• Title/Summary/Keyword: Conditional distribution

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Estimation of BDI Volatility: Leverage GARCH Models (BDI의 변동성 추정: 레버리지 GARCH 모형을 중심으로)

  • Mo, Soo-Won;Lee, Kwang-Bae
    • Journal of Korea Port Economic Association
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    • v.30 no.3
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    • pp.1-14
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    • 2014
  • This paper aims at measuring how new information is incorporated into volatility estimates. Various GARCH models are compared and estimated with daily BDI(Baltic Dry Index) data. While most researchers agree that volatility is predictable, they differ on how this volatility predictability should be modelled. This study, hence, introduces the asymmetric or leverage volatility models, in which good news and bad news have different predictability for future. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. From the Ljung-Box test statistic for twelfth-order serial correlation for the level we do not find any significant serial correlation in the unpredictable BDI. The coefficients of skewness and kurtosis both indicate that the unpredictable BDI has a distribution which is skewed to the left and significantly flat tailed. Furthermore, the Ljung-Box test statistic for twelfth-order serial correlations in the squares strongly suggests the presence of time-varying volatility. The sign bias test, the negative size bias test, and the positive size bias test strongly indicate that large positive(negative) BDI shocks cause more volatility than small ones. This paper, also, shows that three leverage models have problems in capturing the correct impact of news on volatility and that negative shocks do not cause higher volatility than positive shocks. Specifically, the GARCH model successfully reveals the shape of the news impact curve and is a useful approach to modeling conditional heteroscedasticity of daily BDI.

Analysis of Survivability for Combatants during Offensive Operations at the Tactical Level (전술제대 공격작전간 전투원 생존성에 관한 연구)

  • Kim, Jaeoh;Cho, HyungJun;Kim, GakGyu
    • The Korean Journal of Applied Statistics
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    • v.28 no.5
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    • pp.921-932
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    • 2015
  • This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

The Impact of the Internet Channel Introduction Depending on the Ownership of the Internet Channel (도입주체에 따른 인터넷경로의 도입효과)

