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Multivariate density estimation is plagued by the curse of dimensionality in theory and practice. We propose a hybrid density estimator of a multivariate density f that combines the strengths of the kernel estimator and the exponential series estimator. This estimator refines a preliminary kernel estimate f ̂ 0 with a multiplicative correction that estimates the ratio r = f / f ̂ 0 with an exponential series estimator. Thanks to the consistency of the pilot estimate, the coefficients of the series expansion tend to approach zero asymptotically. Accordingly, we design a thresholding method for basis function selection. A major obstacle of multivariate exponential series estimator is the calculation of its normalization factor. We resolve this difficulty with Monte Carlo integration, using the pilot kernel estimate as the trial density for importance sampling. This approach greatly enhances the practicality of the hybrid estimator. Numerical simulations demonstrate the good finite sample performance of the hybrid estimator. We present one empirical application in financial risk management. [ABSTRACT FROM AUTHOR]

In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector. [ABSTRACT FROM AUTHOR]

Statistical inference for high-dimensional survival data is important for obtaining valid scientific results in many research areas, including biomedical studies and financial risk management. In this paper, a novel framework for feature selection in Cox model is proposed, which achieves false discovery rate (FDR) control asymptotically. The key step is to construct a sequence of ranking statistics based on two independent estimators of the regression coefficients. The FDR control is reached by choosing a data-driven threshold along the ranking of symmetric-based statistics. The de-sparsified estimator and uneven data splitting strategy are employed to improve the robustness of variable selection results and the power in finite sample analysis. We establish the asymptotic FDR control property for the proposed approach at any designated level. Extensive simulation studies and an empirical application on a P2P loan dataset confirm the robustness of the proposed method in FDR control, and show that it often leads to higher power among competitors. [ABSTRACT FROM AUTHOR]

Measuring systemic risk plays an important role in financial risk management to control systemic risk. By means of a vine copula grouped-CoES method, this paper aims to measure the systemic risk of Chinese financial markets. The empirical study indicates that the banking industry has a low risk and a strong ability to resist risks, but also contributes the most of the systemic risk. On the other hand, insurance companies and securities have high ES but low Δ CoES, indicating their low risk tolerance and small contribution to the systemic risk. Furthermore, this study employs a sliding window in Monte Carlo simulation to forecast systemic risk. The findings of this paper suggest that different types of financial industries should adopt different systemic risk measures. [ABSTRACT FROM AUTHOR]

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks. [ABSTRACT FROM AUTHOR]

Taking the green financial ecosystem composed of innovators, green financial institutions and regulators as the object of research, it explores the issue of how to improve the level of efforts of the three types of subjects and the benefits of risk management in the green financial ecosystem. The optimal level of effort, optimal level of return, and optimal level of return on risk management of green financial ecosystems for innovators, green financial institutions, and regulators under the three modes of No-incentive Contract, Cost-sharing Contract, and Synergistic Cooperation Contract are investigated and analyzed respectively, and verified by numerical simulation analysis. The results show: (1) Compared to the No-incentive Contract, the Cost-sharing Contract and the Synergy Cooperation Contract generate more significant incentives, and returns increase over time in both models. (2) The effort level of the participating subjects under the Synergistic Cooperation Contract is the highest, which can realize the Pareto optimization of the participating subjects and the green financial ecosystem at the same time. The study's findings contribute to a deeper understanding of cooperation among innovators, green financial institutions and regulators in facilitating risk management in green financial ecosystems and provide a realistic reference for risk managers in green financial ecosystems. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. Plain Language Summary: Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. Key Points: We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysisWe integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensityWe present global hazard maps consistent with statistical features of TC components and coherent with a global climate model [ABSTRACT FROM AUTHOR]

Bitcoin is an asset with high risks, and a significant part of its volatility can be explained by the speculative component. Parametric variancecovariance (VaR) methods are not applicable for assessing the risks of bitcoin investment, since log returns are not distributed according to the normal law. Autoregressive risk assessment models (such as ARIMA-GARCH) for bitcoin volatility overestimate risks at times of sharp exchange rate changes and they underestimate them at times of less significant rate changes compared to historical volatility. The grid search for the smoothing parameter in the exponentially weighted moving average method is potentially interesting for modeling the risks of bitcoin investment. This makes it possible to fully take into account the autocorrelation of the bitcoin rate to the levels of previous periods and the volatility of the asset. As a conclusion, there are currently no econometric models that can explain and forecast the volatility of bitcoin in the medium and short term, considering the available factors in the market. [ABSTRACT FROM AUTHOR]

