Risk Measures Research Articles

Entropy as a Measure of Risk or a Source of Information to Mitigate Risk: A Comparison Across Various Financial Assets

Since the application of entropy in financial economics has been growing extensively as a measure of volatility, in portfolio selection and to detect anomalies in markets. It’s really complicated to establish that increase in entropy is a source of the useful information for the financial markets that tantamount to mitigate risk, or it is in fact an indicator of disorder reflecting the growing risk scenario in the financial market. To explore the more effective application of entropy in the field of financial economics, this study evaluates entropy in both contexts, as a source of information to mitigate risk and as an indicator of disorder reflecting volatility. Twelve years daily data of 29 financial assets have been used to measure the intrinsic entropy in addition to other eight volatility estimators and three GARCH models-based volatilities. Various assessment techniques are used to test the role of entropy in both contexts including, Run Test, Mean, Variance and Coefficients of Variation, Mean Squared Errors, Proportional Bias and Efficiency Estimator, in addition to spearman rank-order correlation. Results emphasis that entropy is more suitable as a volatility measure rather a source of information in the financial market.

Risk Measurement and Portfolio Optimization Based on NASDAQ 100

Risk Measurement and Portfolio Optimization Based on NASDAQ 100

Distortion risk measures: Prudence, coherence, and the expected shortfall

AbstractDistortion risk measures (DRM) are risk measures that are law invariant and comonotonic additive. The present paper is an extensive inquiry into this class of risk measures in light of new ideas such as qualitative robustness, prudence and no reward for concentration, and tail relevance. Results include several characterizations of prudent DRMs, a novel representation of coherent DRMs as well as an axiomatization of the Expected Shortfall alternative to the one recently provided by Wang and Zitikis. By linking the two axiomatizations, the paper provides a new perspective on the idea of no reward for concentration. The paper also contains results of independent interest such as the lower semicontinuity with respect to convergence in distribution of the Haezendonck–Goovaerts risk measures, the extension of non‐necessarily convex risk measures as well as the structure of the core of a general submodular distortion.

Association between Urine Specific Gravity as a Measure of Hydration Status and Risk of Type 2 Diabetes: The Kailuan Prospective Cohort Study.

Diabetes, especially type 2 diabetes (T2D), poses an unprecedented challenge to global public health. Hydration status also plays a fundamental role in human health, especially in people with T2D, which is often overlooked. This study aimed to explore the longitudinal associations between hydration status and the risk of T2D among the Chinese population. This study used data from the large community-based Kailuan cohort, which included adults who attended physical examinations from 2006 to 2007 and were followed until 2020. A total of 71,526 participants who eventually met the standards were divided into five hydration-status groups based on their levels of urine specific gravity (USG). Multivariable and time-dependent Cox proportional hazards models were employed to evaluate the associations of baseline and time-dependent hydration status with T2D incidence. Restricted cubic splines (RCS) analysis was used to examine the dose-response relationship between hydration status and the risk of T2D. Over a median 12.22-year follow-up time, 11,804 of the participants developed T2D. Compared with the optimal hydration-status group, participants with dehydration and severe dehydration had a significantly increased risk of diabetes, with adjusted hazard ratios (95% CI) of 1.30 (1.04-1.63) and 1.38 (1.10-1.74). Time-dependent analyses further confirmed the adverse effects of impending dehydration, dehydration, and severe dehydration on T2D incidence by 16%, 26%, and 33% compared with the reference group. Inadequate hydration is significantly associated with increased risks of T2D among Chinese adults. Our findings provided new epidemiological evidence and highlighted the potential role of adequate hydration status in the early prevention of T2D development.

Carbon data isn't the whole story: A price signal approach to measure climate transition risk and improve asset pricing

The shift to a green, low-carbon economy is generating new investment strategies and impacting asset prices. In this paper, we isolate price signals related to the low-carbon transition using statistical multi-factor models and the Random Matrix Theory (RMT). This allows us to isolate green and brown stocks, and to build a Brown-minus-Green (BMG) factor which has two purposes. Firstly, the sensitivity of asset prices to this new BMG factor gives us a market measure of climate transition risk that does not involve naturally outdated fundamental climate metrics. Secondly, adding this BMG factor to asset pricing models significantly and robustly improves previous BMG factors used in the literature. We contribute to the nascent literature on climate factors by improving the climate risk measure of enabling activities: we achieve the isolation of companies that allow others to reduce their climate risk and that are challenging to capture when using only greenhouse gas (GHG) emissions or previous BMG factors, due to their high carbon intensities.

