Previous research on bond investment typically aimed to analyzes discount bonds yet mid-and-long term discount bonds do not exist in the market. Therefore, we have difficulty in applying it to actual investment activity. In this paper, we propose an investment strategy to build a portfolio of interest-bearing bonds, which are tradable. First, we look into the statistical nature of market mispricing which we defined as the difference between the observed price and the theoretical price of Japanese Government Bond. Next, capitalizing on the feature of mean reversion and low correlation among issues that market mispricing has, we discuss how to construct a coupon bond portfolio as opposed to an optimal portfolio of discount bonds regarded as a benchmark and to construct long-short strategy of coupon bonds. As a result of empirical analysis, we found that both an investment into a coupon bond whose duration is closest to that of corresponding discount bond and a diversified investment into coupon bonds with a certain range of duration are effective as passive investing. We also found that an investment into comparatively cheap issues in terms of market mispricing is profitable as active investing. In addition, as an investment type to seek absolute return, we discovered that long/short trading between the issues with a specific duration performs well.
In this paper, we propose a stochastic interest rate model with a Markovian regime-switching property in order to evaluate the interest-rate risk of various kinds of asset and liability portfolios synthetically. In the model, parameters in the short rate process depend on a latent state, and the state transits between some finite regimes with a Markovian property. The Monte Carlo simulation is used to generate many sample paths of the future short rates and evaluate the interest rate risk numerically. Since the term structures of interest rates in future are derived by using the no-arbitrage pricing method based on sample paths, interest rates with various maturities can be used for modeling assets and liabilities. Numerical examples show that the future interest rates will not proceed to decrease deeply to negative values, rather they will behave as if they had a lower limit, and that they will keep their present trends, while there exist some small probabilities under which the extremely upwarding interest-rate scenarios will happen. Additionally, from applying of this model to evaluating the interest rate risk of the non-maturity deposits, we obtain that the present increasing trend of the volume of the deposits will be kept in future, while that the drastic decrease of the volume will happen with small probabilities.
This research focuses on the three objects of using heat energy generated by incineration (maximizing the amount of heat), minimizing total waste weighted distances, and maximizing total population density weighted distances for determining allocations and locations of general waste incineration facilities. For these objectives, we proposed the Multi-objective Optimization with Voronoi diagram and Genetic Algorithm (MOVGA), which has the XY coordinates of the Voronoi seeds and the ID of the facility locations as genes. As for the maximization of the amount of generated heat, we predicted the amount by using the regression equation of multiple regression analysis, and formulated it as the set partitioning problem (SPP) to maximize the prediction value. As for the minimization of the total waste weighted distances, we formulated it as the P-median problem to reduce the environmental load. As for the maximization of the total population density weighted distances, we formulated it as the P-median problem to keep away incineration facilities from densely populated areas. And also, we considered reducing the number of facilities when optimizing. To verify the validity of the MOVGA method, we conducted the case study on Chiba northwest bay area (Ichikawa city, Funabashi city, Narashino city and Urayasu city). As a result of the research, we obtained the approximate solution that can cover 2,788 [households/year] in terms of apartments, with the calorific value increased by 2.275E+11 [kJ/year] (6% increase compared to 2015) under the conditions of 4 facilities (decreased 1 facility compared to the 2015 year). From the research results, we verified that the MOVGA is effective in effectively using thermal energy and reducing the number of facilities in the target area.
In many current railway systems, trains are operated under the fixed timetable. Dynamic planning of train timetable with demands can make passengers more comfortable. There are many researches for demand-driven train timetabling by solving optimization problem and efforts for practical use have begun for small railway networks. However, it is difficult to plan train timetable on-demand for large scale networks in urban area, because it takes too much time to solve train timetabling problem (TTP). In this paper, we propose an algorithm which aims to solve TTP in O(L) time where LM is number of lines. Our method decomposes the original problem into several single line problems by converting a multiple transfer demand to several ones with no transfer. This makes the problem size small and the computation time short. Also, our method updates the solution iteratively in order to prevent declining the solution quality. We evaluate our method by comparing with a local search method. The result shows that our method can compute the solution more quickly and accurately than local search for large scale networks. On the other hand, the result reveals the problem that the computation time of updating the solution is larger than O(L) and becomes a bottleneck for large scale networks. Future research is hence needed to resolve this task to solve TTP for large scale network in short time.