With gradually increasing global climate issues, the Energy Protection Agency (EPA) emphasizes emission reduction measures at the national and international levels. Financial globalization and green innovation are considered a solution to the low‐income problem through internalization and specialization of markets and bring technological progress and innovation through open market access. The developed nations have been using financial globalization as the main driver of economic progress. This study examines the effects of financial globalization and green innovation on energy‐related greenhouse gas emissions for G7 countries. Unlike the literature that considered carbon and overall greenhouse gas emissions, this study is novel as it specifically focuses on energy‐related emissions in developed economies. The authors use the panel data of G7 economies from 1990 to 2020. To estimate the individual effects of each explanatory variable on emissions despite the nonlinear data distribution, this study uses the novel method of moment quantile regression (MMQR) approach. The results suggested that financial globalization, income, eco‐innovation, financial risk and human capital significantly influence greenhouse gas emissions in G7 countries, where economic growth and human capital are the leading drivers of energy‐related emissions. However, green technology, financial globalization and financial risk significantly help improve the environmental quality of the region. The results suggested fruitful implications, including increased participation in global economic activities, investment in green innovation, human capital and efficient financial risk management to attain sustainable development. [ABSTRACT FROM AUTHOR]

This study aims to address the gap in the literature by examining the internal factors affecting the implementation of maturity rating policies in Indonesian healthcare settings. This study employs a multimethod-qualitative approach in line with the adopted philosophy, enabling researchers to capture dynamic and contextual elements of organisational actual settings. This research draws several conclusions that are relevant to the implementation of business maturity policies, especially in the context of health care institutions in Indonesia. First, the implementation of the Maturity Rating policy has a significant impact on organizational governance, covering aspects of People, Process, and Technology. Benefits include managerial improvements, operational efficiency, compliance with legal requirements, and financial transparency and accountability. However, it is important to be aware of potential risks, especially conflicts of interest that can undermine objective evaluations. [ABSTRACT FROM AUTHOR]

This paper reviews 17 studies addressing dynamic option hedging in frictional markets through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL models, state and action spaces, reward formulations, data generation processes and results for each study. It is found that policy methods such as DDPG are more commonly employed due to their suitability for continuous action spaces. Despite diverse state space definitions, a lack of consensus exists on variable inclusion, prompting a call for thorough sensitivity analyses. Mean-variance metrics prevail in reward formulations, with episodic return, VaR and CvaR also yielding comparable results. Geometric Brownian motion is the primary data generation process, supplemented by stochastic volatility models like SABR (stochastic alpha, beta, rho) and the Heston model. RL agents, particularly those monitoring transaction costs, consistently outperform the Black–Scholes Delta method in frictional environments. Although consistent results emerge under constant and stochastic volatility scenarios, variations arise when employing real data. The lack of a standardized testing dataset or universal benchmark in the RL hedging space makes it difficult to compare results across different studies. A recommended future direction for this work is an implementation of DRL for hedging American options and an investigation of how DRL performs compared to other numerical American option hedging methods. [ABSTRACT FROM AUTHOR]

Supply chain finance plays a crucial role as a financing channel for small- and medium-sized enterprises (SMEs). However, issues such as financial problems and credit defaults have led to disruptions in this channel. To address credit risk control in SME financing within the field of supply chain finance, this paper focuses on a sample of 506 equipment manufacturing companies listed on the SME board of the Shenzhen Stock Exchange from 2016 to 2020. Taking into consideration, the overall risks faced by these enterprises, the study establishes seven first-level indicators and identifies 84 candidate second-level indicators. Partial correlation and variance analysis are then used for the first round of indicator screening, followed by the use of a BP neural network for the second round of selection. As a result, a system of 26 indicators for supply chain financial risk is constructed. The XGBoost model is employed to evaluate the constructed risk index system, while SVM and random forest models are used as comparison models. Bayesian optimization is utilized for parameter tuning of the three models. Empirical results demonstrate that the BO-XGBoost model reduces prediction errors in comparison to the control models. Furthermore, statistical tests reveal that the predicted values of the BO-XGBoost model significantly differ from those of the other control models. Compared to other individual models, the BO-XGBoost model exhibits increased accuracy in credit risk prediction and a significant discriminative effect. These findings highlight the effectiveness of constructing an efficient risk indicator system and utilizing Bayesian optimization for parameter tuning in XGBoost to better differentiate between risky and normal enterprises, thereby minimizing default losses. The research results underscore the advantages of employing Bayesian optimization in XGBoost, which can be applied in credit default prediction for SMEs and serves as a valuable tool in financial risk management and control. [ABSTRACT FROM AUTHOR]

Quantiles and expected shortfalls are commonly used risk measures in financial risk management. The two measurements are correlated while having distinguished features. In this project, our primary goal is to develop a stable and practical inference method for the conditional expected shortfall. We consider the joint modelling of conditional quantile and expected shortfall to facilitate the statistical inference procedure. While the regression coefficients can be estimated jointly by minimizing a class of strictly consistent joint loss functions, the computation is challenging, especially when the dimension of parameters is large since the loss functions are neither differentiable nor convex. We propose a two‐step estimation procedure to reduce the computational effort by first estimating the quantile regression parameters with standard quantile regression. We show that the two‐step estimator has the same asymptotic properties as the joint estimator, but the former is numerically more efficient. We develop a score‐type inference method for hypothesis testing and confidence interval construction. Compared to the Wald‐type method, the score method is robust against heterogeneity and is superior in finite samples, especially for cases with many confounding factors. The advantages of our proposed method over existing approaches are demonstrated by simulations and empirical studies based on income and college education data. [ABSTRACT FROM AUTHOR]

