{"title":"Monte Carlo Simulation with Machine Learning for Pricing American Options and Convertible Bonds","authors":"Bella Dubrov","doi":"10.2139/ssrn.2684523","DOIUrl":"https://doi.org/10.2139/ssrn.2684523","url":null,"abstract":"Li, Szepesvari and Schuurmans (2009) show that reinforcement learning (RL) algorithms are superior to the classical methods (such as Longstaff and Schwartz (2001)) in pricing American options using Monte Carlo simulation. We extend their techniques to the problem of pricing convertible bonds and show that RL outperforms LS on this task. Additionally, we propose a new method, based on the random forest algorithm from machine learning [Breiman (2001)], that can be used for pricing both American options and convertible bonds with Monte Carlo simulation. We show that this algorithm outperforms LS and is also superior to RL in most cases. We demonstrate how to use Monte Carlo simulation with the methods described above for pricing a complex convertible bond trading at the Tel Aviv stock exchange. Like many Israeli convertibles, this bond exhibits the \"gradually diminishing principal\" feature, meaning that instead of one payment of the principal at maturity, there are multiple principal payments during the lifetime of the bond. This feature presents a challenge to existing models. We also model other exotic features of this bond, such as path-dependent conversion ratio and exchange rate indexation. The prices that we obtain using this model are close to the market prices of the bond.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863888","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}
{"title":"Implementing a Monte Carlo Simulation: Correlation, Skew, and Kurtosis","authors":"Stuart A. McCrary","doi":"10.2139/ssrn.2665147","DOIUrl":"https://doi.org/10.2139/ssrn.2665147","url":null,"abstract":"This manuscript is program documentation for various Monte Carlo models involving multiple correlated variables, skewed distributions, kurtotic distributions, or combinations of correlation, skew, and kurtosis.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115413041","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}
{"title":"A Computational Spectral Approach to Interest Rate Models","authors":"L. Di Persio, Gregorio Pellegrini, M. Bonollo","doi":"10.2139/ssrn.2635012","DOIUrl":"https://doi.org/10.2139/ssrn.2635012","url":null,"abstract":"The Polynomial Chaos Expansion (PCE) technique recovers a finite second order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochas- tic quantity {xi}, hence acting as a kind of random basis. The PCE methodology has been developed as a mathematically rigorous Uncertainty Quantification (UQ) method which aims at providing reliable numerical estimates for some uncertain physical quantities defining the dynamic of certain engineering models and their related simulations. In the present paper we exploit the PCE approach to analyze some equity and interest rate models considering, without loss of generality, the one dimensional case. In particular we will take into account those models which are based on the Geometric Brownian Motion (gBm), e.g. the Vasicek model, the CIR model, etc. We also provide several numerical applications and results which are discussed for a set of volatility values. The latter allows us to test the PCE technique on a quite large set of different scenarios, hence providing a rather complete and detailed investigation on PCE-approximation's features and properties, such as the convergence of statistics, distribution and quantiles. Moreover we give results concerning both an efficiency and an accuracy study of our approach by comparing our outputs with the ones obtained adopting the Monte Carlo approach in its standard form as well as in its enhanced version.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125028105","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}
{"title":"To Basel or Not to Basel? Banking Crises and Contagion","authors":"Aristeidis Samitas, Stathis Polyzos","doi":"10.1108/JFRC-11-2014-0045","DOIUrl":"https://doi.org/10.1108/JFRC-11-2014-0045","url":null,"abstract":"Purpose - – The purpose of this paper is to propose an object-oriented model of financial simulations which aims to test the applicability and suitability of the proposed measures of Basel III with respect to the prevention of banking crises. Design/methodology/approach - – The authors introduce an object-oriented model of financial simulations in the banking sector, namely, virtual banking (VBanking). The system is based on behavioural simulation of economic agents and allows for transactions between them, using various forms of financial assets. VBanking has been implemented as an automated stand-alone model, allowing for repetitive simulations under the same parameter sets, producing an efficient series of statistical data. Findings - – Interpretation of the resulting data suggests that some of the criticism against the proposed measures is justified, as neither economic crises nor contagion are diminished under Basel III. At the same time, the authors’ findings support that the stability goal is met, at least in part. Research limitations/implications - – The model encompasses a relatively small part of the banking sector, while the authors choose not to deal with the production part of the economy. However, these limitations do not hinder the validity and importance of the authors’ findings. Originality/value - – The originality of this article lies in the use of an object-oriented behavioural model and in the resulting model application that is based on it. This enables the authors to run a series of simulations with different parameters, the results of which the authors can then compare. The authors’ findings can contribute to the authorities’ efforts to ameliorate the policies of Basel III.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130132335","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}
{"title":"Validating the Assumptions of Sequential Bifurcation in Factor Screening","authors":"Wen Shi, J. Kleijnen","doi":"10.2139/ssrn.2627090","DOIUrl":"https://doi.org/10.2139/ssrn.2627090","url":null,"abstract":"Abstract Sequential bifurcation (or SB) is an efficient and effective factor-screening method; i.e., SB quickly identifies the important factors (inputs) in experiments with simulation models that have very many factors—provided the SB assumptions are valid. The specific SB assumptions are: (i) a second-order polynomial is an adequate approximation (a valid metamodel) of the input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation is monotonic); (iii) so-called “heredity” applies; i.e., if a specific input has a “small” first-order effect, then this input has “small” second order effects. Moreover, SB assumes Gaussian simulation outputs if the simulation model is stochastic (random). A generalization of SB called “multiresponse SB” (or MSB) uses the same assumptions, but allows multiple types of simulation responses (outputs). In this article, we develop heuristic practical methods for testing whether these assumptions hold, and we evaluate these methods through Monte Carlo experiments and a case study (namely, a Chinese logistics network).","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115140753","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}
