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Journal articles on the topic 'Risk decomposition'

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Author: Grafiati

Published: 9 March 2023

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1

Schilling, Katja, Daniel Bauer, Marcus C. Christiansen, and Alexander Kling. "Decomposing Dynamic Risks into Risk Components." Management Science 66, no. 12 (December 2020): 5738–56. http://dx.doi.org/10.1287/mnsc.2019.3522.

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The decomposition of dynamic risks a company faces into components associated with various sources of risk, such as financial risks, aggregate economic risks, or industry-specific risk drivers, is of significant relevance in view of risk management and product design, particularly in (life) insurance. Nevertheless, although several decomposition approaches have been proposed, no systematic analysis is available. This paper closes this gap in literature by introducing properties for meaningful risk decompositions and demonstrating that proposed approaches violate at least one of these properties. As an alternative, we propose a novel martingale representation theorem (MRT) decomposition that relies on martingale representation and show that it satisfies all of the properties. We discuss its calculation and present detailed examples illustrating its applicability. This paper was accepted by Baris Ata, stochastic models and simulation.

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2

Mark Peplow, special to C&EN. "DMSO’s decomposition risk analyzed." C&EN Global Enterprise 98, no. 36 (September 21, 2020): 5. http://dx.doi.org/10.1021/cen-09836-scicon2.

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3

DOĞAN, Özlem, and Yunus KILIÇ. "Risk Decomposition in BRICS-T Stock Markets." Gaziantep University Journal of Social Sciences 21, no. 4 (October 19, 2022): 2175–86. http://dx.doi.org/10.21547/jss.1066195.

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In this study, the measurement and decomposition of risk were examined and the risks of the national stock market indices of the BRICS-T countries (Brazil, Russia, India, China, South Africa and Turkey) were measured using the monthly closing data for the 2009-2018 period, based on the US market. S&P 500 index was chosen as the market index. Accordingly, the total risks of the national stock market indices of the BRICS-T countries are calculated and separated into systematic and non-systematic risks. In addition, beta coefficients that measure the sensitivity to market movements were calculated based on 120-month data. The study findings show that the non-systematic risks of national stock exchanges in the BRICS-T community are generally quite low, and most of the risk is the systematic risk factor.

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4

Venter, Gary G., John A. Major, and Rodney E. Kreps. "Marginal Decomposition of Risk Measures." ASTIN Bulletin 36, no. 02 (November 2006): 375–413. http://dx.doi.org/10.2143/ast.36.2.2017927.

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The marginal approach to risk and return analysis compares the marginal return from a business decision to the marginal risk imposed. Allocation distributes the total company risk to business units and compares the profit/risk ratio of the units. These approaches coincide when the allocation actually assigns the marginal risk to each business unit, i.e., when the marginal impacts add up to the total risk measure. This is possible for one class of risk measures (scalable measures) under the assumption of homogeneous growth and by a subclass (transformed probability measures) otherwise. For homogeneous growth, the allocation of scalable measures can be accomplished by the directional derivative. The first well known additive marginal allocations were the Myers-Read method from Myers and Read (2001) and co-Tail Value at Risk, discussed in Tasche (2000). Now we see that there are many others, which allows the choice of risk measure to be based on economic meaning rather than the availability of an allocation method. We prefer the term “decomposition” to “allocation” here because of the use of the method of co-measures, which quantifies the component composition of a risk measure rather than allocating it proportionally to something. Risk adjusted profitability calculations that do not rely on capital allocation still may involve decomposition of risk measures. Such a case is discussed. Calculation issues for directional derivatives are also explored.

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5

Asaturov, Konstantin. "Portfolio Optimization with Risk Decomposition." Moscow University Economics Bulletin 2017, no. 5 (October 30, 2017): 61–85. http://dx.doi.org/10.38050/01300105201754.