An objective of model validation within organisations is to provide guidance on model selection decisions that balance the operational effectiveness and structural complexity of competing models. We consider a practice-relevant model validation scenario where a financial quantitative analysis team seeks to decide between incumbent and alternative models on the basis of parameter risk. We devise a model risk management methodology that gives a meaningful distributional assessment of parameter risk in a setting where market calibration and historical estimation procedures must be jointly applied. Such a scenario is typically driven by data constraints that preclude market calibration only. We demonstrate our proposed methodology in a natural gas storage modelling context, where model usage is necessary to support profit and loss reporting, and to inform trading and hedging strategy. We leverage our distributional parameter risk approach to devise an accessible technique to support model selection decisions. [ABSTRACT FROM AUTHOR]

Market liquidity can reflect whether financial market conditions are favorable and is the primary concern for investors when making investment decisions. Therefore, investors' psychological perception and confidence in the quality of products (assets) are particularly important. Using 264 of China's online loan platforms from August 2017 to November 2018, we investigate the impact of the negative psychological perceptions of investors on platform liquidity. The empirical results suggest that the negative psychological perceptions of investors reduce platform liquidity and increase platform liquidity risk. Using the Baidu Search Index to measure investor sentiment, we find that the negative psychological perceptions of investors affect platform liquidity by affecting investor sentiment, which provides a good channel for explaining the main conclusions. Heterogeneity analysis shows that the impact of the negative psychological perceptions of investors on platform liquidity is smaller in high-quality platforms with higher market share and higher registered capital. Meanwhile, we also find that the impact of negative psychological perceptions of investors is greater in private platforms, after the rectification policy, with positive net inflow, and in first- and second-tier cities and coastal cities. Precautionary financial regulatory policies are necessary, not punishment ex post. The research findings of this article can assist investors, platform managers, and regulatory agencies in identifying the liquidity characteristics of platforms, which can contribute to strengthening market liquidity management and financial risk control and provide some reference and support for formulating sustainable development policies in the financial industry. [ABSTRACT FROM AUTHOR]

The financial risks associated with livestock rearing have significantly increased in Punjab, Pakistan. Livestock farmers in this region often face financial uncertainty. This study aims to determine different types of financial risks and relevant coping strategies adopted by livestock farmers at the farm level. The research was conducted in Punjab, Pakistan, a major region for livestock production. The study used a benchmark survey in two purposefully selected districts (Rahim-Yar-Khan and Muzaffargarh), which generated a list of 3808 livestock farmers. A multistage sampling procedure was employed to draw a sample of 400 livestock farmers from the 40 selected villages. The data was analysed by using SPSS which facilitated rigorous examination, and presentation of findings through tables and graph. High input prices (X̅ =4.30), inflation (X̅ =3.96), and low output prices (X̅ =3.38) were the three leading financial risks as perceived by the respondents. As such, personal saving (X̅ =2.54), informal borrowing (X̅ =2.26), and loan allocation (X̅ =2.00) were the first-hand choices of farmers to deal with financial risks. In this study, a binary logistic regression was run to examine the impact of demographic characteristics on financial risk perceptions. The findings revealed that education, income source, land holding, and herd size had statistically significant impacts with the coefficient values of 0.365 (P<0.05), 0.731 (P<0.05), 0.335 (P<0.05) and 0.0.619 (P<0.05), respectively. The findings shows that small farmers in selected districts face a significant challenge of informal borrowing from middlemen, which often traps them in a never-ending cycle of debt. To mitigate financial risks, the government should prioritize the promotion of education, income diversification and affordable insurance options to enhance the financial stability of livestock farmers in the region. [ABSTRACT FROM AUTHOR]

In order to transform the uncontrollable risks of a single enterprise's credit business into a controllable overall risk, and better alleviate the financing difficulties of small and micro enterprises. This article first studies the concept and function of supply chain finance and the meaning and characteristics of supply chain finance, and then analyzes the operating mechanism of the three financing methods and various risks in the financing process. This article combines the unique operation and business process of the Logistic model of the Internet of Things technology to design a new model to further reduce credit risk and market risk. The empirical analysis results show that: facing the financing problems of small and medium-sized enterprises, the current financing model based on supply chain finance is the most ideal choice. By using blockchain decentralization technology to solve the risks of supply chain financial projects, and using the characteristics of blockchain to improve the efficiency of supply chain financing in clearing and settlement. According to data from the Demica Institute, it is estimated that from 2011 to 2013, the financial supply chain developed by international financial institutions will grow at a rate of more than 30%. It is expected that by 2020, its average annual growth rate will maintain 10%. [ABSTRACT FROM AUTHOR]

