A Mixture Price Trend Model for Long-Term Risk Management

2010 ◽  
pp. 157-173
Author(s):  
Eric S. Fung ◽  
Wai-Ki Ching ◽  
Tak-Kuen Siu
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Estimation Method ◽  
Real Data ◽  
Price Trend ◽  
Trend Model ◽  
Proposed Model ◽  
Price Trends ◽  
Long Term Forecasting

In financial forecasting, a long-standing challenging issue is to develop an appropriate model for forecasting long-term risk management of enterprises. In this chapter, using financial markets as an example, we introduce a mixture price trend model for long-term forecasts of financial asset prices with a view to applying it for long-term financial risk management. The key idea of the mixture price trend model is to provide a general and flexible way to incorporate various price trend behaviors and to extract information from price trends for long-term forecasting. Indeed, the mixture price trend model can incorporate model uncertainty in the price trend model, which is a key element for risk management and is overlooked in some of the current literatures. The mixture price trend model also allows the incorporation of users’ subjective views on long-term price trends. An efficient estimation method is introduced. Statistical analysis of the proposed model based on real data will be conducted to illustrate the performance of the model.

scholarly journals A multidimensional generalized many-facet Rasch model for rubric-based performance assessment

2021 ◽  
Author(s):  
Masaki Uto
Keyword(s):  
Performance Assessment ◽  
Rasch Model ◽  
Measurement Accuracy ◽  
Estimation Method ◽  
Real Data ◽  
Monte Carlo Algorithm ◽  
Irt Model ◽  
Irt Models ◽  
Proposed Model ◽  
Problem Item

AbstractPerformance assessment, in which human raters assess examinee performance in a practical task, often involves the use of a scoring rubric consisting of multiple evaluation items to increase the objectivity of evaluation. However, even when using a rubric, assigned scores are known to depend on characteristics of the rubric’s evaluation items and the raters, thus decreasing ability measurement accuracy. To resolve this problem, item response theory (IRT) models that can estimate examinee ability while considering the effects of these characteristics have been proposed. These IRT models assume unidimensionality, meaning that a rubric measures one latent ability. In practice, however, this assumption might not be satisfied because a rubric’s evaluation items are often designed to measure multiple sub-abilities that constitute a targeted ability. To address this issue, this study proposes a multidimensional IRT model for rubric-based performance assessment. Specifically, the proposed model is formulated as a multidimensional extension of a generalized many-facet Rasch model. Moreover, a No-U-Turn variant of the Hamiltonian Markov chain Monte Carlo algorithm is adopted as a parameter estimation method for the proposed model. The proposed model is useful not only for improving the ability measurement accuracy, but also for detailed analysis of rubric quality and rubric construct validity. The study demonstrates the effectiveness of the proposed model through simulation experiments and application to real data.

scholarly journals The Weibull Birnbaum-Saunders Distribution And Its Applications

2020 ◽  
Vol 9 (1) ◽  
pp. 61-81
Author(s):  
Lazhar BENKHELIFA
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Reliability Estimation ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Proposed Model ◽  
Modeling Data

A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

V̇o2, V̇co2, and RQ in a respiratory chamber: accurate estimation based on a new mathematical model using the Kalman-Bucy method

2004 ◽  
Vol 96 (3) ◽  
pp. 1045-1054 ◽  
Author(s):  
L. Granato ◽  
A. Brandes ◽  
C. Bruni ◽  
A. V. Greco ◽  
G. Mingrone
Keyword(s):  
Mathematical Model ◽  
Measurement Errors ◽  
Estimation Method ◽  
Accurate Estimation ◽  
Production Rates ◽  
Estimation Errors ◽  
Respiratory Chamber ◽  
Residual Noise ◽  
Proposed Model