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The paper offers the modification of traditional portfolio optimization approach to construct the portfolio with possibility to control both systematic and specific risk (portfolio with risk decomposition). Built on modern econometric tools, the author estimates and forecasts the dynamics of alphas and betas of stocks in the frame of CAPM model, which are further applied for portfolio optimization. The closing weekly prices of 10 Australian stocks and ASX Index as the market index during the period from July 2000 to July 2016 were used. Within the sample there is no evidence of arbitrage on the Australian equity market employing neutral beta portfolio. The study confirms that portfolios with risk decomposition outperform Markowitz’s one according to various performance indicators.

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6

Venter, Gary G., John A. Major, and Rodney E. Kreps. "Marginal Decomposition of Risk Measures." ASTIN Bulletin 36, no. 2 (November 2006): 375–413. http://dx.doi.org/10.1017/s0515036100014562.

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The marginal approach to risk and return analysis compares the marginal return from a business decision to the marginal risk imposed. Allocation distributes the total company risk to business units and compares the profit/risk ratio of the units. These approaches coincide when the allocation actually assigns the marginal risk to each business unit, i.e., when the marginal impacts add up to the total risk measure. This is possible for one class of risk measures (scalable measures) under the assumption of homogeneous growth and by a subclass (transformed probability measures) otherwise. For homogeneous growth, the allocation of scalable measures can be accomplished by the directional derivative. The first well known additive marginal allocations were the Myers-Read method from Myers and Read (2001) and co-Tail Value at Risk, discussed in Tasche (2000). Now we see that there are many others, which allows the choice of risk measure to be based on economic meaning rather than the availability of an allocation method. We prefer the term “decomposition” to “allocation” here because of the use of the method of co-measures, which quantifies the component composition of a risk measure rather than allocating it proportionally to something.Risk adjusted profitability calculations that do not rely on capital allocation still may involve decomposition of risk measures. Such a case is discussed. Calculation issues for directional derivatives are also explored.

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7

Simon, Chad A., Jason L. Smith, and Mark F. Zimbelman. "How Fraud Risk Decomposition Affects Auditors' Fraud Risk Assessments." Current Issues in Auditing 14, no. 1 (January 21, 2020): P26—P32. http://dx.doi.org/10.2308/ciia-52723.

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SUMMARY In this paper, we provide a practitioner summary of our paper “The Influence of Judgment Decomposition on Auditors' Fraud Risk Assessments: Some Trade-Offs” (Simon, Smith, and Zimbelman 2018). In that study, we investigate potential unintended consequences from current auditing guidance on risk assessments. Specifically, auditing standards recommend separate assessments of the likelihood and magnitude of risks (hereafter, LM decomposition) when auditors assess risk. Our study involved several experiments, including one with experienced auditors, where we found evidence that LM decomposition leads auditors to be less concerned about high-risk fraud schemes relative to auditors who make holistic risk assessments. Our other experiments involved non-auditing settings and replicated this finding while exploring potential explanations for it. After providing a summary of our study and its results, we offer concluding remarks on the potential implications of our findings.

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8

Simon, Chad A., Jason L. Smith, and Mark F. Zimbelman. "The Influence of Judgment Decomposition on Auditors' Fraud Risk Assessments: Some Trade-Offs." Accounting Review 93, no. 5 (January 1, 2018): 273–91. http://dx.doi.org/10.2308/accr-52024.

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ABSTRACT Auditing standards recommend separate assessments of the likelihood and magnitude of risks (hereafter, LM decomposition). Prior research shows that decomposition can focus individuals on the components of a judgment and make them more sensitive to information. An experiment with 101 experienced auditors shows that LM decomposition leads auditors to be less concerned about high-risk fraud schemes relative to auditors who make holistic risk assessments. Our analyses also show that, relative to those making holistic risk assessments, the correlation between auditors' likelihood judgments and their overall fraud risk judgments and the coherence of their fraud risk judgments are higher for auditors who perform an LM decomposition. Two follow-up experiments with students replicate these findings for higher-risk events, and (unlike the auditor experiment) we also find that LM decomposition results in lower risk judgments for lower-risk issues. We also find that LM decomposition mitigates the influence of affective responses on high-risk judgments.

