{"title":"Comparison of Specification Tests for GARCH Models","authors":"K. Ghoudi, B. Rémillard","doi":"10.2139/ssrn.2046072","DOIUrl":"https://doi.org/10.2139/ssrn.2046072","url":null,"abstract":"Specification procedures for testing the null hypothesis of a Gaussian distribution for the innovations of GARCH models are compared using simulations. More precisely, Cramer-von Mises and Kolmogorov-Smirnov type statistics are computed for empirical processes based on the standardized residuals and their squares. For calculating P-values, the parametric bootstrap method and the multipliers method are used. In addition, the Khmaladze transform is also applied to obtain an approximate Brownian motion under the null hypothesis, for which Cramer-von Mises and Kolmogorov-Smirnov type statistics are computed, using both the standardized residuals and their squares.","PeriodicalId":163739,"journal":{"name":"ERN: Model Construction & Selection (Topic)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114214291","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":"Can 'Time-Invariant' Hedges Estimated from Time-Varying Hedge Models Outperform Time-Invariant Hedges Even During Period of Financial Crisis? An Empirical Investigation of Indian Futures Market","authors":"S. K. Thakur","doi":"10.2139/ssrn.1915124","DOIUrl":"https://doi.org/10.2139/ssrn.1915124","url":null,"abstract":"The real test of effectiveness of hedge models and its consistency happens when markets are turbulent and during extreme events. This study is, therefore, an attempt to explore Indian futures market for hedging by equity holders in general as well as in period of recent financial crisis. We have estimated effectiveness of the optimal hedge ratio based on HKM [Herbst, Kare and Marshall (1993)] methodology with benchmark model JSE [Johnson (1960), Stein (1961) and Ederington (1979)] methodology for futures. Hedge ratio based on HKM methodology is a time-variant whereas hedge ratio based on JSE methodology is a constant and time-invariant. To bring the comparison of hedge effectiveness on equal level (from transaction cost point of view), time-varying hedge ratio estimated based on HKM methodology are made “time-invariant” and then Bases using the hedge ratios are estimated. For empirical validation of the Effectiveness of the optimal hedge ratios and their stability in normal as well in the period of financial crisis, the study of S&P Nifty Index {National Stock Exchange of India (NSE) – 50 Index and its futures is conducted using daily data for the year 2005 (representing normal period) and January, 2007 to June,2009 (representing turbulent time period) based on the value of volatility index. Results suggest that hedge using HKM model is more effective than that of hedge based on JSE model. The results are statistically significant at 95% confidence level. An additional contribution of this study is to help the hedger to decide “when” to re-balance the hedge.","PeriodicalId":163739,"journal":{"name":"ERN: Model Construction & Selection (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115809857","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":"Supply and Demand in Residential Property Forecasts","authors":"Matthew Hardman","doi":"10.2139/ssrn.1861625","DOIUrl":"https://doi.org/10.2139/ssrn.1861625","url":null,"abstract":"This paper defines a measure of net housing demand or supply which allows the calculation of a long, high frequency time series, whose principal use is intended to be in a multivariate residential property price forecasting model. The series is easily and inexpensively replicable. We formulate this measure as excess demand. A negative excess demand figure implies a net oversupply. Supply is defined as the available dwelling stock. Demand is calculated from the long term trend in adult dwelling occupancy rates and is thus structural, rather than implicit. We examine the usefulness of our excess demand series in a forecasting model of property index returns over horizons from 6 months to 5 years. It is found that the explanatory power of current excess demand in estimating future property returns increases with forecast horizon.","PeriodicalId":163739,"journal":{"name":"ERN: Model Construction & Selection (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133424595","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":"Model Selection and Adaptive Markov Chain Monte Carlo for Bayesian Cointegrated VAR Model","authors":"G. Peters, B. Kannan, B. Lasscock, C. Mellen","doi":"10.2139/ssrn.2980412","DOIUrl":"https://doi.org/10.2139/ssrn.2980412","url":null,"abstract":"This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropolis algorithm of Roberts and Rosenthal, (2009). Developing the adaptive MCMC framework for Bayesian CVAR models allows for efficient estimation of posterior parameters in significantly higher dimensional CVAR series than previously possible with existing griddy Gibbs samplers. For a n-dimensional CVAR series, the matrix-variate posterior is in dimension $3n^2 + n$, with significant correlation present between the blocks of matrix random variables. We also treat the rank of the CVAR model as a random variable and perform joint inference on the rank and model parameters. This is achieved with a Bayesian posterior distribution defined over both the rank and the CVAR model parameters, and inference is made via Bayes Factor analysis of rank. Practically the adaptive sampler also aids in the development of automated Bayesian cointegration models for algorithmic trading systems considering instruments made up of several assets, such as currency baskets. Previously the literature on financial applications of CVAR trading models typically only considers pairs trading (n=2) due to the computational cost of the griddy Gibbs. We are able to extend under our adaptive framework to $n >> 2$ and demonstrate an example with n = 10, resulting in a posterior distribution with parameters up to dimension 310. By also considering the rank as a random quantity we can ensure our resulting trading models are able to adjust to potentially time varying market conditions in a coherent statistical framework.","PeriodicalId":163739,"journal":{"name":"ERN: Model Construction & Selection (Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114859739","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":"Bayesian Analysis in Moment Inequality Models","authors":"Yuan Liao, Wenxin Jiang","doi":"10.2139/ssrn.2028118","DOIUrl":"https://doi.org/10.2139/ssrn.2028118","url":null,"abstract":"This paper presents a study of the large-sample behavior of the posterior distribution of a structural parameter which is partially identified by moment inequalities. The posterior density is derived based on the limited information likelihood. The posterior distribution converges to zero exponentially fast on any δ -contraction outside the identified region. Inside, it is bounded below by a positive constant if the identified region is assumed to have a nonempty interior. Our simulation evidence indicates that the Bayesian approach has advantages over frequentist methods, in the sense that, with a proper choice of the prior, the posterior provides more information about the true parameter inside the identified region.We also address the problem of moment and model selection. Our optimality criterion is the maximum posterior procedure and we show that, asymptotically, it selects the true moment/model combination with the most moment inequalities and the simplest model.","PeriodicalId":163739,"journal":{"name":"ERN: Model Construction & Selection (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130450800","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":"From the General to the Specific: Modelling Inflation in China","authors":"J. Reade, Ulrich Volz","doi":"10.3790/AEQ.57.1.27","DOIUrl":"https://doi.org/10.3790/AEQ.57.1.27","url":null,"abstract":"This article uses automatic model selection procedures, based on the general-to-specific approach, to investigate inflation in China. A novelty of this article is the use of a technique called impulse indicator saturation which allows us to uncover instabilities and to specify a very general model and select down to a more specific model that best explains inflation in China. By and large, our findings suggest that China has been able to insulate itself against shocks from the US, although (maybe surprisingly) monetary growth in Europe seems to have an effect. Nonetheless, the main factors impacting Chinese inflation appear to be domestic, namely GDP growth and money growth.","PeriodicalId":163739,"journal":{"name":"ERN: Model Construction & Selection (Topic)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127957454","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
