This study introduces a novel generative modeling framework for simulating the term structure of interest rates. In recent years, generative models have achieved significant progress in image generation and are increasingly being applied to finance. To the best of our knowledge, this is the first study to apply a generative model-specifically, a diffusion model-to the term structure of interest rates. Furthermore, we extend the framework to incorporate conditional generation mechanisms and v-parameterization. The training dataset consists of spot yield curves constructed from daily overnight index swap (OIS) rates using cubic Hermite splines. As base conditioning variables, we use short-term interest rates and changes in consumer price indexes (CPIs). Empirical analysis covering the period from 2015 to 2025 demonstrates that our model successfully reproduces the level and shape of yield curves corresponding to historical macroeconomic conditions and short-term interest rate environments. Additionally, when incorporating further conditioning variables related to quantitative easing policies, monetary base, current account balances, and nominal gross domestic product (GDP), we find that the inclusion of quantitative easing indicator notably enhances the model's output relative to the base conditioning case. This suggests improved robustness and representational capacity under expanded conditioning.

This paper provides a time series analysis of the Bank of Japan (BOJ)’s exchange traded fund (ETF) and real estate investment trust (REIT) purchase program. The program is a part of the BOJ’s unconventional monetary policy introduced in December 2010 and the timing and quantity of purchases have changed substantially over time. To the best of our knowledge, the current study is the first attempt at investigating the purchase program from a dynamic viewpoint and developing a system that takes expert knowledge as input to analyze the BOJ’s purchase program and outputs a trading strategy. Firstly, to find factors underlying decisions of the BOJ’s purchases, we apply a state space model with explanatory variables obtained by expert knowledge, which is an extension of a logistic regression and enables us to predict their timings with high accuracy throughout all periods. Secondly, we figure out the dynamics of the program’s effect on equity and REIT prices by using a different type of state space model. Finally, we find a profitable trading strategy that incorporates the insights revealed in the above dynamic analysis and that focuses on an effect of the BOJ’s purchase on the spread between the Tokyo Stock Price Index (TOPIX) and Nikkei225 Index (Nikkei225). In addition, we implement event studies, particularly the price dynamics of ETF and REIT just before and after the purchase.

This paper considers a new problem for portfolio optimization with a choice of a probability measure, particularly optimal investment problem under sentiments. Firstly, we formulate the problem as a sup-sup-inf problem consisting of optimal investment and a choice of a probability measure expressing aggressive and conservative attitudes of the investor. This problem also includes the case where the agent has conservative and neutral views on risks represented by Brownian motions and degrees of conservativeness differ among the risk. Secondly, we obtain an expression of the volatility process of a backward stochastic differential equation related to the conservative sentiment in order to investigate cases where the sup-sup-inf problem is solved. Specifically, we take a Malliavin calculus approach to solve the problem and obtain an optimal portfolio process. Finally, we provide an expression of the optimal portfolio under the sentiments in two examples with stochastic uncertainties in an exponential utility case and investigate the impact of the sentiments on the portfolio process.

This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e., (i) a module for preparing candidate investment proposals and (ii) a module for their evaluation to construct a well-performed portfolio.

This paper describes a measurement system that can simultaneously measure the distribution of grasping pressure, grasping posture and wrist position synchronously with grasping action video image data. A sensor designed to measure the distribution of grasping pressure was developed, for use with this system, and when employed, it is slipped onto the hand the same way as an ordinary glove. It has 15 load detection points on each finger and a total of 81 such points for a single hand. A sensor that can detect finger movements at 18 joints was used, for the measurement of the grasping posture and wrist position. This paper discusses the configuration of the system and presents an experimental result.

The authors describe a new sensor data integration architecture that utilizes a physical network paradigm. The authors have realized a prototype system on a parallel processor network using Inmos's transputers. Simple demonstrations of 2-D shape recognition and target tracking are shown and the authors have demonstrated its ability to respond appropriately to varying environmental constraints on the structure of the signal flow. It is shown that one can gain higher reliability in the architecture using the flexibility of the signal flow topology.