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9

Kalev, Petko S., Konark Saxena, and Leon Zolotoy. "Coskewness Risk Decomposition, Covariation Risk, and Intertemporal Asset Pricing." Journal of Financial and Quantitative Analysis 54, no. 1 (December 21, 2018): 335–68. http://dx.doi.org/10.1017/s0022109018000637.

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We develop an intertemporal asset pricing model where cash-flow news, discount-rate news, and their second moments are priced by the market. This model generalizes the market-return decomposition framework, showing that intertemporal considerations imply a decomposition of squared market returns (coskewness risk). Our model accounts for 68% of the return variation across portfolios sorted by size, book-to-market ratio, momentum, investment, and profitability for a modern U.S. sample period. Further, our findings highlight the importance of covariation risk, that is, the risk of simultaneous unfavorable shocks to cash flows and discount rates, in understanding equity risk premia.

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10

Mussard, Stéphane, and Virginie Terraza. "The Shapley decomposition for portfolio risk." Applied Economics Letters 15, no. 9 (July 4, 2008): 713–15. http://dx.doi.org/10.1080/13504850600748968.

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11

Klein, Rudolf F., and Victor K. Chow. "Orthogonalized factors and systematic risk decomposition." Quarterly Review of Economics and Finance 53, no. 2 (May 2013): 175–87. http://dx.doi.org/10.1016/j.qref.2013.02.003.

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12

Mock, Theodore J., Rajendra P. Srivastava, and Arnold M. Wright. "Fraud Risk Assessment Using the Fraud Risk Model as a Decision Aid." Journal of Emerging Technologies in Accounting 14, no. 1 (February 1, 2017): 37–56. http://dx.doi.org/10.2308/jeta-51724.

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ABSTRACT This study investigates the efficacy of using a technology based on an elaboration of the traditional fraud risk model to assess the risk of fraud and subsequently plan the audit. The fraud risk model used is based on Srivastava, Mock, and Turner (2007, 2009) and explicitly assesses the presence of fraud triangle factors and the need for forensic tests to aid in the assessment of fraud detection risk and audit planning. Previous studies that examine fraud risk decomposition simply advise subjects to assess fraud risks separately without an analytical model. We examine the effectiveness of the approach using an experiment involving 76 experienced auditors where specific fraud risks are present or absent. As expected, the results indicate that the model significantly enhances auditors' sensitivity to differences in the level of fraud risks. That is, the auditors using the fraud risk model appropriately assessed low fraud risk as low and high fraud risk as high, whereas the auditors using the traditional Audit Risk Model approach assessed fraud risk at essentially the same level under either risk condition. The experiment also investigates effects on audit program planning decisions. Contrary to expectations but consistent with prior research, the risk decomposition technology tested did not result in auditors providing more effective fraud detection procedures. In all, the results suggest that although the tested risk decomposition technology can enhance risk assessments and recognition of the need for additional forensic tests, auditors continue to have difficulties in responding to fraud risks, perhaps because they lack the requisite fraud experience and training. Data Availability: Copies of the instruments are available from the first author.

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13

Grandits, Peter, Peter Grandits, Christopher Summer, and Christopher Summer. "Risk averse asymptotics and the optional decomposition." Teoriya Veroyatnostei i ee Primeneniya 51, no. 2 (2006): 409–18. http://dx.doi.org/10.4213/tvp64.

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14

de Graaf, C. S. L., D. Kandhai, and C. Reisinger. "Efficient exposure computation by risk factor decomposition." Quantitative Finance 18, no. 10 (April 24, 2018): 1657–78. http://dx.doi.org/10.1080/14697688.2018.1435902.