There has been no database suitable for disclosing the influence of dementia on economic activities. Therefore, through using related databases, we have aimed to obtain the principles to resolve its impact. In order to develop the method to detect capability deterioration of economic activities for elderly single-person and two-person households at the age of sixty-five or older, we have employed an anonymous data set obtained from the National Survey of Family Income and Expenditure (NSFIE) carried out by Ministry of Internal Affairs and Communications (MIC) in 1994, 1999 and 2004. Annual income is classified into five groups (High, Mid-H, Mid-L, Low-H and Low-L).
We then propose the detection method mentioned above through selection of features, discriminant analysis, and regression analysis. The ratio of expenditure (after designated total compensation) against annual income is defined as O/I-per. In this method, the higher and lower portions of O/I-per are then selected. Among the expenditure items of the chosen data, the items where expenditure gaps are apt to be outstanding between high/low portions of O/I-per are extracted as feature values and utilized for the analysis. Then for the data whose income is greater than expenditure (after designated total compensation), through the method using the predicted values calculated from the approximate formulas where the feature values above are used as variables, we determine if the data are under-expenditure. Next, for each income group, discriminant analysis is performed by setting O/I-per as variable. Among the data divided into two groups by discriminant analysis, the maximum value among the set of lower O/I-per is set as the threshold to determine under-expenditure. Moreover, approximate formulas are obtained by performing multiple regression analysis, by setting expenditure (after designated total compensation) as respondent variable and the designated feature value as explanatory variable. The prediction values of expenditure (after designated total compensation) is calculated through the formulas obtained. The values are compared with the threshold to determine under-expenditure and used to determine if the data are under-expenditure.
As a result of determination, for single-person household, females show better estimation accuracy than males. In addition, for both single males and females, “the case of not distinguishing rent house with own house” outperforms “the case of distinguishing rent house with own house.” On the other hand, for two-person households, “the case of distinguishing rent house with own house” shows slightly better estimation accuracy than “the case of not distinguishing rent house with own house.”
It is well known that people often behave so that preferable states such as cooperation are realized, even when traditional economics assuming rational agents predicts otherwise. Although many researchers have studied how social preferences can explain such behavior, little is known as to how social preferences influence the outcomes and dynamics of games such as coordination games, where rational agents are expected to realize preferable states. This paper investigates the effects of players’ inequity aversion on the dynamics of coordination games. Replicator dynamics, where fitness is represented by utilities of the players instead of material payoffs, is considered. The dynamics is analyzed analytically and numerically. Inequity aversion is characterized by players’ envy and regret, i.e. the extent to which players dislike disadvantageous and advantageous inequity in payoff, respectively. It was found that the outcome of the game changes when players’ envy is sufficiently large, and that both envy and regret affect the dynamics of the game. Our results show that coordination may not be achieved when players are inequity averse. This paper also demonstrates the importance of the analysis on dynamics of a game, in addition to its equilibria, in studying the influence of social preferences.
This paper constructs a multiple regression model for forecasting the number of workers in the accommodation and food service industry as a model that satisfies four requirements that contribute to EBPM: high forecasting accuracy, rapidity, persuasiveness, and the ability to support policy making. In addition to a normal model that forecasts the number of workers every quarter for the next two quarters, we construct an emergency model that can incorporate the impact of unforeseen events such as the recent declaration of a state of emergency by using impulse response analysis of the VAR model for the prediction of explanatory variables in the regression model. The emergency model predicts that the number of workers will fall to the lowest level since the global financial crisis, indicating that it is an urgent issue to take supportive measures for the industry.
Our primary goal is to examine the effect of grouping multiple search terms on Nash equilibria in a multi-keyword auction. Furthermore, we calculate revenue of a search engine and social welfare of the equilibria in the presence of grouping with those in the absence in order to clarify the effect of grouping.
Grouping was introduced by Dhangwatnotai (2011), who analyzed social welfare in the multi-keyword setting. However, the strategic perspective is still left uninvestigated. Therefore, in our paper, we analyze a strategic aspect of the multi-keyword auction with grouping by applying the concept of Nash equilibria and examine revenue and social welfare. Specifically, our model limits to auction with two advertisers and at most two groups of search terms.
Our results reveal the necessary and sufficient conditions for specific types of Nash equilibria to exist in our model. In these conditions, weighted average value (WAV) of the advertisers plays the key role. WAV is the weighted average of values for search terms by differences in CTR. As for revenue and social welfare, a search engine makes profit, but social welfare decreases by grouping search terms in the equilibria.