Pub Date : 2012-08-01DOI: 10.1111/j.1468-2354.2012.00702.x
A. Markiewicz
This article proposes an explanation for shifts in the volatility of exchange-rate returns. Agents are uncertain about the true data generating model and deal with this uncertainty by making inference on the models and their parameters' approach, I call model learning. Model learning may lead agents to focus excessively on a subset of fundamental variables. Consequently, exchange-rate volatility is determined by the dynamics of these fundamentals and changes as agents alter models. I investigate the empirical relevance of model learning and find that the change in volatility of GBP/USD in 1993 was triggered by a shift between models.
{"title":"Model Uncertainty and Exchange Rate Volatility","authors":"A. Markiewicz","doi":"10.1111/j.1468-2354.2012.00702.x","DOIUrl":"https://doi.org/10.1111/j.1468-2354.2012.00702.x","url":null,"abstract":"This article proposes an explanation for shifts in the volatility of exchange-rate returns. Agents are uncertain about the true data generating model and deal with this uncertainty by making inference on the models and their parameters' approach, I call model learning. Model learning may lead agents to focus excessively on a subset of fundamental variables. Consequently, exchange-rate volatility is determined by the dynamics of these fundamentals and changes as agents alter models. I investigate the empirical relevance of model learning and find that the change in volatility of GBP/USD in 1993 was triggered by a shift between models.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"119952863","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}
An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.
{"title":"Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling","authors":"J. Janczura, S. Trück, R. Weron, R. Wolff","doi":"10.2139/ssrn.2081738","DOIUrl":"https://doi.org/10.2139/ssrn.2081738","url":null,"abstract":"An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126440588","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}
We propose an efficient method to evaluate callable and putable bonds under a wide class of interest rate models, including the popular short rate diffusion models, as well as their time changed versions with jumps. The method is based on the eigenfunction expansion of the pricing operator. Given the set of call and put dates, the callable and putable bond pricing function is the value function of a stochastic game with stopping times. Under some technical conditions, it is shown to have an eigenfunction expansion in eigenfunctions of the pricing operator with the expansion coefficients determined through a backward recursion. For popular short rate diffusion models, such as CIR, Vasicek, 3/2, the method is orders of magnitude faster than the alternative approaches in the literature. In contrast to the alternative approaches in the literature that have so far been limited to diffusions, the method is equally applicable to short rate jump–diffusion and pure jump models constructed from diffusion models by Bochner's subordination with a Levy subordinator.
{"title":"Evaluating Callable and Putable Bonds: An Eigenfunction Expansion Approach","authors":"Dongjae Lim, Lingfei Li, V. Linetsky","doi":"10.2139/ssrn.2089131","DOIUrl":"https://doi.org/10.2139/ssrn.2089131","url":null,"abstract":"We propose an efficient method to evaluate callable and putable bonds under a wide class of interest rate models, including the popular short rate diffusion models, as well as their time changed versions with jumps. The method is based on the eigenfunction expansion of the pricing operator. Given the set of call and put dates, the callable and putable bond pricing function is the value function of a stochastic game with stopping times. Under some technical conditions, it is shown to have an eigenfunction expansion in eigenfunctions of the pricing operator with the expansion coefficients determined through a backward recursion. For popular short rate diffusion models, such as CIR, Vasicek, 3/2, the method is orders of magnitude faster than the alternative approaches in the literature. In contrast to the alternative approaches in the literature that have so far been limited to diffusions, the method is equally applicable to short rate jump–diffusion and pure jump models constructed from diffusion models by Bochner's subordination with a Levy subordinator.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125885885","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}
This article attempts to identify the best model of the short term interest rates that can predict its stochastic process over time. We studied eight different models of interest rates in the short term. The choice of these models was the aim of analyzing the relevance of certain specifications of the stochastic process of the short term interest rates, the effect of mean reversion and the sensitivity of the volatility to the level of interest rate. The yield on three months treasury bills is used as a proxy for the short term interest rates. The parameters of the different stochastic process are estimated using the generalized method of moments. The results show that the effect of mean reversion is not statistically significant and that volatility is highly sensitive to the level of interest rates. To further study the performance prediction of the intertemporal behavior of the short term interest rate of the various models; we simulated their stochastic process for different periods. The results show that none of the studied models reproduce the actual path of the short term interest rates. The problem lies in the parametric specification of the mean and volatility of the diffusion process.
