No description available.

The existence of pathwise stationary solutions of this stochastic partial differential equation (SPDE, for abbreviation) is demonstrated. In Part II, a connection between certain kind of state constrained controlled Forward-Backward Stochastic Differential Equations (FBSDEs) and Hamilton-Jacobi-Bellman equations (HJB equations) are demonstrated. The special case provides a probabilistic representation of some geometric flows, including the mean curvature flows. Part II includes also a probabilistic proof of the finite time existence of the mean curvature flows.

本文採用Lognormal Forward LIBOR Model (LFM) 利率模型，針對可贖回雪球式債券進行相關的評價與避險分析，而由於此商品的計息方式為路徑相依型態，價格沒有封閉解，故必須利用數值方法來進行評價。過去通常使用二元樹或三元樹的方法來評價具有可贖回特性的商品，但因為LFM是屬於多因子模型，所以不容易處理建樹的過程。而一般路徑相依商品的評價是使用蒙地卡羅法來進行，但是標準的蒙地卡羅法不易處理美式或百慕達式選擇權的問題，因此，本研究將使用由Longstaff and Schwartz(2001)所提出的最小平方蒙地卡羅法，來處理同時具有可贖回與路徑相依特性的商品評價並進行實證研究。 / 此外，關於可贖回商品的避險參數部分，由於商品的價格函數不具有連續性，若在蒙地卡羅法之下直接使用重新模擬的方式來求算避險參數，將會造成不準確的結果，而Piterbarg (2004)提出了兩種可用來計算在LFM下可贖回商品避險參數的方法，其實証結果發現所求出的避險參數結果較準確，因此本研究將此方法運用至可贖回雪球式利率連動債券，並分析各種參數變化對商品價格的影響大小，便於進行避險工作。

No description available.

In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is commonly done using Monte Carlo simulations. However, obtaining accurate estimates of the Greeks can be computationally costly. Multilevel Monte Carlo offers complexity improvements over standard Monte Carlo techniques. However the idea has never been used for the computation of Greeks. In this work we answer the following questions: can multilevel Monte Carlo be useful in this setting? If so, how can we construct efficient estimators? Finally, what computational savings can we expect from these new estimators? We develop multilevel Monte Carlo estimators for the Greeks of a range of options: European options with Lipschitz payoffs (e.g. call options), European options with discontinuous payoffs (e.g. digital options), Asian options, barrier options and lookback options. Special care is taken to construct efficient estimators for non-smooth and exotic payoffs. We obtain numerical results that demonstrate the computational benefits of our algorithms. We discuss the issues of convergence of pathwise sensitivities estimators. We show rigorously that the differentiation of common discretisation schemes for Ito processes does result in satisfactory estimators of the the exact solutions’ sensitivities. We also prove that pathwise sensitivities estimators can be used under some regularity conditions to compute the Greeks of options whose underlying asset’s price is modelled as an Ito process. We present several important results on the moments of the solutions of stochastic differential equations and their discretisations as well as the principles of the so-called “extreme path analysis”. We use these to develop a rigorous analysis of the complexity of the multilevel Monte Carlo Greeks estimators constructed earlier. The resulting complexity bounds appear to be sharp and prove that our multilevel algorithms are more efficient than those derived from standard Monte Carlo.