ERN: Simulation Methods (Topic)最新文献

{"title":"Mean Aversion in Fixed Income Returns: Evidence and Implications for Long Horizon Investors","authors":"Andrew C. Szakmary","doi":"10.2139/ssrn.2153312","DOIUrl":"https://doi.org/10.2139/ssrn.2153312","url":null,"abstract":"This study documents strong mean aversion in U.S. fixed income returns (but not stock returns) at 5-20 year horizons. These results are only slightly weaker for nominal returns than for real returns and prevail regardless of the period examined (1926-2011, 1951-2011, 1857-1925 or 1857-2011). I show that the presence of mean aversion, along with a historically sizable equity premium, dramatically increases the risk of holding fixed income securities for long horizon investors, to the point where they are often actually riskier than stocks in a downside-risk framework for investment horizons as short as ten years. These results are fairly robust to the use of nominal vs. real returns, different sample periods from which simulated returns are drawn and even to sizable reductions in the historical equity premium.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211792","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":"Is Lottery Demand Driven by Effective Price? Evidence from the China Lottery Industry","authors":"Jia Yuan, J. Gao","doi":"10.2139/ssrn.2151664","DOIUrl":"https://doi.org/10.2139/ssrn.2151664","url":null,"abstract":"There has been a long debate on whether the expected utility model can explain lottery purchase decision under uncertainty. The traditional strand of research, named the effective price approach, following the expected utility approach takes the expected loss of each ticket as the lottery price and uses this effective price to understand lottery demand. Another branch of literature, more from the prospect theory perspective, argues that lottery demand depends more on jackpot size, or small odds events, than expected values. It is hard to separately evaluate these two different approaches as they share common factors which are hard to cleanly differentiate. In this paper, we examine these two approaches by exploiting a unique lottery game setup in China. This lottery game is similar to the lotteries in other countries except that there is a cap policy on the grand prize, which limits the reward of each jackpot winner. We show that this seemingly complex cap-policy actually causes the whole lottery price to be almost fixed all of the time although the rollover money from the last draw significantly varies. This suggests that the effective price approach cannot explain the observed variation in lottery sales. We further conduct Monte Carlo simulations and provide evidence showing that the popular 2SLS method using rollover and its square as instruments in the effective price literature may give spurious estimation result. We also find that lottery sales are highly correlated to the rollover size.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123573084","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":"Stock Prices in an Artificial Stock Market with Optimistic and Pessimistic Agents","authors":"H. Kimura, F. Lima, L. J. Perera, R. B. Kerr","doi":"10.2139/ssrn.2146989","DOIUrl":"https://doi.org/10.2139/ssrn.2146989","url":null,"abstract":"This research aims to investigate, through simulation models, how the interaction among agents in an artificial stock market can affect the dynamics of asset prices. Thus, the study follows a different methodology for the analysis of prices by exploring the simulation of agents’ behavior in an artificial stock market. From the defining characteristics of heterogeneous agents, we set up an artificial stock market in which individuals interact, by demanding and supplying assets, driving the price of a stock to an equilibrium value. The results suggest that, under the assumption of utility maximizers agents with different expectations about future dividends, asset prices may under-react. The gradual change of prices observed in the sub-reaction confronts the efficient market hypothesis, in which all information is instantly","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115572808","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":"Structural Threshold Quantile Regression","authors":"Chung-Ming Kuan, Christos Michalopoulos","doi":"10.2139/ssrn.2133713","DOIUrl":"https://doi.org/10.2139/ssrn.2133713","url":null,"abstract":"In this paper, a simultaneous equation model with an endogenous variable and an exogenous threshold variable is analysed and estimated thereby extending Caner and Hansen (2004) model to quantile regression. In our framework, we allow both the reduced-form and the structural equation to exhibit regime-change behavior. An two-step estimation procedure for the model parameters is proposed assuming an unknown threshold value while existing estimation procedures are extended to deal with the case of a known threshold value. We develop an asymptotic frame-work for the parameter estimators and the estimated threshold value assuming the size of the regime-change shrinks as the sample size increases and we derive the limiting distribution of the last. A likelihood-ratio-type statistic is employed to test hypotheses of interest concerning the estimated threshold and its limiting distribution is derived. We also form confidence regions robust for heteroskedasticity for the estimated threshold by inverting the likelihood-ratio-type statistic as in Hansen (2000) and simulate its coverage probability. Extensive simulation assesses the accuracy of our estimation procedure in detecting the unknown threshold value under different error distributions and size of the threshold.