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15

Grandits, P., and C. Summer. "Risk Averse Asymptotics and the Optional Decomposition." Theory of Probability & Its Applications 51, no. 2 (January 2007): 325–34. http://dx.doi.org/10.1137/s0040585x97982384.

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16

Haensly, Paul J. "Risk decomposition, estimation error, and naïve diversification." North American Journal of Economics and Finance 52 (April 2020): 101146. http://dx.doi.org/10.1016/j.najef.2020.101146.

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17

Abbara, Omar, and Mauricio Zevallos. "Portfolio risk decomposition through pair-copula models." Communications in Statistics: Case Studies, Data Analysis and Applications 3, no. 1-2 (April 3, 2017): 29–40. http://dx.doi.org/10.1080/23737484.2017.1399483.

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18

Rybakowski, Marek, Grzegorz Dudarski, Alena Očkajová, and Ján Stebila. "Assessment of the Fire Risk and Thermal Resistance of Tyres." Advanced Materials Research 805-806 (September 2013): 1771–74. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1771.

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The paper assessed the fire risk of the summer tyres marks Kormoran on the base of thermogravimetric analysis complemented by the analysis and determination of the carbon monoxide (CO) concentration in the decomposition products. Thermogravimetric analysis was performed in an atmosphere of air with flow rate of 4 l/min. During the thermal analysis, the samples were loaded with temperatures from 20 to 550 °C, in the first case by rate of heating 5 °C/min and in second case by 10°C/min. The results of thermal analysis shown that the thermal decomposition of the samples at a heating rate of 5 °C/min started at 320 °C and from the temperature of 370 °C thermal decomposition pass intensively. At a heating rate of 10 °C/min, thermal decomposition of the samples started at 360 °C, from the temperature of 420 °C thermal decomposition pass intensively. The measured concentrations of CO in combustion gases showed that the thermal decomposition of tyres is accompanied by a significant release of gas. The results also shown that the thermal decomposition of the investigated tyres coupled with the release of CO starts at temperatures around 320 °C. That temperature cannot be considered as safe in the medium and long-term exposure.

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19

Tavanaie Marvi, Morteza, and Daniël Linders. "Decomposition of Natural Catastrophe Risks: Insurability Using Parametric CAT Bonds." Risks 9, no. 12 (December 1, 2021): 215. http://dx.doi.org/10.3390/risks9120215.

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Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk into idiosyncratic (and hence insurable) risk and systematic risk (carrying the correlated part). It is explained that the systematic risk can be transferred to capital markets using a set of parametric CAT bonds. Premium calculation is presented for insuring the decomposed risk. Portfolio risk-return trade-off measures for investing on the parametric CAT bond are derived. Multi-regional and multi-hazard parametric CAT bonds are introduced to reduce the risk of the investment. The methodology is applied on a region with about 3000 residential buildings subject to flood hazards.

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20

Cheng, Yao, and Dong Zou. "Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 5 (April 3, 2019): 868–80. http://dx.doi.org/10.1177/1748006x19838129.

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Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

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21

Lin, Bing-Huei, Yueh-Neng Lin, and Yin-Jung Chen. "Volatility risk premium decomposition of LIFFE equity options." International Review of Economics & Finance 24 (October 2012): 315–26. http://dx.doi.org/10.1016/j.iref.2012.04.002.

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22

Ahmed, Shabbir. "Convexity and decomposition of mean-risk stochastic programs." Mathematical Programming 106, no. 3 (October 12, 2005): 433–46. http://dx.doi.org/10.1007/s10107-005-0638-8.

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23

Xiao, Sinan, Zhenzhou Lu, and Pan Wang. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition." Risk Analysis 38, no. 12 (July 5, 2018): 2703–21. http://dx.doi.org/10.1111/risa.13133.

