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Record ID: 160 [ Page 1 of 16, No. 1 ]
Modelling Portfolio Risk and Diversification Effects of a Portfolio Using the Exponential Distribution – Bivariate Archimedean Gumbel Copula Model
Authors: Owen Jakata and Delson Chikobvu
Abstract:
This study uses the Archimedean Gumbel copula model to construct the dependence structure and joint probability distributions using the Exponential Distribution as the marginal distribution to asset returns. The main objective of this study is to estimate the diversification effects of investing in a portfolio consisting of two financial assets, viz: the South African Industrial and Financial Indices. The Exponential Distribution is used as the marginal distribution of the returns, instead of the Normal distribution, to better characterise the financial returns of the two assets. The scatterplots indicate that the dependence in gains, as well as the losses are better captured using the Archimedean Gumbel copula. Monte Carlo simulation of an equally weighted portfolio of the two financial assets is used to model and quantify the risk of the resultant portfolio. The results confirm that there are benefits in diversification, since the riskiness of the portfolio is less than the sum of the risk of the two financial assets. It is less risky to invest in diversified portfolios that includes assets from the two different industries/stock markets. Due to dependence and contagion between Global stock markets, the findings of this study are useful information for the local and international investors seeking a portfolio which include developing countries’ stock market Indices containing, say the South African financial assets. This study provides investors with a framework to quantify diversification effects, which allows for the avoidance of extreme risks, whilst benefiting from extreme gains.
Keywords: Expected Shortfall. Monte Carlo simulation, Value-at-Risk
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Year: 2023 Vol.: 72 No.: 1
Record ID: 159 [ Page 1 of 16, No. 2 ]
Local Quadratic Regression: Maximizing Performance via a Modified PRESS** for Bandwidths Selection
Authors: E. Edionwe and O. Eguasa
Abstract:
In the application of nonparametric regression model, it is a well-established fact that the bandwidth - also called smoothing parameter- is the single most crucial parameter that determines the quality of the estimated responses that are obtained from the regression procedure, and that its choice (how small or large the size) is hugely influenced by the criterion that is applied for its selection. Under small-sample settings, which is typical of response surface studies, the penalized Prediction Error Sum of Squares (PRESS**) criterion is recommended for selecting this all-important parameter. However, for the purpose of selecting bandwidths of improved statistical properties, we propose a modified version of the PRESS** criterion specifically for Local Quadratic Regression (LQR) model. Results from simulated data as well as those from two popular problems from the literature show that LQR procedure that utilizes the bandwidths selected via the proposed modified criterion performs outstandingly better than its counterpart that utilizes bandwidths selected via PRESS** criterion.
Keywords: Desirability Function, Hat matrix, Penalized Prediction Error Sum of Squares, Response Surface Methodology
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Year: 2023 Vol.: 72 No.: 1
Record ID: 158 [ Page 1 of 16, No. 3 ]
Spatiotemporal Patterns of COVID-19 Cases in Quezon City, Philippines
Authors: Tricia Janylle B. Sta. Maria, Nancy E. Añez-Tandang, and Edrun R. Gayosa
Abstract:
Various studies have been undertaken to explore the spatial characteristics of the COVID-19 pandemic. However, only a few have considered the pandemic's temporal characteristics to assess space-time dynamics. This study focuses on COVID-19 spatiotemporal patterns in Quezon City, Philippines from November 2020 to October 2021. Spatial clustering and spatiotemporal patterns were analyzed based on a space-time cube (STC). Results showed that hot spots and cold spots were found in the city's northern and southern parts, respectively. Also, a significant increasing pattern was revealed throughout the study period. Moreover, STC analysis demonstrated that intensifying hot spots or locations that were statistically significant hot spots for 90% of the study period and the intensity of clustering of high counts of COVID-19 cases is significantly increasing overall, was primarily concentrated in the center and northern regions of Quezon City, where the majority of the barangays in Districts 2, 5, and 6 are located. Barangays identified with this pattern were Bagong Silangan, Batasan Hills, Commonwealth, Holy Spirit, Payatas, Matandang Balara, Pasong Putik Proper, Fairview, Pasong Tamo, and Sauyo. As there is a possible resurgence in COVID-19 cases, identifying spatiotemporal trends and clustering patterns is vital for regulating and controlling COVID-19's spread. Thus, the study's findings and methods can be utilized to predict and manage epidemics and help decision-makers control existing and future outbreaks.