The object of research is the importance of monetary management in Small and Medium-sized Enterprises (SMEs), specializing in challenges, best practices, and future trends. Financial management in SMEs is an important aspect that influences their growth, sustainability, and competitiveness. The paper begins by defining SMEs and highlighting the significance of financial management for their success. It emphasizes the need for SME owners to understand financial concepts, make informed decisions, and prioritize financial planning to ensure sound business operations. Insights from real-world case studies showcase successful financial management practices adopted by SMEs. Government policies and support for SME financial management are also explored, with a focus on initiatives, tax incentives, and access to financial advisory services. These government interventions play a crucial position in empowering SMEs with the necessary sources and steerage for powerful financial management. Moreover, the evaluation delves into destiny developments, such as rising technology (AI, blockchain, IoT) and regulatory adjustments, and their capacity impact on economic management for SMEs. It discusses the challenges and possibilities in monetary forecasting, highlighting using information analytics and predictive modeling for improved accuracy. In conclusion, this review assessment underscores the significance of financial control for SMEs, emphasizing the want for monetary literacy, era adoption, and compliance with regulatory adjustments. By embracing firstclass practices and authorities’ help, SMEs can reap long-term financial balance and thrive in dynamic commercial enterprise environments. As SMEs preserve to evolve within digital technology, powerful economic control remains vital for his or her sustainable increase and achievement. [ABSTRACT FROM AUTHOR]

Value-at-risk is one of the most popular risk management tools in the financial industry. Over the past 20 years, several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a mixed-integer quadratic programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the more binding optimal Mean-Variance-VaR portfolios seems to be generally better than that of the Equally Weighted and of the Mean-Variance-CVaR portfolios. [ABSTRACT FROM AUTHOR]

This article introduces an innovative analytical method tailored to address the complexities of non-linear FFR (”fractional financial risk”) models. The LRPS (”Laplace residual power se- ries”) approach non-linear FFR models empowers risk analysts to more accurately assess portfolios and predict potential losses span- ning diverse risk categories. These encompass credit risk, market risk, model risk, liquidity risk, and operational risk. By expand- ing the array of techniques for risk modeling, this study offers a valuable asset for refining risk assessment and management strate- gies across these distinct risk domains. Through the utilization of the LRPS approach, this methodology rapidly generates accu- rate solutions, providing an efficient pathway for approximating the intricate non-linear FFR models intrinsic to risk modeling. By means of numerical simulations and graphical representations, the article effectively demonstrates the efficacy of the LRPS technique. This study not only offers a practical and time-efficient tool for fi- nancial risk analysis but also contributes valuable insights to the advancement of novel techniques within the realm of financial risk management. [ABSTRACT FROM AUTHOR]

Modern forest management requires a well-founded knowledge and understanding of all the risks involved in forest management. It requires a wealth of information not only on natural hazards, but also related to the financial aspect of running a business. The most important activity in the risk management phase is to identify all the known risk areas, and on this basis, to determine the appropriate classification of risks accompanying the activities of the entity in question, taking into account the various categories of risk division. In forest management, the global risk of activities should be considered in terms of two main risks: operational risk (including natural and anthropogenic factors), and financial risk, which, in simple terms, is the consequence of decisions and economic processes in an entity's area of activity. Considering the impact of the production factors that affect the results of the activities carried out, it should be emphasized that forest management has a specific distribution of standard production factors. The forest stand plays the role of both a production resource and an object of production, and in the final analysis, represents a production effect. Any consideration of financial risk management in forest management should be carried out based on long-term analytical data series. Another factor for forests under state management is the various functions that forest management performs. Taking into account the ownership criterion cited earlier, it needs to be emphasized that the weighting of individual financial risks may vary depending on the ownership structure of forests in a given country and taking into account the ownership share of forests in the timber sales market. In conclusion, it was pointed out that, unlike other industries, financial risk management in forest holdings can be disrupted by the function of forests, as well as the strong influence in the long term of natural phenomena that have a significant impact on determining the types and ordering the degree of significance of individual risks in the process of identifying them. [ABSTRACT FROM AUTHOR]

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China's regional carbon price. [ABSTRACT FROM AUTHOR]

Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities. [ABSTRACT FROM AUTHOR]

The variance swap is one of volatility derivatives popularly used for the risk management of financial instruments traded in volatile market. An appropriate choice of a volatility model is an important part of the risk management. One of desirable considerations should be given to the fact that the volatility varies on several characteristic time scales. Stochastic volatility models can reflect this feature by introducing multiscale volatility factors. However, pure stochastic volatility models cannot capture the whole volatility surface accurately although the model parameters have been calibrated to replicate the market implied volatility data for near at-the-money strikes. So, we choose a hybrid model of constant elasticity of variance type of local volatility and fast and slow scale stochastic volatility for evaluating the fair strikes of variance swaps. We obtain a closed-form solution formula for the approximate fair strike values of continuously sampled variance swaps and compute the solution. The theoretical formula is validated through Monte Carlo simulation. The predictability of the strike price movements is discussed in terms of the sensitive effects of the stochastic volatility and the elasticity of variance parameters for a given partial information about the underlying asset and volatility. [ABSTRACT FROM AUTHOR]

