• Environmental and Resource Economics Review
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• v.14 no.3
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• pp.595-626
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• 2005

The purpose of this paper is to estimate technical efficiency, scale efficiency, and environmental efficiency by income level of Korean farms, and analyze the factors to decide three efficiencies. Depending on the non-parametric methods, we estimate technical using inputs and outputs of total farms without assuming of goods or behavior of optimization. The average technical efficiency of total firms under constant return to scale and strong disposability is 0.437. The technical inefficiency was caused by 47.7% in pure technical inefficiency, 11.3% in scale failure, and 3.2% in environmental inefficiency. The number of firms under increasing return to scale occupied almost 70% and 27% of total firms respectively. Higher are income class, middle debt & long debt per asset, and N effluents per cultural land, higher technical efficiency. The increases of BOD discharges per cultural land and machines per cultural land deteriorate environmental efficiency.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.909-917
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• 2020

The research aims to analyze the firm-specific and macroeconomic factors that affect insurance company's financial performance. The research explores the variables that influence the financial performance of the United Arab Emirates (UAE)' insurance companies. The analysis for determining financial performance considers the following variables: the firm's age, retention ratio, capital adequacy, underwriting risk/loss ratio, financial-leverage, reinsurance dependency, and macro-economic factors such as GDP per capita, inflation rate considered as independent factors. The return-on-asset (ROA) is the key measuring indicator; it is regarded as the dependent variable for financial performance measures. The research focuses on secondary information obtained from insurance companies' financial statements. The researcher targeted 18 insurance companies listed on the UAE stock exchanges for study purposes. The research examines the overall factors that influence the financial performance of an insurance company. For analysis of data, software package of social sciences (SPSS version 20) is used. The studies used correlation and multiple linear regression analysis to determine financial performance and their effects. The analysis suggests that there are important and constructive relationships between the size, capital adequacy, and reinsurance dependency, while loss ratio, retention ratio, and financial leverage indicate a major negative relationship. And there's no link between GDP per capita and inflation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1249-1256
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• 2021

This study investigates the company value determinant by observing the effect of financial performance and Corporate Social Responsibility (CSR) and its role in moderating performance achievement. The macro-economy variables such as inflation and interest rate are also used as the controlling variable. This research employs the sample of manufacturing companies of the food and beverage sub-sector listed on the Indonesia Stock Exchange. This study used panel data from 2013 to 2017, with the moderating regression analysis. The result shows that the profitability of the current or previous period affects the company's value. CSR and company size affect the company value at the next period shows that stock price, which reflects the investor's perception today, will be affected by the CSR, Size, and Return On Asset of the previous year. CSR also shows that it can be the substitute for profitability since a company that performs CSR is the one that has a good performance. The regression moderating model and the profitability of the previous period have a higher explanatory power than the higher R square value in explaining company value.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.63-73
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• 2022

In today's financial economics literature, the impact of innovative family ownership and management on firm performance is a prominent concern. In this study, the existence of family firms in the listed sector of Pakistan's economy is investigated. The objective of this study is to examine the performance-oriented relationship of family ownership and active involvement of family member at the CEO position. The theoretical perspectives that underpin this research are agency and stewardship. This analysis used a sample of 315 publicly traded companies from 2009 to 2019. The study's primary independent variables include family influence on ownership and family CEO. Financial performance is the dependent variable that is divided into accounting and market measures. The proxy for accounting measure is return on asset and proxy for market measure is Tobin's Q. This study employs univariate and balanced panel data analysis. For robustness of the analysis random-effects GLS regression is carried out. The empirical results show that that Family Firms outperform Non-Family Firms both in terms of accounting and market measures. In the later part family CEOs firms outperform the firms that have either insider or outsider non-family CEOs. This superior performance is subjected to the positive and statistically significant association between family ownership, management, and financial performance.

• Journal of Navigation and Port Research
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• v.43 no.4
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• pp.264-272
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• 2019

In this study, the rate of return on investment used as a proxy variable for the entity's value and financial structure (liability ratio) is related to positive balance. This is consistent with the Static Tradeoff Theory (STT) that the entity's value and financial structure are related to a positive balance because the capital expense of a debt (tax-saving effects) that is less than its equity cost before it is in financial difficulty. Also, operating profitability (EBITDA/Sales), investment safety, total asset growth, net working capital and depreciation expenses are related to negative (-) with financial structure (liability ratio). This is the result of an analysis consistent with the Pecking Order Theory (POT). Fuel costs, borrowing, total asset turnover, financial costs, and tangible asset ratios have a significant positive relationship with the debt ratio. This is consistent with the agency theory and confirms that excessive chartering expenses, such as the bankrupt H company, are the main factors that pressure the financial structure of Korean ocean carriers.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.21-34
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• 2018