Keywords: spatial clustering, spatiotemporal analysis, space-time cube, coronavirus
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Year: 2023 Vol.: 72 No.: 1
Record ID: 157 [ Page 1 of 16, No. 4 ]
Utilization of Machine Learning, Government-Based and Non-Conventional Indicators for Property Value Prediction in the Philippines
Authors: Gabriel Isaac L. Ramolete, Bryan Bramaskara, Dustin A. Reyes, and Adrienne Heinrich
Abstract:
Property appraisal and value estimation in the Philippines are prone to human errors and bias, due to price subjectivity and the general difficulty in properly quantifying the impact of factors beyond the property itself. Predictive models for property valuation typically involve conventional features of the house (e.g., number of bathrooms) and market prices of nearby properties. This paper investigates the value of incorporating alternative data to account for deviations in true market value and improve property value predictions in the Philippines and other developing countries. The study considers public data and anchors socio-economic indicators to assess its relevance to property value prediction in the Philippines. By utilizing the Department of Trade and Industry’s 2021 National Competitiveness Index Rating, this research also investigates the significance of a Local Government Unit’s competitiveness based on their economic dynamism, government efficiency, infrastructure, and resiliency. Different commonly used Machine Learning (ML) methods and features from various data sources are compared and it is found that the inclusion of government indicators has substantial positive effect on the model performance on top of conventional indicators that can be globally replicated. A Mean Average Percentage Error (MAPE) of 10.7-21% is obtained which is competitive compared to the performance ranges of other reported models. A property segment (personalized) approach is proposed to achieve lower error rates in Philippine appraisal (in 87.5% of cases), better access and transparency for populations outside the real estate network, and minimally biased assessments, all of which are also relevant for other developing countries.
Keywords: property appraisal, spatial analysis, city competitiveness, clustering
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Year: 2023 Vol.: 72 No.: 1
Record ID: 156 [ Page 1 of 16, No. 5 ]
Verification of Coffee Product Form and Determination of Conversion Rate From Coffee Dried Berries to Green Coffee Beans (GCB)
Authors: Dennis S. Mapa, Ph.D., Divina Gracia L. Del Prado, Ph.D., Vivian R. Ilarina, Rachel C. Lacsa, Manuela S. Nalugon, Abella A. Regala, Marivic C. De Luna, and Ray Francis B. De Castro
Abstract:
The Philippine Statistics Authority (PSA) collects, generates, and releases production data on coffee in the form of dried berries from the results of the Crops Production Survey. In the computation of the Supply Utilization Accounts and Food Balance Sheet for coffee, the coffee form used is green coffee beans (GCB), using a conversion rate of 28 percent from dried berries to GCB. However, the Food and Agriculture Organization of the United Nations (FAO) and International Coffee Organization (ICO) use a 50 percent conversion rate from dried berries to GCB. The common form of coffee traded by farmers and the conversion rate from dried berries to GCB were investigated through consultations with traders, processors, and other stakeholders; and surveys with coffee farmers and traders as respondents. The results of this study show that the common form of coffee traded by farmers is GCB, and the average conversion rate from dried berries to GCB is 50 percent.
Keywords: fresh berries, field visits, survey, processors, experiment, percent recovery
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Year: 2022 Vol.: 71 No.: 2
Record ID: 155 [ Page 1 of 16, No. 6 ]
Determination of Dry Rubber Content of Rubber Cup Lump
Authors: Claire Nova O. Abdulatip and Honey Fe G. Boje
Abstract:
The quality of latex from rubber trees is determined by the amount of Dry Rubber Content (DRC). Mainly, the price of the cup lumps is directly dependent on the DRC, commonly determined through visual observation by rubber dealers. Thus, no standard method is used by rubber buyers in the industry for farm gate determination of cup lump DRC. But the Philippines used a 25% conversion rate from cup lumps to dry rubber; however, other member countries used 50%. Therefore, this paper addresses the conversion rate of dry rubber content of rubber cup lumps in the Philippines. “On-site validation” was conducted by collecting data from selected rubber processing plants in major rubber producing provinces, namely: Zamboanga Sibugay, North Cotabato, and Bukidnon. Secondary data, such as cup lumps and crumb rubber volume from 2017 to 2019, were collected and analyzed using Percentage Ratio Comparison. Results indicated that the DRC of cup lumps to dry rubber was more than 50%.
Keywords: Rubber, Conversion Rate, On-Site Validation
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Year: 2022 Vol.: 71 No.: 2
Record ID: 154 [ Page 1 of 16, No. 7 ]
Topic Identification and Classification of GooglePlay Store Reviews
Authors: Daniel David M. Pamplona
Abstract:
Digital distribution platforms, such as Google®3 Play Store, contain an enormous quantity of information related to app data and user reviews. A particularly challenging task is to classify a large unstructured dataset into smaller clusters or topics. With this, data from 19,886 user reviews was extracted from Google Play Store. The main task is to determine app characteristics, though common themes, that are commonly mentioned in positive and negative reviews. Text data was preprocessed and then common topics were identified using LDA for positive reviews and negative reviews. The accuracy of topics was assessed using perplexity-based approach and human interpretation. To further validate the topic model, the topic assignment was used as the outcome variable in Naive Bayes model with reviews as input. Empirical results show that the extracted topics can be predicted well using text reviews. Finally, the distribution of topics was calculated according to different app categories.
