ERN: Simulation Methods (Topic)最新文献_第2页

Fast and Efficient Nested Simulation for Large Variable Annuity Portfolios: A Surrogate Modeling Approach 大型可变年金投资组合的快速高效嵌套模拟:代理模型方法

ERN: Simulation Methods (Topic) Pub Date : 2019-02-26 DOI: 10.2139/ssrn.3342701

X. Lin, Shuai Yang

引用次数: 20

A Backward Simulation Method for Stochastic Optimal Control Problems 随机最优控制问题的逆向模拟方法

ERN: Simulation Methods (Topic) Pub Date : 2019-01-20 DOI: 10.2139/ssrn.3319160

Zhiyi Shen, Chengguo Weng

引用次数: 6

Monte Carlo Calibration Method of Stochastic Volatility Model with Stochastic Interest Rate 随机利率下随机波动率模型的蒙特卡罗校正方法

ERN: Simulation Methods (Topic) Pub Date : 2019-01-02 DOI: 10.2139/ssrn.3406240

Mingyang Xu

{"title":"Monte Carlo Calibration Method of Stochastic Volatility Model with Stochastic Interest Rate","authors":"Mingyang Xu","doi":"10.2139/ssrn.3406240","DOIUrl":"https://doi.org/10.2139/ssrn.3406240","url":null,"abstract":"Implied volatility skew and smile are ubiquitous phenomena in the financial derivative market especially after the Black Monday 1987 crash. Various stochastic volatility models have been proposed to capture volatility skew and smile in derivative pricing and hedging. Almost 30 years after the advent of the first type of stochastic volatility model calibrating them to the market volatility surface still remains challenging, especially when stochastic interest rate has to be also taken into account for long-dated options. Many techniques have been applied to tackle this problem, including Fast Fourier Transform, singular perturbation expansion, heat kernel expansion, Markovian projection, to name a few. Although they have achieved some success in deriving either a close-form solution for a specific type of model or asymptotic solution in more general, none of them can really solve the calibration problem satisfying our need in term of both efficiency and accuracy. Monte Carlo method is a flexible numerical pricing method but has not been considering for calibration because of its slow convergence. However, with the great advance in computational power, in particular, parallel computation and the invention of other variance reduction techniques fast and accurate calibration using Monte Carlo becomes possible. This paper presents a Monte Carlo calibration method for stochastic volatility models with stochastic interest rate, which reduces simulation dimension by conditional expectation and further improves speed by vectorization. Numerical experiments show that both the calibration speed and accuracy of this generic method are satisfactory for almost all applications.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122968743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Crude Estimation of XVA Sensitivities XVA灵敏度的粗略估计

ERN: Simulation Methods (Topic) Pub Date : 2018-12-29 DOI: 10.2139/ssrn.3314837

S. Alavian

引用次数: 0

Investment-Cash Flow Sensitivities Are Very Probably Not Valid Measures of Financing Constraints: on the Accounting Partial Identities Problem 投资-现金流量敏感性很可能不是融资约束的有效度量:关于会计偏恒等式问题

ERN: Simulation Methods (Topic) Pub Date : 2018-12-01 DOI: 10.2139/ssrn.3341335

Javier Sánchez Vidal

引用次数: 0

Eignung von Varianz-Kovarianz-Ansätzen und Copula-Modellen zur Risikoaggregation in bankaufsichtlichen Risikotragfähigkeitskonzepten (How Adequate Are Covariance and Copula Based Approaches of Risk Aggregation?) 银行对风险分担方法和基于风险计算的不能合并方案

ERN: Simulation Methods (Topic) Pub Date : 2018-12-01 DOI: 10.2139/ssrn.3454418

M. Graalmann, F. Lehrbass

引用次数: 4

Innovation or Shock Size for Stata’s PVAR Stata PVAR的创新或冲击大小

ERN: Simulation Methods (Topic) Pub Date : 2018-11-20 DOI: 10.2139/ssrn.3288004

George S. Ford

引用次数: 0

A Second Order Weak Approximation of SDEs Using Markov Chain Without Levy Area Simulation 无列维面积模拟的马尔可夫链二阶弱逼近

ERN: Simulation Methods (Topic) Pub Date : 2018-09-28 DOI: 10.2139/ssrn.3257365

T. Yamada, Kenta Yamamoto

引用次数: 0

Estimating Average Treatment Effects With Propensity Scores Estimated With Four Machine Learning Procedures: Simulation Results in High Dimensional Settings and With Time to Event Outcomes 用四种机器学习程序估计的倾向分数估计平均治疗效果:高维设置和事件结果时间的模拟结果

ERN: Simulation Methods (Topic) Pub Date : 2018-09-21 DOI: 10.2139/ssrn.3272396

Kip Brown, P. Merrigan, Jimmy Royer

{"title":"Estimating Average Treatment Effects With Propensity Scores Estimated With Four Machine Learning Procedures: Simulation Results in High Dimensional Settings and With Time to Event Outcomes","authors":"Kip Brown, P. Merrigan, Jimmy Royer","doi":"10.2139/ssrn.3272396","DOIUrl":"https://doi.org/10.2139/ssrn.3272396","url":null,"abstract":"Background: The increased availability of claims data allows one to build high dimensional datasets, rich in covariates, for accurately estimating treatment effects in medical and epidemiological cohort studies. This paper shows the full potential of machine learning for the estimation of average treatment effects with propensity score methods in a context rich and high dimensional datasets. \u0000Methods: Four different methods are used to estimate average treatment effects in the context of time to event outcomes. The four methods explored in this study are LASSO, Random Forest, Gradient Descent Boosting and Artificial Neural networks. Simulations based on an actual medical claims data set are used to assess the efficiency of these methods. The simulations are performed with over 100, 000 observations and 1,100 explanatory variables. Each method is tested on 500 datasets that are created from the original dataset, allowing us to report the mean and standard deviation of estimated average treatment effects. \u0000Results: The results are very promising for all four methods; however, LASSO, Random Forest and Gradient Boosting seem to be performing better than Random Forest. \u0000Conclusion: Machine Learning methods can be helpful for observational studies that use the propensity score when a very large number of covariates are available, the total number of observations is large, and the dependent event rare. This is an important result given the availability of big data related to Health Economics and Outcomes Research (HEOR) around the world.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126109681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Optimal Unbiased Estimation for Expected Cumulative Cost 期望累积成本的最优无偏估计

ERN: Simulation Methods (Topic) Pub Date : 2018-04-11 DOI: 10.2139/ssrn.3161030

Zhenyu Cui, M. Fu, Yijie Peng, Lingjiong Zhu

引用次数: 1