The study tests the Fama and French three-factor model by using the newly created Islamic equity style indices. Based on a dataset from May 2006 to April 2011, the three-factor model is tested based on returns of Islamic unit trust funds using the Generalized Method of Moments (GMM) methodology. The sample period is also divided between periods before and after the Global Financial Crisis in August 2008 to test for robustness, and the Bai and Perron (2003) multiple structural break test was used to determine the structural break in the series. The analysis shows that the Fama and French model is valid for Islamic unit trust funds before and after the collapse of Lehman Brothers. The result further indicates the reversal of size effect. As for trading strategies, value funds outperform growth funds by annualized 3.13 percent for the full period. During pre-crisis period, value funds perform better than growth funds while in post-crisis, size factor yields better return than other strategies. As policy suggestion, fund managers need to be aware of the reversal of size effect, and they need to ensure a more transparent stock selection process so that investors can make an informed decision in their asset allocation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.685-695
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• 2021

This study explores the impact of stochastic volatility in option pricing. To be more specific, we compare the option pricing performance between stochastic volatility option pricing model, namely, Heston option pricing model and standard Black-Scholes option pricing. Our finding, based on the market price of SET50 index option between May 2011 and September 2020, demonstrates stochastic volatility of underlying asset return for all level of moneyness. We find that both deep in the money and deep out of the money option exhibit higher volatility comparing with out of the money, at the money, and in the money option. Hence, our finding confirms the existence of volatility smile in Thai option markets. Further, based on calibration technique, the Heston option pricing model generates smaller pricing error for all level of moneyness and time to expiration than standard Black-Scholes option pricing model, though both Heston and Black-Scholes generate large pricing error for deep-in-the-money option and option that is far from expiration. Moreover, Heston option pricing model demonstrates a better pricing accuracy for call option than put option for all level and time to expiration. In sum, our finding supports the outperformance of the Heston option pricing model over standard Black-Scholes option pricing model.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.459-466
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• 2013

Risk exists in all construction projects and resides among the collection of subcontractors and their array of individual activities. Wherever risk resides, the interrelation of participants to one another becomes paramount for the way in which risk is measured. Inherent risk becomes recognizable and quantifiable within network schedules in the form of consuming float - the flexibility to absorb delays. Allocating, owning, valuing, and expending such float in network schedules has been debated since the inception of the critical path method itself. This research investigates the foundational element of a three-part approach that examines how float can be traded as a commodity, a concept whose promise remains unfulfilled for lack of a holistic approach. The Capital Asset Pricing Model (CAPM) of financial portfolio theory, which describes the relationship between risk and expected return of individual stocks, is explored as an analogy to quantify the inherent risk of the participants in construction projects. The inherent relationship between them and their impact on overall schedule performance, defined as schedule risk -the likelihood of failing to meet schedule plans and the effect of such failure, is matched with the use of CAPM's beta component - the risk correlation measure of an individual stock to that of the entire market - to determine parallels with respect to the inner workings and risks represented by each entity or activity within a schedule. This correlation is the initial theoretical extension that is required to identify where risk resides within construction projects, allocate and commoditize it, and achieve actual tradability.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• KDI Journal of Economic Policy
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• v.14 no.2
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• pp.189-203
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• 1992

Any money model should address the most important phenomenon of a monetary economy, which is the phenomenon of the rate of return dominance. Even if the holding returns on financial or nonfinancial assets are higher than the rate of return on fiat money holding, which is typically zero, people still hold and use money. In a period of accelerating inflation, number of dominating assets increases continuously, yet people continue to hold and use money. Wallace's (1980) overlapping generations model cannot address the rate of return dominance phenomenon. His model does not capture the mediun of exchange role of fiat money. In this paper, an overlapping types model of fiat money is constructed, in which different types of consumers have different preferences on different types of goods, are endowed with different types of goods, are located at seperated regions, and live for only two periods. In this model, people hold and use money despite the dominating assets, even if inflation accelates. Money in this case serves as a pure medium of exchange, whereas in Wallace's model, money serves as a pure store of value, and money disappears if a dominating asset exists. An interesting feature of the overlapping types model presented in this paper is that money does not provide a cheap approximation to an idealized and efficient real allocation. A monetary economy is always superior to a nonmonetary economy, because money helps overcome the incompleteness of the overlapping types friction. In a monetary economy, however, a pareto optimal allocation cannot always be achieved, because money cannot always overcome the overlapping types friction itself. Therefore, with the criterion of optimality of real allocations, the monetary economy is more optimal than a nonmonetary economy but less optimal than a complete Arrow-Debreu economy. This feature has important implications on macro modelling. Because of the difficulty in introducing money into a macro model in an essential and endogenous manner as in the overlapping types model of this paper, a macro model typically ignores money and studies real allocations without the money factor. The possible inefficiencies of a monetary economy, relative to a complete real Arrow-Debreu economy, may indicate differences in real allocations between the two models.