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24

Norman, Carolyn Strand, Anna M. Rose, and Jacob M. Rose. "Internal audit reporting lines, fraud risk decomposition, and assessments of fraud risk." Accounting, Organizations and Society 35, no. 5 (July 2010): 546–57. http://dx.doi.org/10.1016/j.aos.2009.12.003.

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25

Li, Runzhi, Wei Liu, Yusong Lin, Hongling Zhao, and Chaoyang Zhang. "An Ensemble Multilabel Classification for Disease Risk Prediction." Journal of Healthcare Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8051673.

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It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance.

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26

Ercolani, Marco G. "Risk aversion and risk loving in the small: a decomposition of the multivariate risk premium." Bulletin of Economic Research 56, no. 1 (January 2004): 81–106. http://dx.doi.org/10.1111/j.1467-8586.2004.00190.x.

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27

Favere-Marchesi, Michael. "Effects of Decomposition and Categorization on Fraud-Risk Assessments." AUDITING: A Journal of Practice & Theory 32, no. 4 (June 1, 2013): 201–19. http://dx.doi.org/10.2308/ajpt-50528.

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SUMMARY This study examines two issues related to the decomposition of fraud-risk assessments. First, it investigates whether there is a significant difference in the fraud-risk assessment of auditors who decompose the fraud judgment from that of auditors who merely categorize fraud-risk factors. Second, it examines whether the perceived need to modify the audit plan and the extent of testing in response to the fraud-risk assessment is significantly influenced by the decomposition of the fraud judgment. In an experiment with 60 audit managers, auditors who decomposed fraud-risk judgments have significantly different fraud-risk assessments than those of auditors who simply categorized fraud cues. When management's attitude cues are indicative of a low fraud risk, decomposition auditors are significantly more sensitive to changes in incentive and opportunity cues than categorization auditors. Finally, auditors who decompose fraud-risk assessments perceive a significantly higher need to revise audit plans and to increase the extent of audit testing.

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28

Baillon, Aurélien, Aleli Kraft, Owen O’Donnell, and Kim van Wilgenburg. "A behavioral decomposition of willingness to pay for health insurance." Journal of Risk and Uncertainty 64, no. 1 (February 2022): 43–87. http://dx.doi.org/10.1007/s11166-022-09371-2.

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AbstractDespite widespread exposure to substantial medical expenditure risk in low-income populations, health insurance enrollment is typically low. This is puzzling from the perspective of expected utility theory. To help explain it, this paper introduces a decomposition of the stated willingness to pay (WTP) for insurance into its fair price and three behavioral deviations from that price due to risk perception and risk attitude consistent with prospect theory, plus a residual. To apply this approach, we elicit WTP, subjective distributions of medical expenditures and risk attitude (utility curvature and probability weighting) from Filipino households in a nationwide survey. We find that the mean stated WTP of the uninsured is less than both the actuarially fair price and the subsidized price at which public insurance is offered. This is not explained by downwardly biased beliefs: both the mean and the median subjective expectation are greater than the subsidized price. Convex utility in the domain of losses pushes mean WTP below the fair price and the subsidized price, and the transformation of probabilities into decision weights depresses the mean further, at least using one of two specific decompositions. WTP is reduced further by factors other than risk perception and attitude.

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29

Zhang, Ning. "The Modified Mortality Decomposition Model and its Application in the China Longevity Risk Analysis." Advanced Materials Research 756-759 (September 2013): 2912–17. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2912.

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the paper made an adjustment on the mortality decomposition model which was first proposed by the author. The mortality data can be processed by the classical wavelet and HHT methods. Compared with the classical mortality analyzing method, more information about longevity risk can be captured by the adjusted mortality decomposition. As a new development, the adjusted mortality decomposition is more effective for the short data set like China. Also the paper gave a modified form of longevity risk index which is different from that the author introduced in another paper. The new modified index is more suitable for China. Based on the adjusted decomposition of mortality rate data and modified longevity risk index, the paper gave their application and detailed analysis on China longevity risk. The important result of different provinces is also given.