Keywords: Topic Modeling, Latent Dirichlet Allocation, Naive Bayes Classifier, Perplexity
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Year: 2022 Vol.: 71 No.: 2
Record ID: 153 [ Page 1 of 16, No. 8 ]
A Bayesian Hierarchical Model for COVID-19 Cases in Mindanao Philippines
Authors: Jejemae D. Nacion and Bernadette F. Tubo
Abstract:
A Bayesian hierarchical modelling approach is utilized to nowcast COVID-19 cases in Mindanao, Philippines for the year 2020 to 2021. A spatio-temporal model is considered and the proposed methodology explores the possibility of a flexible way of correcting the time and space delayed reports of the COVID-19 cases for a duration of 4 weeks for the 27 provinces in Mindanao via a Bayesian approach. The goal of the modelling approach is to include parameters that will correct reporting delays in the dataset and derive a model using the Integrated Nested Laplace Approximation (INLA). The study shows that the proposed model was able to capture the increasing trend of the COVID-19 disease counts, that is, the prediction counts derived are closer to the true count compared to the currently reported counts of COVID-19 cases which showed a decreasing behavior. The ability of the proposed model to nowcast statistically significant estimates, particularly, for epidemic counts of COVD-19 in the presence of report delays may aid health authorities to have effective control measures and issuance of warnings to the public.
Keywords: Bayesian inference, spatio-temporal model, reporting delay, nowcasting
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Year: 2022 Vol.: 71 No.: 2
Record ID: 152 [ Page 1 of 16, No. 9 ]
Hierarchical Bayesian Model for Correcting Reporting Delays in Dengue Counts
Authors: Mikee T. Demecillo and Bernadette F. Tubo
Abstract:
Real-time surveillance and precise case estimation are necessary for situational awareness in order to spot trends and outbreaks and establish efficient control actions. The comprehension of the mechanisms of a sudden rise or fall in disease cases that change over time is hampered by the reporting delays between disease start and case reporting. This study uses a flexible temporal nowcasting model with a Bayesian inference for latent Gaussian models built in R-INLA to rectify reporting delays for weekly dengue surveillance data in Northern Mindanao from 2009 to 2010. Additionally, it seeks to quantify all the uncertainties involved in replacing the missing value. The statistical issue is to forecast run-off triangle numbers based on actual counts ????????!,#. In contrast to the currently reported instances, which seem to be declining, the posterior predictive model on thegiven temporal dataset recognizes the fact that there are more dengue cases than there were previously (supporting the actual scenario). This implies that even with delayed data, the model was still able to provide a reliable estimate of the true number of instances. This paper offers a model for nowcasting to aid in dengue control and good judgment on the part of interested authorities.
Keywords: Latent Gaussian Model, Nowcast, Count Data
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Year: 2022 Vol.: 71 No.: 2
Record ID: 151 [ Page 1 of 16, No. 10 ]
Estimating the Magnitude of the Poor Households in Metro Manila Using the Poisson Regression Model
Authors: Bernadette B. Balamban, Anna Jean C. Pascasio, Driesch Lucien R. Cortel and Maxine R. Ridulme
Abstract:
In the Official Poverty Statistics, Metro Manila, also known as the National Capital Region (NCR), is one of the areas that belong to the least poor cluster – a cluster that has relatively low poverty incidences. The Philippine Statistics Authority released the 2018 Municipal and City Level Poverty Estimates using the Elbers, Lajouw, and Lanjouw (ELL) methodology. The 2018 Small Area Estimates (SAE) of Poverty also released estimates for the 14 sub-areas in Metro Manila, ranging from 1.5 percent to 6.5 percent. The city-level poverty estimates were released as official statistics using direct estimation technique. Given the relatively low poverty incidences of the region, this paper aims to estimate the 2018 poverty incidence for the legislative districts in NCR, including the 14 sub-areas of the City of Manila, using the Poisson regression methodology and to compare with the results of the ELL methodology. Data sources include the 2015 Census of Population, and the merged 2018 Family Income and Expenditure Survey and January 2019 round of the Labor Force Survey. A total of 5 significant indicators were included in the final model. Results show that the Poisson model produced more reliable estimates for NCR than the ELL methodology. These SAE techniques allow for generating more granular poverty statistics useful for targeting poor beneficiaries. Furthermore, information may provide an opportunity for the LGU to act swiftly and provide appropriate subsidies for areas within Metro Manila.
Keywords: poverty statistics, small area estimation, survey and census data, time invariance
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Year: 2022 Vol.: 71 No.: 2