The Microfinance Institutions (MFIs) in Southern Africa have been condemned as being unprofitable, and unsustainable. These MFIs also suffer due to unhealthy loan portfolios. Motivated by these challenges, this study empirically investigates the relationship between credit risk management and the financial performance of MFIs in Southern Africa. The study uses panel data set for the period 2012-2018 which were extracted from the Microfinance Information Exchange (MIX) online database from a sample of 44 MFIs. The data were analysed using descriptive statistics and regression analysis. A panel data regression model which used lagged financial performance (profitability), productivity and microfinance size as control variables was developed to test the relationship between financial performance (the dependent variable) and credit risk management (the independent variable).The specified model was estimated using a dynamic panel data model, the Generalized Methods of Moment (GMM) estimation technique. The study established that there was a relatively high level of profit persistence in the MFI industry in Southern Africa. The relationship between lagged profitability and financial performance was significant for Return on assets (ROA) and Return on equity (ROE). In addition, the study also found that credit risk management had a significant negative impact on the financial performance of MFIs as measured by both ROA and ROE. Furthermore, the study proved that operational efficiency had a significant negative impact on ROA whereas the personnel productivity ratio had a positive impact on ROA. Surprisingly, the study established that the log of total assets was inversely related to the financial performance of microfinance institutions. Their effects were, however, insignificant in the two estimated regression equations. The study recommends that MFIs put in place policies that help to increase competition in the microfinance industry to improve efficiency and profitability. In addition, the study recommends that management in MFIs should develop policies, procedures, and guidelines to improve the efficiency of risk management. Microfinance institutions should also train their staff to improve efficiency and productivity. [ABSTRACT FROM AUTHOR]

In view of the poor effect of traditional management methods, a quantitative model of financial risk management for forestry enterprises based on nonlinear differential equation is proposed. According to the definition of forestry enterprises, this paper analyzes the development status of forestry enterprises from the quantitative analysis and industrial analysis of forestry enterprises. This paper constructs a nonlinear differential equation model, and analyzes the risks faced by enterprises in operation. The specific application content is studied, and the quantitative model analysis of financial risk management of forestry enterprises is completed. The simulation results show that the management effect of this method is good, which lays a foundation for the protection of forestry economy. [ABSTRACT FROM AUTHOR]

With the accelerated pace of financial globalization and the gradual increase in linkages among financial markets, correctly identifying and describing the risk spillover and network diffusion in the financial system is extremely important for the prevention and management of systemic risk. Based on this, this paper takes the equity markets of 17 countries around the world from 2007 to 2022 as the research object, measures the volatility spillover effect of global financial markets using R-Vine Copula and the DY spillover index, constructs the volatility spillover network of global financial markets, discovers the spillover and diffusion pattern of global financial market risks, and provides relevant suggestions for systemic risk management. It is found that (1) there are certain aggregation characteristics in the network diffusion of global financial market volatility spillover; (2) developed European countries such as the Netherlands, France, the UK, and Germany are at the center of the network and have a strong influence; (3) Asian countries such as China, Japan, and India are at the periphery of the network; and (4) shocks from crisis events enhance the global financial market volatility spillover effect. Based on the above findings, effective prevention of global financial market risk volatility spillover and network diffusion and reduction in systemic risk need to be carried out in two ways. First, by focusing on the financial markets of key countries in the network, such as the Netherlands, the UK, France, and Germany. The second approach is to mitigate the uneven development in global financial markets and reduce the high correlation among them. [ABSTRACT FROM AUTHOR]

An interview is presented with Martin Adler, senior financial risk expert in risk management at the European Central Bank. Topics discussed include the role played by climate change at his work and the European Central Bank; strategies to deal with the risk management; and his work in the field of mathematics.

Currently, the application of big data and artificial intelligence (AI) in financial credit risk management is a research hot spot. Most research has focused on using data and AI algorithms to improve modeling performance, ignoring the difficulty of data sharing. Federated learning has become an effective solution for achieving the "available and invisible" goal of data sharing under the premise that the data do not come out of the local area and meet global data compliance. However, we must find a solution for the poor modeling performance caused by data heterogeneity to truly achieve the goal of credit risk management in the financial industry. This paper proposes an application scheme of federated learning in the field of financial credit risk management. It presents an experimental comparison and analysis to verify the feasibility of the application. The antagonistic verification results show the existence of non-independent and identically distributed (non-IID) data. Moreover, using the Chi-square test method, we detail the modeling, analysis, and evaluation of heterogeneous data with non-IID characteristics. The experimental results demonstrate a performance improvement of 14% by the proposed application framework and method in the financial credit risk management of small banks with few data samples. [ABSTRACT FROM AUTHOR]

A new operator for handling the joint risk of different sources has been presented and its various properties are investigated. The problem of risk evaluation of multivariate risk sources has been studied, and a multivariate risk measure, so-called multivariate average-value-at-risk, m A V a R α , is proposed to quantify the total risk. It is shown that the proposed operator satisfies the four axioms of a coherent risk measure while reducing to one variable average-value-at-risk, A V a R α , in case N = 1. In that respect, it is shown that m A V a R α is the natural extension of A V a R α to N-dimensional case maintaining its axiomatic properties. We further show m A V a R α is flexible by giving the investor the option to choose the risk level α i of each random loss i differently. This flexibility is novel and can not be achieved applying univariate A V a R α with corresponding risk level α to the sum of the risk marginals. The framework is applicable for Gaussian mixture models with dependent risk factors that are naturally used in financial and actuarial modelling. A multivariate tail variance and its connection with m A V a R α is also presented via Chebyshev inequality for tail events. Examples with numerical simulations are also illustrated throughout. [ABSTRACT FROM AUTHOR]