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892036","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 Historical Simulation Method for Value-at-Risk: A Research Based Evaluation of the Industry Favorite","authors":"Meera Sharma","doi":"10.2139/ssrn.2042594","DOIUrl":"https://doi.org/10.2139/ssrn.2042594","url":null,"abstract":"This paper surveys the literature relating to the historical simulation method of calculating VaR. The historical simulation method is the most popular method for VaR calculation in the banking industry. Thirty Eight papers are surveyed to understand the performance measures for VaR methods and the comparative performance of Historical Simulation VaR methods. The performance measures are broadly divided into unconditional coverage and conditional coverage measures. While regulatory requirements are limited to unconditional coverage measures, conditional coverage measures have been developed to spot the phenomenon of exception clustering. The performance of historical simulation - the basic and modified methods - in comparison with other methods is surveyed through available studies. The historical simulation approach is found to provide superior unconditional coverage among a wide variety of alternate methods ranging from the simple variance covariance approach to the sophisticated GARCH, explaining its popularity in the industry. This superiority translates into lesser likelihood of regulatory penalties since the regulatory back testing framework is based on unconditional coverage. The advantage of superior performance by historical simulation is lost when it is measured on conditional coverage measures (joint tests of unconditional coverage and independence). However, the sophisticated conditional volatility models like GARCH are not much better than the historical simulation in conditional coverage. A modification to the historical simulation method, the filtered historical simulation method emerges as the best performer using conditional coverage criteria. This study has an important contribution to make to available research. First it compiles the performance measures of VaR methods. Second it surveys the modifications to the historical simulation approach. Third and most important it presents a comparative picture of the most popular approach vis a vis other methods on a variety of performance parameters.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838236","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":"MEA-Pensim 2.0: Weiterentwicklung eines Rentensimulationsmodells, Konzeption und ausgewählte Anwendungen (MEA-Pensim 2.0: Advancement of a Pension Simulation Model, Conception and Selected Applications)","authors":"A. Holthausen, J. Rausch, C. Wilke","doi":"10.2139/SSRN.2573520","DOIUrl":"https://doi.org/10.2139/SSRN.2573520","url":null,"abstract":"German Abstract: Die demographische Entwicklung stellt die Gesetzliche Rentenversicherung vor enorme Her-ausforderungen. Vor allem steht die Frage nach der zukunftigen Finanzierbarkeit des Sys-tems in seiner heutigen Form im Mittelpunkt der Diskussion. Um die zukunftige Entwicklung sowie die Auswirkungen verschiedener Faktoren und moglicher Reformen auf das deutsche Rentensystem prognostizieren zu konnen, wird ein Simulationsmodell benotigt, welches das gesetzliche Rentensystem inklusive aller entscheidenden Determinanten (u.a. Bevolkerung, Arbeitsmarkt) abbildet. Ziel dieses Papiers ist es, das Rentensimulationsmodell MEA-Pensim 2.0 vorzustellen. Dabei handelt es sich um eine Weiterentwicklung des Rentensimulations-modells MEA-Pensim aus dem Jahr 2004. Das Papier gliedert sich hierbei in sechs Abschnitte, wobei das Modell in den Abschnitten 2, 3 und 4 beschrieben wird. Im funften Abschnitt werden ausgewahlte Anwendungen vorgestellt und diskutiert. So werden u.a. unterschiedliche Bevolkerungs- und Arbeitsmarktentwicklungen sowie der Vorschlag der Bundesregierung zur Anhebung der Zurechnungszeit auf ihre Auswirkungen untersucht. Abschliesend wird ein Ausblick auf offene Fragestellungen gegeben.English Abstract: The German Pension System is confronted with huge challenges by the demographic trend. Especially the current systems financially viability is in focus of the present discussion. To be able to prognosticate the future development and impacts of different factors or possible reforms on the German Pension System, we need a simulation model, which reproduces the pension system and all its critical determinants (i.e. the population and the labour market). The goal of this paper is to present the pension simulation model MEA‐Pensim 2.0, an advancement of the pension simulation model MEA‐Pensim of the year 2004. Therefore, this paper is divided into six chapters. The model is described in the chapters 2, 3 and 4. In the fifth chapter we present and discuss some selected applications. In this context we analyse, among other things, different population and labour market developments as well as the impacts of the proposal by the federal government to extend the supplementary period. Finally we give a perspective on open questions that remain.