  • Yoo, Weon-Sang
    • Journal of Global Scholars of Marketing Science
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    • v.19 no.1
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    • pp.37-46
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    • 2009
  • The Census Bureau of the Department of Commerce announced in May 2008 that U.S. retail e-commerce sales for 2006 reached $ 107 billion, up from $ 87 billion in 2005 - an increase of 22 percent. From 2001 to 2006, retail e-sales increased at an average annual growth rate of 25.4 percent. The explosive growth of E-Commerce has caused profound changes in marketing channel relationships and structures in many industries. Despite the great potential implications for both academicians and practitioners, there still exists a great deal of uncertainty about the impact of the Internet channel introduction on distribution channel management. The purpose of this study is to investigate how the ownership of the new Internet channel affects the existing channel members and consumers. To explore the above research questions, this study conducts well-controlled mathematical experiments to isolate the impact of the Internet channel by comparing before and after the Internet channel entry. The model consists of a monopolist manufacturer selling its product through a channel system including one independent physical store before the entry of an Internet store. The addition of the Internet store to this channel system results in a mixed channel comprised of two different types of channels. The new Internet store can be launched by the independent physical store such as Bestbuy. In this case, the physical retailer coordinates the two types of stores to maximize the joint profits from the two stores. The Internet store also can be introduced by an independent Internet retailer such as Amazon. In this case, a retail level competition occurs between the two types of stores. Although the manufacturer sells only one product, consumers view each product-outlet pair as a unique offering. Thus, the introduction of the Internet channel provides two product offerings for consumers. The channel structures analyzed in this study are illustrated in Fig.1. It is assumed that the manufacturer plays as a Stackelberg leader maximizing its own profits with the foresight of the independent retailer's optimal responses as typically assumed in previous analytical channel studies. As a Stackelberg follower, the independent physical retailer or independent Internet retailer maximizes its own profits, conditional on the manufacturer's wholesale price. The price competition between two the independent retailers is assumed to be a Bertrand Nash game. For simplicity, the marginal cost is set at zero, as typically assumed in this type of study. In order to explore the research questions above, this study develops a game theoretic model that possesses the following three key characteristics. First, the model explicitly captures the fact that an Internet channel and a physical store exist in two independent dimensions (one in physical space and the other in cyber space). This enables this model to demonstrate that the effect of adding an Internet store is different from that of adding another physical store. Second, the model reflects the fact that consumers are heterogeneous in their preferences for using a physical store and for using an Internet channel. Third, the model captures the vertical strategic interactions between an upstream manufacturer and a downstream retailer, making it possible to analyze the channel structure issues discussed in this paper. Although numerous previous models capture this vertical dimension of marketing channels, none simultaneously incorporates the three characteristics reflected in this model. The analysis results are summarized in Table 1. When the new Internet channel is introduced by the existing physical retailer and the retailer coordinates both types of stores to maximize the joint profits from the both stores, retail prices increase due to a combination of the coordination of the retail prices and the wider market coverage. The quantity sold does not significantly increase despite the wider market coverage, because the excessively high retail prices alleviate the market coverage effect to a degree. Interestingly, the coordinated total retail profits are lower than the combined retail profits of two competing independent retailers. This implies that when a physical retailer opens an Internet channel, the retailers could be better off managing the two channels separately rather than coordinating them, unless they have the foresight of the manufacturer's pricing behavior. It is also found that the introduction of an Internet channel affects the power balance of the channel. The retail competition is strong when an independent Internet store joins a channel with an independent physical retailer. This implies that each retailer in this structure has weak channel power. Due to intense retail competition, the manufacturer uses its channel power to increase its wholesale price to extract more profits from the total channel profit. However, the retailers cannot increase retail prices accordingly because of the intense retail level competition, leading to lower channel power. In this case, consumer welfare increases due to the wider market coverage and lower retail prices caused by the retail competition. The model employed for this study is not designed to capture all the characteristics of the Internet channel. The theoretical model in this study can also be applied for any stores that are not geographically constrained such as TV home shopping or catalog sales via mail. The reasons the model in this study is names as "Internet" are as follows: first, the most representative example of the stores that are not geographically constrained is the Internet. Second, catalog sales usually determine the target markets using the pre-specified mailing lists. In this aspect, the model used in this study is closer to the Internet than catalog sales. However, it would be a desirable future research direction to mathematically and theoretically distinguish the core differences among the stores that are not geographically constrained. The model is simplified by a set of assumptions to obtain mathematical traceability. First, this study assumes the price is the only strategic tool for competition. In the real world, however, various marketing variables can be used for competition. Therefore, a more realistic model can be designed if a model incorporates other various marketing variables such as service levels or operation costs. Second, this study assumes the market with one monopoly manufacturer. Therefore, the results from this study should be carefully interpreted considering this limitation. Future research could extend this limitation by introducing manufacturer level competition. Finally, some of the results are drawn from the assumption that the monopoly manufacturer is the Stackelberg leader. Although this is a standard assumption among game theoretic studies of this kind, we could gain deeper understanding and generalize our findings beyond this assumption if the model is analyzed by different game rules.

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A Study on the Determinants of Land Price in a New Town (신도시 택지개발사업지역에서 토지가격 결정요인에 관한 연구)

  • Jeong, Tae Yun
    • Korea Real Estate Review
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    • v.28 no.1
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    • pp.79-90
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    • 2018
  • The purpose of this study was to estimate the pricing factors of residential lands in new cities by estimating the pricing model of residential lands. For this purpose, hedonic equations for each quantile of the conditional distribution of land prices were estimated using quantile regression methods and the sale price date of Jangyu New Town in Gimhae. In this study, a quantile regression method that models the relation between a set of explanatory variables and each quantile of land price was adopted. As a result, the differences in the effects of the characteristics by price quantile were confirmed. The number of years that elapsed after the completion of land construction is the quadratic effect in the model because its impact may give rise to a non-linear price pattern. Age appears to decrease the price until certain years after the construction, and increases the price afterward. In the estimation of the quantile regression, land age appears to have a statistically significant impact on land price at the traditional level, and the turning point appears to be shorter for the low quantiles than for the higher quantiles. The positive effects of the use of land for commercial and residential purposes were found to be the biggest. Land demand is preferred if there are more than two roads on the ground. In this case, the amount of sunshine will improve. It appears that the shape of a square wave is preferred to a free-looking land. This is because the square land is favorable for development. The variables of the land used for commercial and residential purposes have a greater impact on low-priced residential lands. This is because such lands tend to be mostly used for rental housing and have different characteristics from residential houses. Residential land prices have different characteristics depending on the price level, and it is necessary to consider this in the evaluation of the collateral value and the drafting of real estate policy.

Estimation of GARCH Models and Performance Analysis of Volatility Trading System using Support Vector Regression (Support Vector Regression을 이용한 GARCH 모형의 추정과 투자전략의 성과분석)

  • Kim, Sun Woong;Choi, Heung Sik
    • 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.