within themselves as well as with selected three Developed countries of the world. The data set consists of daily closing values of benchmark stock indices of selected SAARC and developed countries for more than 27 years period starting from November 5th, 1991 till December 31st, 2018. The data is analysed using Unit Root tests, Correlation Analysis, Vector Autoregression, Granger Causality Test, Impulse Response Analysis, Johansen's Co-integration Test and Vector Error Correction Model. The results indicate the strong influencing nature of USA economy which is found to significantly affect the stock market behaviour of selected SAARC countries both in the short term as well as long term specially after the Asian and Global Financial Crisis. Not much integration has been found among the stock markets of SAARC countries. The extent of linkages among the stock markets has been found to intensify with the passage of time because of opening up of economies, capital market reforms and advancements in Technology. This study is unique because of its emphases on selected SAARC countries, for which there is dearth of literature. Moreover, the time period of study is substantial. Sub-period analysis has also been carried on to check for the impact of Asian and Global Financial Crisis. Therefore, its results can be of great help in formulating portfolio diversification strategies by potential investors. The findings also have noteworthy value for government, regulators, policy makers, academicians and researchers.

This study is the first to investigate the efficient market hypothesis in its weak form and the random walk behaviour of globally listed private equity (LPE) markets represented by nine global, regional, and style indices based on weekly data covering the period from January 2004 to December 2020. Autocorrelation tests, variance ratio tests, and a non-parametric runs test are employed. The results of the autocorrelation tests and the variance ratio tests tend to correspond for all indices, and they reject the random walk hypothesis for the returns of all LPE indices under investigation. In contrast, the runs test for direct weak-form market efficiency cannot reject the null hypothesis of a random walk process for almost all LPE indices under investigation. Furthermore, there is no evidence that the market efficiency of globally listed private equity markets has improved after the global financial crisis. Due to the fact that the rapidly growing asset class of LPE as a form of private equity is still relatively unknown, the implications of the results of our paper are relevant for investors, policy makers, and academics alike. In addition, the results provide valuable insights to better understand the emerging asset class of LPE. [ABSTRACT FROM AUTHOR]

This study focuses on volatility estimation using asymmetric GARCH family models in financial series of S&P BSE LargeCap index collected from BSE Limited (formerly known as Bombay Stock Exchange) of India. The objective of this paper is to investigate volatility in market, asymmetry in volatility, measure short and long term volatility persistence and impact of news on market. This study considers data from 01:2005 to 05:2020 counting 3818 daily observations and follows GARCH (1, 1), EGARCH (1, 1) and GJR (1, 1). The empirical results indicate the following:1) presence of changing asymmetry in series returns of S&P BSE LargeCap index and evidence of leverage effect, 2) presence of abnormal volatility shocks which indicates high investment risk, 3) estimated impact of news and effect on market and 4) an opportunity for investment and return prospects. Main results and findings include all data statistics outcomes with graphical explanations. Furthermore, detailed result statistics available in full for GARCH and GARCH family models. [ABSTRACT FROM AUTHOR]