Too-systemic-to-fail: Empirical comparison of systemic risk measures in the Eurozone financial system

This paper quantifies the Too-Systemic-To-Fail (TSTF) paradigm in the Eurozone since the introduction of the Euro through three primary dimensions: Too-Big-To-Fail (TBTF), Too-Interconnected-To-Fail (TITF), and Too-Many-To-Fail (TMTF). We apply prominent systemic risk measures based on public data, including the Granger-causality network (GCN), Delta Conditional Value-at-Risk (ΔCoVaR), Marginal Expected Shortfall (MES), and Systemic Risk Index (SRISK). Financial interconnectedness and systemic risk exposure within the 17-member states of the Eurozone are measured on two levels: (i) identifying which financial sectors (banking, diversified financials, insurance, and real estate) are most exposed to systemic risk in the Eurozone at the union level; and (ii) identifying which member state is most exposed to systemic risk within each financial sector at the country level. We extend the original ΔCoVaR, MES and SRISK models by incorporating the bootstrap Kolmogorov-Smirnov stochastic dominance test to rank institutions based on their exposure to systemic risk formally.

Optimal insurance with mean-deviation measures

This paper studies an optimal insurance contracting problem in which the preferences of the decision maker are given by the sum of the expected loss and a convex, increasing function of a deviation measure. As for the deviation measure, our focus is on convex signed Choquet integrals (such as the Gini coefficient and a convex distortion risk measure minus the expected value) and on the standard deviation. We find that if the expected value premium principle is used, then stop-loss indemnities are optimal, and we provide a precise characterization of the corresponding deductible. Moreover, if the premium principle is based on Value-at-Risk or Expected Shortfall, then a particular layer-type indemnity is optimal, in which there is coverage for small losses up to a limit, and additionally for losses beyond another deductible. The structure of these optimal indemnities remains unchanged if there is a limit on the insurance premium budget. If the unconstrained solution is not feasible, then the deductible is increased to make the budget constraint binding. We provide several examples of these results based on the Gini coefficient and the standard deviation.

A neural network framework for portfolio optimization under second-order stochastic dominance

Traditional portfolio optimization strategies often rely on statistical methods and linear programming tools to achieve a balance between return and risk. Despite their usefulness for portfolio optimization, they cannot efficiently capture the specific differences and complexity of real-world financial markets. Several modern approaches attempt to overcome these limitations by using nonlinear models, machine learning, and advanced risk measures. In this study, we propose a novel strategy for optimizing portfolios that incorporates second-order stochastic dominance constraints and solves them with neural networks. we show that portfolios subject to second-order stochastic dominance constraints outperform their traditional counterparts, especially in tail-risk situations.

Strong Comonotonic Additive Systemic Risk Measures

In this paper, we propose a new class of systemic risk measures, which we refer to as strong comonotonic additive systemic risk measures. First, we introduce the notion of strong comonotonic additive systemic risk measures by proposing new axioms. Second, we establish a structural decomposition for strong comonotonic additive systemic risk measures. Third, when both the single-firm risk measure and the aggregation function in the structural decomposition are convex, we also provide a dual representation for it. Last, examples are given to illustrate the proposed systemic risk measures. Comparisons with existing systemic risk measures are also provided.

Stress testing for IInd pillar life-cycle pension funds using hidden Markov model

AbstractThis paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that investor’s risk preferences and beliefs are primarily observed in main world indices and then transmitted to other markets such as IInd pillar life-cycle pension funds. This enables both regime (market state) identification and regime-switching detection. Second, we apply stress to the transition matrix by increasing probabilities of moving to a worse market state, i.e., we increase the transition probabilities on the upper-right side of the main diagonal and decrease the diagonal probabilities correspondingly. Third, future scenarios (evolution) of returns of pension funds are generated using stressed transition matrices and basic risk and performance measures of the final wealth are presented. Finally, three different strategies for pension managers are considered and compared to each other. The results show how the best strategy depends on the stress level.