This paper seeks to explore how professionals in the financial sector understand the challenge that climate change presents to economy and society. It is a case study into how 'climate-related financial risk' is understood in a particular area of expertise – within the actuarial profession. There is an increasingly prominent claim among financial regulators that climate change should be considered as an issue of financial risk and stability; it is argued that this will drive capital towards green ends, and an orderly low carbon transition. Responding to this, actuaries are seeking to establish climate-related risk as part of their professional jurisdiction. Yet they are struggling to do so because of their relationship to the investment chain and because the tools they employ for risk management, mostly drawn from financial economics, are fundamentally failing to consider, quantify and financialise climate risks. Instead, the profession is moving toward scenario-based tools for managing climate-related uncertainty that incorporate narratives about policy interventions and market reaction. The paper draws on interviews and ethnographic research conducted with members of the UK-based Institute and Faculty of Actuaries (IFoA) to explore these established and emerging risk management tools and perspectives. [ABSTRACT FROM AUTHOR]

In the era of "Internet plus," the world economy is becoming more and more globalized and informationalized. China's enterprises are facing unprecedented opportunities for their operation and development. However, it is also facing the financial uncertainties brought about by the fluctuations of the general economic environment, and the company is facing increasing financial risks. The reason why most enterprises encounter a serious financial crisis or even close down in the later stage is that they do not pay full attention to the initial financial problems and do not take effective measures to deal with the crisis in time. Financial risk warning has become an important part of modern enterprise financial management. This paper mainly puts forward the optimized BP neural system as the financial early warning model and ensures its high prediction accuracy. In the research, the operation principle and related reasoning process of the model are described, its shortcomings are analyzed, and solutions are put forward. Through the financial risk analysis of listed companies from 2017 to 2020, we find that the correct rate of the prediction results of the financial distress of normal companies in the selected companies based on the optimized BPNN has reached more than 80%, which proves the effectiveness of the optimized BPNN. [ABSTRACT FROM AUTHOR]

Financial risk management is of great importance for all businesses. Because financial risks affect all economic systems. The effects of financial risk factors on companies; Decreased profitability may occur in the form of financial difficulties and bankruptcy. In order to cope with the risk factor, business management financial risk management should be implemented effectively. Businesses can reduce their losses to the lowest level thanks to a good risk management. Businesses provide a decrease in the factors under the damages they may be exposed to by active financial risk methods, while strengthening the weak points and increasing the profit rates. Financial analysis has a very important place in terms of ensuring that companies can maintain their existence and maximize the value of the final purpose of the company. Basic elements of financial analysis; profitability analysis, risk analysis and cash flow analysis. With these elements of financial analysis, it is used to estimate the current financial situation and performance of the firm and its future financial status. In this article study, the financial structure of the five companies in the energy sector was examined comparatively with the ratio analysis techniques. With this examination, it is to reveal the factors of the company's failure with risk management and analysis techniques and compare with other successful companies. As a result of the investigations, the failure of a failed firm in the main activities indicates that the company's profitability and productivity decreases. It has been seen that the unsuccessful firm did not use both financial risk management and financial analysis methods effectively and efficiently. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field. [ABSTRACT FROM AUTHOR]

The majority of small and medium-sized enterprises (SMEs) worldwide are fragile and endangered in facing financial problems due to a lack of financial resources. To overcome these issues, their capabilities based on a Resource-based View (RBV), such as innovativeness and competitiveness, might enable them to reduce their major financial issues related to their financial risk management. In this regard, this paper aims to examine the impacts of SMEs’ innovative and competitive attitudes on their financial risk management. Moreover, this paper examines whether those impacts differ depending on firm size. In line with those purposes, this paper analyzes 1221 Czech, Slovak, and Hungarian SMEs randomly selected from various databases. The researchers employ an online survey to collect the research data from the survey respondents, who are the executives of the analyzed SMEs. Concerning the data analysis, the researchers run Ordinal Logistic Regression Test. According to the results, while innovativeness negatively affects the financial risk management of SMEs, competitiveness does not. On the other hand, the results regarding firm size indicate that more competitive and less innovative microenterprises perform better in financial risk management compared to their less competitive and more innovative counterparts. However, competitiveness and innovativeness do not play determining roles in the financial risk management of small and medium-sized firms. The costs of R&D activities that firms face, the sectors that firms operate, and the educational status of the executives of SMEs might be strong arguments to explain the result of this paper. [ABSTRACT FROM AUTHOR]