A respiratory chamber is used for monitoring O2 consumption (V̇o2), CO2 production (V̇co2), and respiratory quotient (RQ) in humans, enabling long term (24-h) observation under free-living conditions. Computation of V̇o2 and V̇co2 is currently done by inversion of a mass balance equation, with no consideration of measurement errors and other uncertainties. To improve the accuracy of the results, a new mathematical model is suggested in the present study explicitly accounting for the presence of such uncertainties and error sources and enabling the use of optimal filtering methods. Experiments have been realized, injecting known gas quantities and estimating them using the proposed mathematical model and the Kalman-Bucy (KB) estimation method. The estimates obtained reproduce the known production rates much better than standard methods; in particular, the mean error when fitting the known production rates is 15.6 ± 0.9 vs. 186 ± 36 ml/min obtained using a conventional method. Experiments with 11 humans were carried out as well, where V̇o2 and V̇co2 were estimated. The variance of the estimation errors, produced by the KB method, appears relatively small and rapidly convergent. Spectral analysis is performed to assess the residual noise content in the estimates, revealing large improvement: 2.9 ± 0.8 vs. 3,440 ± 824 (ml/min)2 and 1.8 ± 0.5 vs. 2,057 ± 532 (ml/min)2, respectively, for V̇o2 and V̇co2 estimates. Consequently, the accuracy of the computed RQ is also highly improved (0.3 × 10-4 vs. 800 × 10-4). The presented study demonstrates the validity of the proposed model and the improvement in the results when using a KB estimation method to resolve it.

scholarly journals Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models

2020 ◽  
Vol 10 (16) ◽  
pp. 5627 ◽  
Author(s):  
Abdulla I. Almazrouee ◽  
Abdullah M. Almeshal ◽  
Abdulrahman S. Almutairi ◽  
Mohammad R. Alenezi ◽  
Saleh N. Alhajeri
Keyword(s):  
Performance Metrics ◽  
Gaussian White Noise ◽  
Peak Load ◽  
Real Data ◽  
Electric Load ◽  
Peak Loads ◽  
Long Term Forecasting ◽  
Electrical Loads ◽  
Robust To Noise

The rapidly increasing population growth and expansion of urban development are undoubtedly two of the main reasons for increasing global energy consumption. Accurate long-term forecasting of peak load is essential for saving time and money for countries’ power generation utilities. This paper introduces the first investigation into the performance of the Prophet model in the long-term peak load forecasting of Kuwait. The Prophet model is compared with the well-established Holt–Winters model to assess its feasibility and accuracy in forecasting long-term peak loads. Real data of electric load peaks from Kuwait powerplants from 2010 to 2020 were used for the electric load peaks, forecasting the peak load between 2020 and 2030. The Prophet model has shown more accurate predictions than the Holt–Winters model in five statistical performance metrics. Besides, the robustness of the two models was investigated by adding Gaussian white noise of different intensities. The Prophet model has proven to be more robust to noise than the Holt–Winters model. Furthermore, the generalizability test of the two models has shown that the Prophet model outperforms the Holt–Winters model. The reported results suggest that the forecasted maximum peak load is expected to reach 18,550 and 19,588 MW for the Prophet and Holt–Winters models by 2030 in Kuwait. The study suggests that the best months for scheduling the preventive maintenance for the year 2020 and 2021 are from November 2020 until March 2021 for both models.

scholarly journals Geometrical Variable Weights Buffer GM(1,1) Model and Its Application in Forecasting of China’s Energy Consumption

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Li ◽  
Han Xie
Keyword(s):  
Energy Consumption ◽  
Maximum Degree ◽  
Original Data ◽  
Grey Model ◽  
Variable Weights ◽  
Proposed Model ◽  
Term Energy ◽  
Long Term Forecasting ◽  
The Stability

In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China.

scholarly journals On estimation and influence diagnostics for a Bivariate Promotion Lifetime Model Based on the FGM Copula: A Fully Bayesian Computation

2013 ◽  
Vol 14 (3) ◽  
pp. 441 ◽  
Author(s):  
Adriano Kamimura Suzuki ◽  
Francisco Louzada ◽  
Vicente Garibay Cancho
Keyword(s):  
Selection Criteria ◽  
Real Data ◽  
Influential Observations ◽  
Case Deletion ◽  
Model Based ◽  
Influence Diagnostics ◽  
Proposed Model ◽  
Leibler Divergence ◽  
Fully Bayesian

In this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data.