{"title":"BLP Estimation Using Laplace Transformation and Overlapping Simulation Draws","authors":"H. Hong, Huiyu Li, Jessie Li","doi":"10.2139/ssrn.2612266","DOIUrl":"https://doi.org/10.2139/ssrn.2612266","url":null,"abstract":"Abstract We derive the asymptotic distribution of the parameters of the Berry et al. (1995) (BLP) model in a many markets setting which takes into account simulation noise under the assumption of overlapping simulation draws. We show that as long as the number of simulation draws R and the number of markets T approach infinity, our estimator is m = m i n ( R , T ) consistent and asymptotically normal. We do not impose any relationship between the rates at which R and T go to infinity, thus allowing for the case of R ≪ T . We provide a consistent estimate of the asymptotic variance which can be used to form asymptotically valid confidence intervals. Instead of directly minimizing the BLP GMM objective function, we propose using Hamiltonian Markov Chain Monte Carlo methods to implement a Laplace-type estimator which is asymptotically equivalent to the GMM estimator.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115046037","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}
{"title":"An Information Theoretic Criterion for Empirical Validation of Time Series Models","authors":"F. Lamperti","doi":"10.2139/ssrn.2570828","DOIUrl":"https://doi.org/10.2139/ssrn.2570828","url":null,"abstract":"Simulated models suffer intrinsically from validation and comparison problems. The choice of a suitable indicator quantifying the distance between the model and the data is pivotal to model selection. However, how to validate and discriminate between alternative models is still an open problem calling for further investigation, especially in light of the increasing use of simulations in social sciences. In this paper, we present an information theoretic criterion to measure how close models' synthetic output replicates the properties of observable time series without the need to resort to any likelihood function or to impose stationarity requirements. The indicator is sufficiently general to be applied to any kind of model able to simulate or predict time series data, from simple univariate models such as Auto Regressive Moving Average (ARMA) and Markov processes to more complex objects including agent-based or dynamic stochastic general equilibrium models. More specifically, we use a simple function of the L-divergence computed at different block lengths in order to select the model that is better able to reproduce the distributions of time changes in the data. To evaluate the L-divergence, probabilities are estimated across frequencies including a correction for the systematic bias. Finally, using a known data generating process, we show how this indicator can be used to validate and discriminate between different models providing a precise measure of the distance between each of them and the data.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127102748","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}
{"title":"The Impact of Step-Down Unit Care on Patient Outcomes","authors":"Lijian Lu, Carri W. Chan, L. Green, G. Escobar","doi":"10.2139/ssrn.2859231","DOIUrl":"https://doi.org/10.2139/ssrn.2859231","url":null,"abstract":"Step Down Units (SDUs) were initially introduced in hospitals in order to provide an intermediate level of care for semi-critically ill patients who are not sick enough to require intensive care but not stable enough to be treated in the general medical/surgical ward. However, there is a lack of consensus within the medical community as to how these units should be used as well as the impact of SDU care on patient outcomes. Using data from 10 hospitals from a single hospital network, we use instrumental variable approaches to estimate the impact on patient outcomes of routing patients to the SDU from the Emergency Department (ED) as well as the Intensive Care Unit (ICU). Our empirical findings suggest that SDU care is associated with better clinical outcomes for some patients – reducing in-hospital mortality by 6%-20%, shortening hospital length-of-stay (LOS) by 0.5-1.08 days or reducing ICU readmission rate by 4% and hospital readmission rate by 8%. However, inappropriately admitting patients to the SDU is associated with an increase in mortality risk by 1.60% and hospital LOS by nearly a factor of 2. Our findings suggest that an SDU may be a cost effective way to treat patients when used as a true step down unit, i.e. for patients who are post-ICU. However, the impact of SDU care is more nuanced if and when other patients are admitted— for some patients, SDU admission is associated with substantial degradation of outcomes, while for others, it is associated with slightly improved outcomes.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116421732","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}
{"title":"Portfolio Optimization and Monte Carlo Simulation","authors":"M. E. H. Pedersen","doi":"10.2139/SSRN.2438121","DOIUrl":"https://doi.org/10.2139/SSRN.2438121","url":null,"abstract":"This paper uses Monte Carlo simulation of a simple equity growth model with resampling of historical financial data to estimate the probability distributions of the future equity, earnings and payouts of companies. The simulated equity is then used with the historical P/Book distribution to estimate the probability distributions of the future stock prices. This is done for Coca-Cola, Wal-Mart, McDonald’s and the S&P 500 stock-market index. The return distributions are then used to construct optimal portfolios using the “Markowitz” (mean-variance) and “Kelly” (geometric mean) methods. It is shown that variance is an incorrect measure of investment risk so that mean-variance optimal portfolios do not minimize risk as commonly believed. This criticism holds for return distributions in general. Kelly portfolios are correctly optimized for investment risk and long-term gains, but the portfolios are often concentrated in few assets and are therefore sensitive to estimation errors in the return distributions.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123404257","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}
{"title":"A MPEC Estimator for Misclassification Models","authors":"Ruichang Lu, Yao Luo, Ruli Xiao","doi":"10.2139/ssrn.2352935","DOIUrl":"https://doi.org/10.2139/ssrn.2352935","url":null,"abstract":"In this paper, we propose a constrained maximum likelihood estimator for misclassification models, by formulating the estimation as an MPEC (Mathematical Programming with Equilibrium Constraints) problem. Our approach improves the numerical accuracy and avoids the singularity problem. Monte Carlo simulations confirm that the proposed estimator reduces bias and standard deviation of the estimator, especially when the sample is small/medium and/or the dimension of latent variable is large.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129015999","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}