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131096747","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":"Active Momentum Trading versus Passive '1/N Naive Diversification'","authors":"A. Banerjee, C. Hung","doi":"10.2139/ssrn.2279336","DOIUrl":"https://doi.org/10.2139/ssrn.2279336","url":null,"abstract":"We consider a passive 1/N naive diversification strategy which is long in the equally weighted portfolio of stocks feasible for trading and short in the risk-free asset. We then examine the profi tability, exposures to risk factors, idiosyncratic variance as well as the relation between the momentum and the 1/N naive diversifi cation strategies. The set of sample stocks that we use to construct the equally weighted portfolio is the same as that for constructing the momentum strategies. We fi nd that both strategies generate an average pro t of 1% per month. The differences in raw and risk-adjusted pro ts between these two strategies are statistically and economically insignifi cant. These two strategies are independent of each other. The idiosyncratic variance of the momentum strategies is 20 times higher than that of the naive diversifi cation strategy. The findings are consistent over the period between 1926 and 2005 and various sub-sample periods including the periods examined in Jegadeesh and Titman (1993 and 2001) and in our simulations where we randomly select 10 years for 100 times. The results also hold when we conduct tests in sub-samples where we divide the sample, each month, into two groups of large and small size stocks.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130006894","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":"Simulation-Based Valuation of Project Finance - Does Model Complexity Really Matter?","authors":"Florian Weber, Thomas Schmid, M. Pietz, C. Kaserer","doi":"10.2139/ssrn.1594116","DOIUrl":"https://doi.org/10.2139/ssrn.1594116","url":null,"abstract":"This paper analyzes the impact of model complexity on the net present value distribution and the expected default probability of equity investments in project finance. Model complexity is analyzed along two dimensions: simulation complexity and forecast complexity. We aim to identify model elements which are crucial for the valuation of project finance in practice. First, we present a simulation-based project finance valuation model. Second, we vary several model aspects in order to analyze their impact on the valuation result. For forecast complexity, we apply different volatility and correlation forecasting techniques, e.g. correlation forecasts based on historical values and on a dynamic conditional correlation (DCC) model. Regarding simulation complexity, the number of Monte Carlo iterations, the equity valuation method, and the time resolution are varied. We find that the applied volatility forecasting models have a strong influence on the expected net present value distribution and on the probability of default. In contrast, correlation forecasting models play a minor role. Time resolution and equity valuation are both crucial when specifying a valuation model for project finance. For the number of Monte Carlo iterations, we demonstrate that 100,000 iterations are sufficient to obtain reliable results.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123634716","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":"State-Observation Sampling and the Econometrics of Learning Models","authors":"Laurent E. Calvet, Veronika Czellar","doi":"10.2139/ssrn.1847646","DOIUrl":"https://doi.org/10.2139/ssrn.1847646","url":null,"abstract":"Author's abstract. In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the incomplete-information equilibrium models considered in financial economics. In this paper, we make two contributions to the learning literature. First, we introduce a new filtering method, the state-observation sampling (SOS) filter, for general state-space models with intractable observation densities. Second, we develop an indirect inference-based estimator for a large class of incomplete-information economies. We demonstrate the good performance of these techniques on an asset pricing model with investor learning applied to over 80 years of daily equity returns.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124587702","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":"Drawdown-at-Risk Monte Carlo Optimization","authors":"Andreas Steiner","doi":"10.2139/ssrn.1782664","DOIUrl":"https://doi.org/10.2139/ssrn.1782664","url":null,"abstract":"We present a portfolio construction approach with two interesting non-standard features: First, the risk measure used is “drawdown-at-risk”, an interesting concept combining attractive features of drawdown and value-at-risk measures. Second, the efficient frontier is calculated from “random portfolios”, i.e. portfolios containing random constituent weights. We call this “Monte Carlo optimization”. Both features would deserve a detailed analysis. The goal of this note is to provide an overview and illustrate the potential of the approach with two examples: Asset allocation in a small universe consisting of three assets (Indian stocks and bonds as well as gold in INR) and drawdown optimization on single-stock level across the full S&P 500 universe.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129766020","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}