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30

Liang, Priscilla. "Explaining the Risk/Return Mismatch of the MSCI China Index: A Systematic Risk Analysis." Review of Pacific Basin Financial Markets and Policies 10, no. 01 (March 2007): 63–80. http://dx.doi.org/10.1142/s0219091507000982.

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This study examines a risk/return mismatch of the MSCI China Index, which has offered investors low returns and high volatility, yet remains a favorite within the global investors' portfolio. The paper suggests several insights, both from behavioral and traditional finance perspectives, to explain this mismatch. An international risk decomposition model is applied to separate the total risk of China's index return into global systematic risks, regional systematic risks and country specific risks. It suggests the index's lower than average systematic risk might be one of the explanations for its risk/return mismatch. The study also finds that the China Index's systematic risks, both global and regional, have been increasing, but more so at the global level.

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31

Margaretic, Paula. "Emerging Market Risk Premia Fluctuations: A micro founded decomposition." Finance 37, no. 1 (2016): 7. http://dx.doi.org/10.3917/fina.371.0007.

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32

He, Kaijian, and Yingchao Zou. "Crude oil risk forecasting using mode decomposition based model." Procedia Computer Science 199 (2022): 309–14. http://dx.doi.org/10.1016/j.procs.2022.01.038.

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33

Mailhot, Mélina, and Mhamed Mesfioui. "Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios." Risks 4, no. 4 (September 23, 2016): 33. http://dx.doi.org/10.3390/risks4040033.

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34

van Rensburg, P. "A decomposition of style-based risk on the JSE." Investment Analysts Journal 30, no. 54 (January 2001): 45–60. http://dx.doi.org/10.1080/10293523.2001.11082431.

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35

Balduzzi, Pierluigi, and Cesare Robotti. "Asset pricing models and economic risk premia: A decomposition." Journal of Empirical Finance 17, no. 1 (January 2010): 54–80. http://dx.doi.org/10.1016/j.jempfin.2009.09.009.

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36

Furman, Edward, and Zinoviy Landsman. "Risk capital decomposition for a multivariate dependent gamma portfolio." Insurance: Mathematics and Economics 37, no. 3 (December 2005): 635–49. http://dx.doi.org/10.1016/j.insmatheco.2005.06.006.

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37

Collado, Ricardo A., Dávid Papp, and Andrzej Ruszczyński. "Scenario decomposition of risk-averse multistage stochastic programming problems." Annals of Operations Research 200, no. 1 (August 10, 2011): 147–70. http://dx.doi.org/10.1007/s10479-011-0935-y.

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38

Zou, Yingchao, and Kaijian He. "Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model." Mathematics 10, no. 14 (July 11, 2022): 2413. http://dx.doi.org/10.3390/math10142413.

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In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy.

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39

Odutola Omokehinde, Joshua. "Mutual funds behavior and risk-adjusted performance in Nigeria." Investment Management and Financial Innovations 18, no. 3 (September 9, 2021): 277–94. http://dx.doi.org/10.21511/imfi.18(3).2021.24.

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The paper investigates the behavior of mutual funds and their risk-adjusted performance in the financial markets of Nigeria between April 2016 and May 31, 2019, using descriptive statistics, as well as CAPM, Jensen’s alpha, and other risk-adjusted portfolio performance measures such as Sharpe and Treynor ratios, as well as Fama decomposition of return. The descriptive tests revealed that 80.77% of the funds were superior to market returns, while 13.46% were riskier. The market and the fund returns behaved abnormally with asymptotic and leptokurtic characteristics as their skewness and kurtosis varied from the normal requirements. Diagnostically, the normality test by Jacque-Berra showed that the return was not normally distributed at a 1% significance level. The market was more aggressive relative to the funds. The average risk-free rate was 6.75% above the market’s return. The risk-adjusted portfolio returns measured by Sharpe and Treynor ratios showed that 67.31% of the funds underperformed the market compared to 40.38% that outperformed the market using Jensen’s alpha. Fama decomposition of return revealed that the fund managers are risk-averse with 48% superior selection ability and rationally invested over 85% of investors’ funds in schemes with fixed income securities at a given risk-free return that cushioned the negative effects of the systematic and idiosyncratic risks and consequently threw the total returns into positive territories. Overall, the fund managers possessed 52% of inferior selection abilities that only earned 33% of superior risk-adjusted returns and hence, failed to achieve the desired diversification in the relevant period.

