政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124734

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124734

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题名:	深度校準：以G2++ 利率模型為例 Calibrating G2++ Interest Rate Model : an Artificial Neural Network approach
作者:	楊東翰 Yang, Tung-Han
贡献者:	廖四郎 Liao, Szu-Lang 楊東翰 Yang, Tung-Han
关键词:	校準最佳化利率模型類神經網路深度學習 Calibration Optimization Interest rate model Artificial Neural Network Deep learning
日期:	2019
上传时间:	2019-08-07 16:11:38 (UTC+8)
摘要:	校準一直是金融工程領域中的重要課題。評估定價模型實務上的可行性，主要端看模型是否具有校準市場資訊的能力。傳統上，執行校準需要反覆進行商品定價，而定價上牽涉的資產動態過程離散化與模擬，可能的龐大計算量將導致校準非常耗時且無效率。本研究提出的「深度校準」乃是基於深度學習框架下的校準方法，旨在解決校準實務上速度緩慢的問題。本研究以G2++ 模型為例，利用類神經網路模型「深度學習」利率模型的定價過程，將傳統校準所牽涉的繁複計算囊括在類神經網路模型的訓練階段，待模型訓練完畢後，即可無需反覆進行耗時的定價過程，從而提升校準流程的效率。為了讓深度校準實務上的應用更一般化，本研究建構的類神經網路模型，可同時校準市場上價平利率交換選擇權與價平利率上限隱含波動度報價。實證結果顯示，深度校準可將原先所需的十分鐘降至一秒以內，且無論是校準誤差或定價誤差上，其結果與傳統校準相近。再者，深度校準的穩健性相當高，無論是面對不同校準商品、不同貨幣市場乃至不同最佳化演算法，深度校準皆能維持既有的成效。本研究另檢驗了實務上看重的模型再訓練週期，推論本研究的類神經網路每兩個禮拜需要重新訓練一次。最後，本研究總結了深度校準的實務上的優點，對於深度校準「快速又不失精確度」的優良特性，本研究亦給予合理推論。 Calibration is an important topic in the field of financial engineering. The implementation of pricing models requires the calibration of model parameters to observed market data. Traditionally, model calibration routines involve repetitive pricing of financial instruments, making calibration of many interest rate models expensive and inefficient, since the dynamics of the underlying asset can be approximated by costly discretization for the simulation. We present a deep-learning-based calibration method called “deep calibration” to resolve the slow calibration issue that practitioners face in practice. In this work we propose a procedure for deep calibration of G2++ model. We evaluate the efficiency of standard calibration procedure by training an artificial neural network to “deeply learn” the complex pricing function, off-loading the bulk of calculations to a training phase. To provide a general implementation of deep calibration, we present a specific architecture that is built to calibrate at-the-money swaption volatilities and at-the-money cap volatilities simultaneously. Experiments show that deep calibration procedure performs the calibration task in a fraction of a second, compared with 10 minutes taken by standard calibration procedure. Moreover, deep calibration procedure performs as well as standard calibration in both calibration error and pricing error. In addition, we examine the robustness by presenting deep calibration with respect to different financial instruments, pricing currencies and optimizers and confirm the sustained high performance of our approach. We also examine the cycle of model retraining. Based on our findings, we conclude that the trained neural network should be retrained 2 weeks. Finally, we investigate the advantages and the reason for the “high accuracy and speed” characteristic provided by deep calibration.
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描述:	碩士國立政治大學金融學系 106352030
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106352030
数据类型:	thesis
DOI:	10.6814/NCCU201900253
显示于类别:	[金融學系] 學位論文

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