MODELING THE VOLATILITY FOR LONG TERM INTEREST RATE RETURNS IN THE NIGERIA BOND MARKET USING CONDITIONALY HETEROSCEDASTIC MODELS

2020 ◽  
Vol 15 (1) ◽  
pp. 46-56
Author(s):  
Sunday Olaniyan ◽  
Hamadu Dallah
Keyword(s):  
Risk Management ◽  
Interest Rate ◽  
Interest Rates ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Suitable Model ◽  
Financial Assets ◽  
Modeling Process ◽  
Volatility Models

Investigating the volatility of financial assets is fundamental to risk management. This study used generalized Autoregressive Conditional Heteroscedastic Volatility models to evaluate the volatility of the long term interest rate of Nigeria's financial market. We also incorporated three innovations distributions viz: the Gaussian, the student-t, and the Generalized Error Distribution (GED) in the modeling process under the maximum likelihood estimation method. The results show that GARCH (GED) is the most performing model for describing the volatility of three and twenty-year interest rate returns while TARCH (GED) is the most suitable model for describing the volatility of five and ten-year interest rate returns in Nigeria. The preferred models will help in the development of tools for effective risk management by monitoring the behavior of long term interest rates.  

scholarly journals A Model to Decompose Property Rental Multipiers with Regard to the Division Between Land and Building Elements

2016 ◽  
Vol 24 (1) ◽  
pp. 51-63
Author(s):  
S.A. Smolyak
Keyword(s):  
Cash Flow ◽  
Flow Analysis ◽  
Discounted Cash Flow ◽  
Building Element ◽  
New Model ◽  
Proposed Model ◽  
Long Term Forecasting ◽  
The Impact ◽  
Building Elements

Abstract We propose a new model for the decomposition of rental multipliers for the property building element which also supports valuation of income-producing real properties based on the principle of stability and an un-orthodox application of discounted cash flow analysis. Having regard to the building/land element analytical split of overall property, the proposed model explicitly accounts for the impact of the value of underlying land on the decomposition of rental multipliers, and doesn’t require long-term forecasting of income.

scholarly journals LONG-TERM RISK MANAGEMENT FOR UTILITY COMPANIES: THE NEXT CHALLENGES

2010 ◽  
Vol 13 (04) ◽  
pp. 517-535 ◽  
Author(s):  
RENÉ AÏD
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
International Standards ◽  
Management Process ◽  
Quantitative Models ◽  
Risk Management Process ◽  
Management Concepts ◽  
Utility Companies ◽  
Firm Structures

Since the energy markets liberalization at the beginning of the 1990s in Europe, electricity monopolies have gone through a profound evolution process. From an industrial organization point of view, they lost their monopoly on their historical business, but gained the capacity to develop in any sector. Companies went public and had to upgrade their financial risk management process to international standards and implement modern risk management concepts and reporting processes (VaR, EaR…). Even though important evolutions have been accomplished, we argue here that the long-term risk management process of utility companies has not yet reached its full maturity and is still facing two main challenges. The first one concerns the time consistency of long-term and mid-term risk management processes. We show that consistencies issues are coming from the different classical financial parameters carrying information on firms' risk aversion (cost of capital and short-term risk limits) and the concepts inherited from the monopoly period, like the loss of load value, that are still involved in the utility company decision-making process. The second challenge concerns the need for quantitative models to assess their business model. With the deregulation, utilities have to address the question of their boundaries. Although intuition can provide insights on the benefits of some firm structures like vertical integration, only sound and tractable quantitative models can bring answers to the optimality of different possible firm structures.

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