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40

WEISENT, J., W. SEAVER, A. ODOI, and B. ROHRBACH. "Comparison of three time-series models for predicting campylobacteriosis risk." Epidemiology and Infection 138, no. 6 (January 22, 2010): 898–906. http://dx.doi.org/10.1017/s0950268810000154.

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SUMMARYThree time-series models (regression, decomposition, and Box–Jenkins autoregressive integrated moving averages) were applied to national surveillance data for campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998–2007 for Minnesota and Oregon, and 1999–2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2 values were 72·2%, 76·3% and 89·9% and validation year R2 values were 66·2%, 52·6% and 79·9% respectively for Georgia, Oregon and Minnesota. All three techniques could be utilized to predict monthly risk of infection for Campylobacter sp. However, the decomposition model provided the fastest, most accurate, user-friendly method. Use of this model can assist public health personnel in predicting epidemics and developing disease intervention strategies.

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Williams, Terry. "The Nature of Risk in Complex Projects." Project Management Journal 48, no. 4 (August 2017): 55–66. http://dx.doi.org/10.1177/875697281704800405.

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Risk analysis is important for complex projects; however, systemicity makes evaluating risk in real projects difficult. Looking at the causal structure of risks is a start, but causal chains need to include management actions, the motivations of project actors, and sociopolitical project complexities as well as intra-connectedness and feedback. Common practice based upon decomposition-type methods is often shown to point to the wrong risks. A complexity structure is used to identify systemicity and draws lessons about key risks. We describe how to analyze the systemic nature of risk and how the contractor and client can understand the ramifications of their actions.

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Dong, Hao, and Zhehao Huang. "Decomposing and reconstructing dynamic risks in the crude oil market based on the VMD and Lempel–Ziv algorithms." Electronic Research Archive 30, no. 12 (2022): 4674–96. http://dx.doi.org/10.3934/era.2022237.

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<abstract> <p>Crude oil markets have become increasingly uncertain. To study them, we first employ the decomposition-ensemble framework based on the variational mode decomposition (VMD) and Lempel–Ziv algorithms to assess the crude oil dual attributes. Three steps are involved: 1) conditional autoregressive value at risk measures the crude oil risk; 2) they are decomposed by the VMD algorithm into submodes; 3) the Lempel–Ziv algorithm is applied to analyze the crude oil risk for each, thereby identifying the oil commodity or oil financial risks. The results of the empirical analysis reveal significantly different amplitudes for the high- and low-frequency crude oil risk. By summarizing the crude oil risk components, we also conclude that the mean value for the oil commodity risk is 0.04, while that for the oil financial risk is 0. What is more, the oil commodity risk is highly related to downward trends in oil prices, while the oil financial risk exerts the same clustering effect as oil returns.</p> </abstract>

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An, Xueli, and Luoping Pan. "Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 2 (February 1, 2017): 200–206. http://dx.doi.org/10.1177/1748006x17693492.

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Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis.

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Sirotkina, Natalya, Elena Shkarupeta, Victoria Kruglyakova, and Anna Batova. "Digital risk management." E3S Web of Conferences 164 (2020): 10055. http://dx.doi.org/10.1051/e3sconf/202016410055.