Large-scale algae production has garnered interest due to its potential as a biofuel feedstock. Previous research assessing the profitability of algae products has been mostly based on values averaged over time, but algae production and resulting financial returns exhibit significant variability due to weather and fluctuations in selling prices for algae-based products. In other sectors, producers often reduce weather- and market price-related financial risk with financial instruments such as insurance, but little research has been performed on the design of insurance products to protect algae producers. This study develops a novel index-based insurance instrument that pays-out during unfavorable weather and market conditions, then explores the instrument's effectiveness, combined with a cash reserve, in reducing revenue stream variability for an algae producer. Results indicate that a biophysically based index-insurance product tailored to the specific financial risks in algae production can reduce variability in net revenues and can do so at a lower cost than relying solely on cash reserves, the most common financial risk management tool. Assessing the performance of index-insurance in algae production is particularly timely given the passage of the 2018 Farm Bill, which newly opens opportunities for the USDA to provide crop insurance to algae producers. [Display omitted] [ABSTRACT FROM AUTHOR]

In this article, we study the problem of the recursive estimator of the expectile regression of a scalar variable Y given a random variable X that belongs in functional space. We construct a new estimator and study the asymptotic properties over a general functional time structure. Precisely, the strong consistency of this estimator is established, considering that the sampled observations are taken from an ergodic functional process. Next, a simulation experiment is conducted to highlight the great impact of the constructed estimator as well as the ergodic functional time series data. Finally, a real data analysis is used to demonstrate the superiority of the constructed estimator. [ABSTRACT FROM AUTHOR]

Financial investment promotes the market's fast economic growth and gradually becomes a new trend of social development in the contemporary era. From the national level, financial risk investment activities directly affect the development process of the information technology industry and social and economic benefits. The management of financial risk investment has received more attention, and the types and difficulties of risks are also gradually increasing. Financial regulatory agencies urgently need to establish a sensitive and scientific economic hazard early alarm system. The perfect earlier alarm system stands based on in-depth scientific theoretical research, so studying financial security evaluation and systemic economic earlier alarm systems is of great practical significance. Taking the systemic financial risk as the research object, this paper analyzes the mechanism of financial systemic risk. After that, deep learning technology in financial investment has been used for the first time to reconstruct the index system of financial security evaluation and early warning. The application of deep learning technology in the early warning of systemic financial risks is realized, which provides a reliable basis for the regulatory authorities to build a financial risk early warning system and makes empirical research. [ABSTRACT FROM AUTHOR]

G-Brownian motion has potential applications in uncertainty problems and risk measures, which has attracted the attention of many scholars. This study investigates the almost sure exponential stability of nonlinear stochastic delay hybrid systems driven by G-Brownian motion. Due to the non-linearity of G-expectation and distribution uncertainty of G-Brownian motion, it is difficult to study this issue. Firstly, the existence of the global unique solution is derived under the linear growth condition and local Lipschitz condition. Secondly, the almost sure exponential stability of the system is analyzed by applying the G-Lyapunov function and G-Itô formula. Finally, an example is introduced to illustrate the stability. The conclusions of this paper can be applied to the stability and risk management of uncertain financial markets. [ABSTRACT FROM AUTHOR]

This study explored ways to manage financial risk and also risk management in a case of merger and acquisitions. The goals of this study is to 1.) To conduct a critical examination of financial risk management in the field of merger and acquisition, which contains assets calculation, financial analysis, market analysis, and project profitability, as well as the connections between these aspects towards the succession of undergoing a merger and acquisition process. 2.) Review the outcomes of the data analysis in order to reach conclusions and suggestions in the context of successability of mergers and acquisitions. The research strategy that is included in this paper are quantitative research and case study. One of the research strategy is by using a method by David S. Clifton & David E Fyfe from “Project Feasibility Analysis”. Another research strategy that will be used in this paper is the Failure Mode and Effects Analysis. The data collected by the researcher will mainly be coming from PT Indo Kaya Energi in a form of archival method, other than that, some secondary sources from articles and journals on the internet will be used in this research. The study demonstrates that the process of merger and acquisitions needs to be carefully examined, the reason behind this is because the researcher needed to analyze not only the financial statement from the company, but also analyze the market, assets and future project forecast cashflow as stated in “Project Feasibility Analysis”. [ABSTRACT FROM AUTHOR]

The observed increase in osteoarthritis (OA) of the knee as a result of an aging population and the obesity epidemic has led to a concomitant increase in the rates of total knee replacement (TKR), placing an additional financial and social burden on the ability of health care systems to control medical costs. Our study shows how a home-based, noninvasive biomechanical intervention reduced the rate of progression to surgery for a cohort of 237 patients with knee OA deemed eligible for TKR based on pre-established clinical selection criteria. Over the 24-month study period, 204 patients (86%) avoided surgery, with only 33 patients (14%, 95% confidence interval 82%–91%) progressing to a TKR with an average length of time to TKR of 324 days (ranging from 31 to 671 days). The application of this intervention provides health plans and provider networks managing patient care under financial risk arrangements an opportunity to realize significant cost savings without compromising quality of care or clinical outcomes. [ABSTRACT FROM AUTHOR]

