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    Strategic Asset Allocation with Generalized Homothetic Robust Epstein-Zin Utility under a Quadratic Model

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    This study proposes “generalized homothetic robust Epstein-Zin (GHREZ) utility,” in which the coefficient of relative ambiguity aversion in homothetic robust Epstein-Zin (HREZ) utility is generalized to a positive-definite matrix of relative ambiguity aversions. We analyze a robust consumption– investment problem with GHREZ utility under a quadratic security market model. We demonstrate that the volatility of the optimal robust portfolio can be expressed as a weighted average of the market price of risk and the “investor hedging value of intertemporal uncertainty.” We derive an approximate optimal robust portfolio. We introduce the concept of “mean relative ambiguity aversion,” and compare the relationship between the approximate optimal robust portfolios and relative ambiguity aversion matrices with an identical mean relative ambiguity aversion. Numerical analysis reveals that, as deviations from the case of HREZ utility increase, deviations from the approximate optimal asset allocation under HREZ utility also increase, and the sensitivity is quite large. This finding suggests that GHREZ utility is promising for solving the equity home bias puzzle.technical repor

    小学生が推論する力を高めるファシリテートのあり方―対話型鑑賞をヒントにして―【教育学研究科高度教職実践専攻(教職大学院) 課題解決研究要旨】

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    滋賀大学大学院教育学研究科高度教職実践専攻教育実践力開発コース 令和6年度修了 課題解決研究要旨research repor

    子どもの自由な表現を引き出す音楽づくり―幼児期の音との出会いから考える―【教育学研究科高度教職実践専攻(教職大学院) 課題解決研究要旨】

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    滋賀大学大学院教育学研究科高度教職実践専攻授業実践力開発コース 令和6年度修了 課題解決研究要旨research repor

    小学校における社会情動的スキルを高めるための学級経営および授業づくりに関する探究【教育学研究科高度教職実践専攻(教職大学院) 課題解決研究要旨】

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    滋賀大学大学院教育学研究科高度教職実践専攻授業実践力開発コース 令和6年度修了 課題解決研究要旨research repor

    特別支援学校において再生栽培を理科教材とする実践―生命の連続性の主体的で深い学びを目指して―【教育学研究科高度教職実践専攻(教職大学院) 課題解決研究要旨】

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    滋賀大学大学院教育学研究科高度教職実践専攻ダイバーシティ教育力開発コース 令和6年度修了 課題解決研究要旨research repor

    Evaluating the Effects of Green Infrastructure in New York City : A Causal Inference Using NYC 311 Data

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    Automatic Calibration of an in-Vehicle Camera based on Structure from Motion

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    In this article, we propose a novel method to automatically estimates camera posture parameters of an in-vehicle camera unit. Several methods have already been proposed which estimate those parameters mainly relying on known texture patterns on the ground (e.g. like road signs, lane markers). Unlike conventional methods, our method achieves camera calibration without given texture patterns, by using the camera trajectory estimated by Structure from Motion (SfM) as a clue. As another contribution, we have evaluated the effectiveness of multiple techniques that are empirically known to improve the robustness and accuracy of SfM but have not been well discussed in the literature. In an experiment, we show that the pose parameters can automatically be estimated in the real driving environments, and the results from the proposed and compared methods are quantitatively evaluated.This work was supported by JSPS KAKENHI Grant Number JP21K11961.journal articl

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