An analysis of the vulnerability of informal and formal households to disaster risks in the Rand West City region.

The study adds to the body of knowledge by revising some old techniques of addressing disaster risk measures, especially in surrounding mining communities.

DEVELOPMENT OF THE INFORMATION PORTAL “KAZGEOWARNING” FOR FORECASTING FIRES ON THE TERRITORY OF KAZAKHSTAN

This article presents the development of an information system tailored for predicting and managing forest fires in Kazakhstan. The system's primary objective is to establish an information portal dedicated to fire forecasting, enhancing the accuracy of predictions and timely warnings. To achieve this, the system focuses on tasks such as predicting the likelihood of forest and steppe fires, monitoring environmental changes in real-time, and visualising the current situation on maps. It also generates comprehensive reports detailing fire risk, available resources, and necessary measures. Users will receive alerts about potential fire threats, facilitating rapid response. The system's data analysis incorporates geographical and climatic factors for precise forecasting. The ultimate goal is to significantly reduce the environmental and economic impact of forest fires in the country, emphasising the creation of a versatile platform for swift emergency response, integrating modern technologies into practical use. The practical significance encompasses improved fire warnings, enhanced safety for both the population and nature, and a reduction in economic losses. The accompanying website serves as a dynamic platform to collect, analyse, and visualise real-time data on disasters and crises, providing users with geographic information, maps, and reports crucial for making informed decisions during emergency situations.

On the benefits of pension plan consolidation: Understanding the impact of full plan mergers

Abstract This study investigates the benefits and drawbacks of pension plan consolidation by quantifying the impact of mergers of heterogeneous plans on different stakeholders in a unique Canadian implementation of defined benefit plans. Using a comprehensive framework that combines a realistic economic scenario generator, a stochastic mortality model that captures differences among subpopulations, a cost model with economies of scale, and a dynamic asset allocation methodology, we evaluate the combined effect of asset- and liability-side changes on three groups of measures: plan-related risk measures assessing profits from an economic capital perspective, consumption-based metrics to understand the impact on members, and contribution risk measures capturing the risk from the employer’s viewpoint. We apply the framework to a hypothetical and empirically relevant merger and find that consolidation is favorable under most circumstances: the positive impacts of better diversification and economies of scale continue to outweigh the negative effects of heterogeneity even when the merging plans have different mortality expectations, different maturity levels, or modest differences in initial funded ratios.

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures.

Investigating the Components of Perceived Risk Factors Affecting Mobile Payment Adoption

As smartphone ownership rapidly expands, mobile payment options are gaining popularity due to the portability and convenience they offer. This study examines attitudes towards adopting mobile payment, focusing on the component risk, which consists of multiple dimensions including performance, financial, time, psychological, and social risks. The study uses a quantitative approach, collecting data through a survey distributed to mobile payment users, with 361 respondents in the United States. The survey instrument includes measures of performance and psychological risk, as well as attitudes towards mobile payment acceptance. Data analysis using SPSS 25.0 and AMOS 24.0 reveals that both performance and psychological risk significantly negatively impact attitudes towards mobile payment acceptance, underscoring the importance of mobile payment service providers implementing effective risk management policies to improve users’ positive attitudes towards their platforms.

Fully Dynamic Risk Measures: Horizon Risk, Time-Consistency, and Relations with BSDEs and BSVIEs

Fully Dynamic Risk Measures: Horizon Risk, Time-Consistency, and Relations with BSDEs and BSVIEs

Time consistency of dynamic risk measures and dynamic performance measures generated by distortion functions

. The aim of this work is to study risk measures generated by distortion functions in a dynamic discrete time setup and to investigate the corresponding dynamic coherent acceptability indices (DCAIs) generated by families of such risk measures. First, we show that conditional versions of Choquet integrals are indeed dynamic coherent risk measures (DCRMs) and also introduce the class of dynamic weighted value at risk measures. We prove that these two classes of risk measures coincides. In the spirit of robust representations theorem for DCAIs, we establish some relevant properties of families of DCRMs generated by distortion functions and then define and study the corresponding DCAIs. Second, we study the time consistency of DCRMs and DCAIs generated by distortion functions. In particular, we prove that such DCRMs are sub-martingale time consistent, but they are not super-martingale time consistent. We also show that DCRMs generated by distortion functions are not weakly acceptance-time consistent. We also present several widely used classes of distortion functions and derive some new representations of these distortions.

