In this study, we consider option pricing under a Markov regime-switching GARCH-jump (RS-GARCH-jump) model. More specifically, we derive the risk neutral dynamics and propose a lattice algorithm to price European and ...
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In this study, we consider option pricing under a Markov regime-switching GARCH-jump (RS-GARCH-jump) model. More specifically, we derive the risk neutral dynamics and propose a lattice algorithm to price European and American options in this framework. We also provide a method of parameter estimation in our RS-GARCH-jump setting using historical data on the underlying time series. To measure the pricing performance of the proposed algorithm, we investigate the convergence of the tree-based results to the true option values and show that this algorithm exhibits good convergence. By comparing the pricing results of RS-GARCH-jump model with regime-switching GARCH (RS-GARCH) model, GARCH-jump model, GARCH model, Black-Scholes (BS) model, and Regime-Switching (RS) model, we show that accommodating jump effect and regime switching substantially changes the option prices. The empirical results also show that the RS-GARCH-jump model performs well in explaining option prices and confirm the importance of allowing for both jump components and regime switching.
A new triangularization technique is presented for solving linear prediction problems. The algorithm is based on the exploitation of the special structure that problems of this type exhibit. The reduced triangular sys...
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A new triangularization technique is presented for solving linear prediction problems. The algorithm is based on the exploitation of the special structure that problems of this type exhibit. The reduced triangular system and the error are computed recursively and the problem is solved when the optimal order has been found. The computational complexity of this algorithm is better than existing methods. In addition, good numerical properties are expected of the method.
This paper addresses the stochastic differential utility (SDU) version of the issue raised by Barrieu and El Karoui (Quantitative Finance, 2: 181-188, 2002a) in which optimal risk transfer from a bank to an investor, ...
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This paper addresses the stochastic differential utility (SDU) version of the issue raised by Barrieu and El Karoui (Quantitative Finance, 2: 181-188, 2002a) in which optimal risk transfer from a bank to an investor, realized by transacting well-designed derivatives written on relevant illiquid assets, was mainly studied in two cases with and without an available financial market. From a stochastic maximum principle as described in Yong and Zhou (Stochastic controls: Hamiltonian systems and HJB equations. Springer-Verlag, New York, 1999) we shall derive necessary and sufficient conditions for optimality in several SDU-based maximization problems. It is also shown that the optimal risk transfer, consumptions, investment policies of both agents are characterized by a forward-backward stochastic differential equation (FBSDE) system.
作者：
Nakamura, NobuhiroHitotsubashi University
Graduate School of International Corporate Strategy National Center of Sciences Chiyoda-ku Tokyo 101-8439 2-1-2 Hitotsubashi Japan
In this paper employing two heuristic numerical schemes, we study the asset pricing models with stochastic differential utility (SDU), which is formulated by either of backward stochastic differential equations (BSDEs...
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In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the caus...
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In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the cause of an accident can prevent catastrophic consequences. Traditional methods mostly focus on combining model algorithms and utilize intricate composite model neural network architectures. However, it is crucial to investigate whether greater complexity necessarily leads to better performance. In addition, the consideration of the impact of dataset construction and data preprocessing on model performance is also needed for model building. This paper proposes a framework named DeepLOCA-lattice to experiment with different preprocessing approaches to fundamental deep learning models for a comprehensive analysis of the diagnosis of LOCA breach size. The DeepLOCA-lattice involves data preprocessing via the lattice algorithm and equal-interval partitioning and deep-learning-based models, including the multi-layer perceptron (MLP), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and the transformer model in LOCA breach size diagnosis. After conducting rigorous ablation experiments, we have discovered that even rudimentary foundational models can achieve accuracy rates that exceed 90%. This is a significant improvement when compared to the previous models, which yield an accuracy rate of lower than 50%. The results interestingly demonstrate the superior performance and efficacy of the fundamental deep learning model, with an effective dataset construction approach. It elucidates the presence of a complex interplay among diagnostic scales, sliding window size, and sliding stride. Furthermore, our investigation reveals that the model attains its highest accuracy within the discussed range when utilizing a smaller sliding stride size and a longer sliding window length. This study could furnish valuable insights for constructing models for LO
Recent development of medical information technology uses personal health record (PHR) system, which allows the patients to create, store and share their own health information with doctors, nurses, health insurance p...
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Recent development of medical information technology uses personal health record (PHR) system, which allows the patients to create, store and share their own health information with doctors, nurses, health insurance providers, and family members. However, it has the security and privacy issues. The secret key generation is the major task by performing different algorithms on attribute based encryption (ABE) technique. This method helps to identify the trusted authority using secret key verification on the decryption process. The revocation scheme is performed to reduce the attributes for improving the performance. The drawback of existing method is overcome by the proposed system in which the PHR file is securely accessed by performing enhanced encryption and decryption of ABE technique. Here the matrix based lattice algorithm is proposed with bit plane transformation of decomposition matrices. Attribute reduction and secret key encryption is performed to improve the result of reliability, security, and scalability. The plain text is created to cipher text as key and it holds the encrypted PHR file on the cloud storage, which is retrieved only by the trusted authority. The proposed method is analyzed and compared with the existing work and achieves greater results than most of the recent related literature.
This paper investigates the recursive adaptive algorithms for rapidly detecting various stochastic trends in signals by modeling them as the autoregressive mtegrated(ARI) process. In order to determine the degree of d...
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This paper investigates the recursive adaptive algorithms for rapidly detecting various stochastic trends in signals by modeling them as the autoregressive mtegrated(ARI) process. In order to determine the degree of differencing which represents the changing rate of nonstationary trend components, we derive a new criterion on a basis of the concept of the AIC. The generalized gradient(GG) algorithm and the normalized least squares lattice(NLSL) filter are utilized to identify coefficient parameters in the ARI model in an on-line manner. The effectiveness of the algorithms is examined through numerical simulation using actual data.
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