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Based on the latest statistics, an analysis of indicators, indices and sub-indices wasmade, showing the level of development of information and communication technologies in the world, and a comparative description of Russia with the leading countries was carried out. Using methods of analysis, comparison, induction and decomposition, it is possible to determine which areas of the Russian digital economy are successfully implemented and competitive, and which suffer and need support. The process of global digitalization has been started. In order to reach a higher level of development, it is necessary to increase the share of activities in the field of information in relation to GDP.

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Wang, Rui, Lei Yang, Xuan Lou, and Yang Zhou. "Application of 3D Laser Scanner on Dike Risk Analysis." Applied Mechanics and Materials 341-342 (July 2013): 1085–88. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.1085.

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A dike risk model experiment is introduced in this paper. Researchers use a 3D laser scanner to scan the dike model before and after a simulated flood event and calculate the volume of the erosional fossa. The scan work includes four scan stations located at different places surrounding the dike model. The four point clouds are merged together by ICP (singular value decomposition) and SVD (singular value decomposition) algorithm to create a complete, thorough and detailed virtual 3D model of the experiment dike. Researchers can do kinds survey and calculate work based on the 3D model instead of working on the real dike. The work efficient and data quality are increased significantly.

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Kumaraswamy, Sumathi, and Ibrahim Al Ezee. "Performance evaluation of Saudi equity mutual funds: Fama decomposition model." Investment Management and Financial Innovations 15, no. 4 (November 16, 2018): 158–68. http://dx.doi.org/10.21511/imfi.15(4).2018.13.

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This paper is in pursuit of analyzing and elongating prior research on the performance evaluation of mutual funds by a comparative analysis with three categories of 82 Saudi equity funds during 2011 to 2016 using Fama’s decomposition model. The paper also made an attempt to explore the relationship with the risk reward ratio to the relative performance measure in predicting the future performance of the Saudi equity fund returns. The empirical results show that Saudi local equity funds perform better followed by Arabian and international/global equity funds in terms of expected signs and diagnostic tests.

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Knox, Benjamin, and Annette Vissing-Jorgensen. "A Stock Return Decomposition Using Observables." Finance and Economics Discussion Series 2022, no. 010 (March 18, 2022): 1–58. http://dx.doi.org/10.17016/feds.2022.014.

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We propose a method to decompose stock returns period by period. First, we argue that one can directly estimate expected stock returns from securities available in modern financial markets (using the real yield curve and the Martin (2017) equity risk premium). Second, we derive a return decomposition which is based on stock price elasticities with respect to expected returns and expected dividends. We calculate elasticities from dividend futures. Our decomposition is an alternative to the Campbell-Shiller log-linearization which relies on an assumption about the log-linearization constant. An application to the COVID crisis in 2020 reveals that risk premium changes drove much of the crash and rebound in the S&P500 while a fall in long-term real yields drove a strong positive return for 2020 as a whole.

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Frei, Christoph. "A New Approach to Risk Attribution and Its Application in Credit Risk Analysis." Risks 8, no. 2 (June 16, 2020): 65. http://dx.doi.org/10.3390/risks8020065.

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How can risk of a company be allocated to its divisions and attributed to risk factors? The Euler principle allows for an economically justified allocation of risk to different divisions. We introduce a method that generalizes the Euler principle to attribute risk to its driving factors when these factors affect losses in a nonlinear way. The method splits loss contributions over time and is straightforward to implement. We show in an example how this risk decomposition can be applied in the context of credit risk.

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Zhu, Dan, Lynn Hodgkinson, and Qingwei Wang. "Interaction and decomposition of gender difference in financial risk perception." Journal of Behavioral and Experimental Finance 30 (June 2021): 100464. http://dx.doi.org/10.1016/j.jbef.2021.100464.

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Nendel, Max, Frank Riedel, and Maren Diane Schmeck. "A decomposition of general premium principles into risk and deviation." Insurance: Mathematics and Economics 100 (September 2021): 193–209. http://dx.doi.org/10.1016/j.insmatheco.2021.05.006.

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