Innovations in Risk Measurement and Management for Strategic Financing Decisions

This study aims to explore the integration of innovative quantitative models, behavioral finance insights, and advanced analytics techniques in contemporary risk management practices to bolster organizations' resilience and informed decision-making within dynamic financial landscapes. Through a systematic literature review spanning disciplines such as finance, psychology, and technology, this research synthesizes diverse insights to comprehensively understand the evolving nature of risk management strategies. The findings elucidate that incorporating advanced quantitative models like Value at Risk (VaR) and Conditional Value at Risk (CVaR) empowers organizations to meticulously assess and quantify financial risks, thereby enhancing risk management accuracy and granularity. Furthermore, the assimilation of behavioral finance insights illuminates the psychological determinants shaping risk perception and decision-making behaviors, leading to the development of more robust risk management frameworks that accommodate both rational and irrational factors. Moreover, leveraging advanced analytics techniques, notably natural language processing (NLP) algorithms, enables proactive risk management by analyzing textual data and market sentiments with heightened precision. This study underscores the imperative of perpetual innovation and adaptability in risk management practices to adeptly navigate the intricacies of modern financial landscapes. By harnessing emerging technologies, behavioral insights, and interdisciplinary methodologies, organizations can fortify their risk management capabilities, mitigate threats, and seize opportunities in today's globally interconnected economy. Addressing the challenges associated with implementing innovative risk management practices necessitates proactive leadership, resource allocation, and organizational culture transformation. Ultimately, this research contributes to advancing the understanding and application of innovative risk measurement and management approaches, fostering sustainable growth and success for organizations.

Analisis Manajemen Strategis Dan Manajemen Risiko Terhadap Bank Mandiri Syariah

This study aims to analyze the implementation of strategic management and risk management at Bank Mandiri Syariah (BMS) and its impact on the bank's performance and stability. This research adopts a qualitative approach by utilizing data from previous studies related to the research topic. The findings of this study indicate that BMS has implemented strong risk management practices, including risk identification, measurement, monitoring, and control. Additionally, strategic management plays a role in determining the direction of the bank and ensuring compliance with Sharia principles. The study concludes that the integration of strategic management and risk management is crucial for BMS to maintain its competitiveness and sustain growth in the dynamic Islamic banking industry.

Risk Analysis of Electricity Demand at Public Electric Vehicle Charging Stations (SPKLU): CVaR Model Approach

The electricity demand at Public Electric Vehicle Charging Stations (SPKLUs) exhibits significant volatility, which is driven by several aspects including electricity demand patterns at specific time intervals, load variability, SPKLU capacity, and other related factors. The variability of these swings can present hazards for SPKLU operators in relation to energy administration as well as operational and financial hazards. The objective of this study is to assess the risk associated with energy demand fluctuation at SPKLUs by employing the Conditional Value-at-Risk (CVaR) model technique. CVaR, or Conditional Value at Risk, is a quantitative measure of risk that calculates the predicted loss value in the most unfavorable situation. It is commonly employed to enhance the risk management approach of SPKLUs. The electricity demand at SPKLU exhibits significant volatility, with an average fluctuation of 10.15% and a standard deviation of 49.67%. The CVAR, calculated at -121.19% for a confidence interval of 1%, represents the maximum potential loss that could be experienced during worst-case electrical demand conditions, highlighting the substantial fluctuations in demand. The study initially implemented the CVaR model to analyze power demand at SPKLU, providing novel perspectives on risk reduction for critical infrastructure and proposing unique strategies for managing demand fluctuations in a reliable and efficient manner. The results also offer comprehensive insights into risk exposure and facilitate the formulation of well-informed and strategic risk management plans.