In order to generalize the applicability of Conditional Value at Risk, one of the most widely used measurements used in financial risk management, we develop a solution methodology for the conditional expectation (CE)-based simulation optimization problems. To optimize CE-based objective functions in a highly generalized context, we propose a gradient-free, direct search optimization method, called SNM-CE, which inherits the search framework of Stochastic Nelder-Mead (SNM) Simplex Method but further incorporates effective mechanisms designed for handling problems with CE-based objective functions. As we assume the underlying problem is complicated enough that no closed-form expression can represent the objective function, stochastic simulation is applied to estimate CE. We apply Importance Sampling (IS) as a variance reduction technique, which, combined with a newly-developed methodology, called SOCBA-mn, ensures that simulation resources are used with great efficiency. We show that SNM-CE can converge to the true global optimum with probability one (w.p.1) like SNM. An extensive numerical study and a communication system-based empirical study are both conducted to demonstrate the effectiveness, efficiency and viability of this research in both theoretical and practical settings. Note to Practitioners—This paper develops a direct search algorithm, called SNM-CE, used for solving conditional expectation-based simulation optimization problems. By tackling conditional expectation-based problems, SNM-CE fills a gap in the stochastic optimization literature which has traditionally focused on expectation- or quantile-based objective functions. SNM-CE possesses a high degree of flexibility and generalizability which users can benefit from in solving real-world applications. For example, the $\alpha $ value in the CE-based simulation optimization formulation can be freely adjusted. In other words, the quantile value above which the CE is estimated and optimized can be set according to the needs of the practitioner and/or characteristics of the problem to be solved. Also, SNM-CE is designed such that instead of setting the $\alpha $ value, the user may choose to utilize a particular numerical value of the objective function as the threshold for the CE estimation and optimization. SNM-CE is also simple in terms of implementation and, being a direct search method, does not impose many assumptions about the structure of the underlying objective function and does not necessitate the use of gradient information in the search process. As one application example, SNM-CE can be utilized to select the operational parameters which minimize the average delay in the manufacturing of semiconductors given that the delay exceeds the 90th percentile of simulated delay times. [ABSTRACT FROM AUTHOR]

Risk estimation is of great importance in financial risk management. In this study, the risk estimation of the exchange rate portfolio is performed via the stochastic copula approach. This model-based latent process has a parameter that changes over time and thus can model the dependency structure between variables in a comprehensive and dynamic way. First, the marginals of the returns are handled with ARMA-GARCH-type models. Then, the dependency between variables is modeled via the stochastic copula approach. Finally, risk estimates are carried out at 95% and 99% confidence level for the foreign exchange portfolios. It is found that the proposed risk estimation model based on the stochastic copula approach outperforms both classical methods and static copula models. [ABSTRACT FROM AUTHOR]

Currently, the level of economic globalisation is expanding, which gives organizations more room to grow while also subjecting them to an increasing amount of pressure from the market. Companies are forced to deal with an increasing number of unclear aspects due to the unstable internal and external environments, which also increases the risks they confront. A management system for corporate financial risk is according to studies on early warning systems for financial risks. Its goals are to raise the standard of corporate financial management and boost economic advantages, identify concerns and potential hazards in the corporate financial management process, stop corporate financial crises in their tracks, and lessen the losses brought on by such crises. The financial risk management of the organization is predicted and examined in this research using the logistic regression model. The use of a logistic regression model allows for the simultaneous analysis of various risk factors, such as discrete and continuous variables, as well as the analysis of external variables' interactions and confounding. This method is suited for widespread usage in practice because it has shown exceptional outcomes in study that are 16.24% better than those of the conventional method. [ABSTRACT FROM AUTHOR]

This article sheds light on cybersecurity risk disclosure practices, offering explanations based on the corporate governance literature. We argue that cybersecurity risk management poses particular challenges for corporations due to amplified agency problems. Cybersecurity risks are increasing in number and growing in complexity for companies worldwide. The financial sector in the Benelux region was already digitalising rapidly when, in 2020, enhanced remote-working requirements due to the COVID-19 pandemic further contributed to risk exposure. Substantiating our theoretical discussion, we present and discuss insights as to the most pressing cybersecurity risk management issues in the financial sector based on evidence from semi-structured interviews with Chief Information Security Officers/Chief Security Officers from financial sector leads in the Benelux region. We discuss contemporary factors that might induce management to dedicate more attention to cybersecurity. This apparent shift in companies' approaches regarding cybersecurity is likely to encounter obstacles and should not be expected to be an even and linear process, given the challenges of processing and communicating information in an environment featuring high uncertainty and technical complexity as well as potentially misaligned incentives. [ABSTRACT FROM AUTHOR]

Since the introduction of ARCH models close to 40 years ago, a wide range of models for volatility estimation and prediction have been developed and integrated into asset allocation, financial derivative pricing, and financial risk management. Research has also been very active in extending volatility modeling to dependence modeling and in developing our understanding of risk and uncertainty in financial systems. This paper presents a review on the statistical modeling on volatility and dynamic dependence of financial returns. In addition, we present a real data example using a time‐varying copula model to estimate the dynamic dependence of stock returns. Research on volatility and dynamic dependence modeling will continue to encounter statistical and computational challenges; it is necessary to persist in dealing with the 3H (high dimension, high frequency, high complexity) paradigm in modeling. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical Models > Nonlinear ModelsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data [ABSTRACT FROM AUTHOR]