{"title":"An Empirical Comparison of the Short Term Interest Rate Models","authors":"M. Salah, Fathi Abid","doi":"10.2139/ssrn.2400433","DOIUrl":"https://doi.org/10.2139/ssrn.2400433","url":null,"abstract":"This article attempts to identify the best model of the short term interest rates that can predict its stochastic process over time. We studied eight different models of interest rates in the short term. The choice of these models was the aim of analyzing the relevance of certain specifications of the stochastic process of the short term interest rates, the effect of mean reversion and the sensitivity of the volatility to the level of interest rate. The yield on three months treasury bills is used as a proxy for the short term interest rates. The parameters of the different stochastic process are estimated using the generalized method of moments. The results show that the effect of mean reversion is not statistically significant and that volatility is highly sensitive to the level of interest rates. To further study the performance prediction of the intertemporal behavior of the short term interest rate of the various models; we simulated their stochastic process for different periods. The results show that none of the studied models reproduce the actual path of the short term interest rates. The problem lies in the parametric specification of the mean and volatility of the diffusion process.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123093819","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}
Spanish Abstract: En el presente trabajo se desarrolla un analisis de las propiedades de los modelos Multinomiales a traves de distintos procesos de simulacion; lo anterior se realizo asumiendo tanto el cumplimiento de los supuestos subyacentes de los mecanismos de estimacion como el incumplimiento de los mismos. Igualmente se analizo el comportamiento de los estimadores bajo diferentes escenarios de tamano muestral. Se encontro que bajo un modelo correctamente especificado y tamanos muestrales superiores a 200 observaciones, se cumplen las propiedades de insesgadez y consistencia, mientras que la incorrecta especificacion de la distribucion del proceso lleva a estimaciones sesgadas e inconsistentes; de igual forma se encontro que en tamanos muestrales pequenos y bajo modelos Condicionales se pierden las propiedades que una buena especificacion del proceso suele generar, hallandose aun mas inestabilidad cuando la estimacion es llevada a cabo con la metodologia Probit.English Abstract: This paper develops an analysis of Multinomial models through simulation; this was done under correct and incorrect assumptions on the data generating process. Also, it was analyzed the performance of the models under different sample sizes. It was found that a correct specified model with samples of 200 or more observations achieves estimators which are unbiased and consistent, while incorrect assumptions about the data generating process causes biased and inconsistent estimators. On the other hand, conditional models with small sample sizes imply bad statistical properties, especially when Probit models are estimated.
{"title":"Modelos Multinomiales: Un Análisis De Sus Propiedades (Multinomial Models: An Analysis of Its Properties)","authors":"Arlen Guarín, Andrés Ramírez Hassan, J. Torres","doi":"10.2139/SSRN.2419734","DOIUrl":"https://doi.org/10.2139/SSRN.2419734","url":null,"abstract":"Spanish Abstract: En el presente trabajo se desarrolla un analisis de las propiedades de los modelos Multinomiales a traves de distintos procesos de simulacion; lo anterior se realizo asumiendo tanto el cumplimiento de los supuestos subyacentes de los mecanismos de estimacion como el incumplimiento de los mismos. Igualmente se analizo el comportamiento de los estimadores bajo diferentes escenarios de tamano muestral. Se encontro que bajo un modelo correctamente especificado y tamanos muestrales superiores a 200 observaciones, se cumplen las propiedades de insesgadez y consistencia, mientras que la incorrecta especificacion de la distribucion del proceso lleva a estimaciones sesgadas e inconsistentes; de igual forma se encontro que en tamanos muestrales pequenos y bajo modelos Condicionales se pierden las propiedades que una buena especificacion del proceso suele generar, hallandose aun mas inestabilidad cuando la estimacion es llevada a cabo con la metodologia Probit.English Abstract: This paper develops an analysis of Multinomial models through simulation; this was done under correct and incorrect assumptions on the data generating process. Also, it was analyzed the performance of the models under different sample sizes. It was found that a correct specified model with samples of 200 or more observations achieves estimators which are unbiased and consistent, while incorrect assumptions about the data generating process causes biased and inconsistent estimators. On the other hand, conditional models with small sample sizes imply bad statistical properties, especially when Probit models are estimated.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131238223","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}
Korean Abstract: 본 연구는 장기과잉반응가설의 검정에서 생존자편의가 나타날 수 있는가를 검정하였다. 이를 위하여 생존분석방법을 이용하였으며 상장폐지율에 영향을 미치는 요인으로 관리종목 여부, 직전기간 수익률, 주식시장가격, 주식시장가격/액면가격에 대하여 검정하였다. 결과에 따르면 주요내용은 다음과 같다. 첫째 관리종목의 경우 상장폐지율은 유의하게 높았던 것으로 나타났다. 둘째 직전기간을 6, 12, 24, 36개월로 설정하였을 때 직전기간 수익률에 따른 상장폐지율의 차이는 유의하지 않았던 것으로 나타났다. 셋째 관찰시점의 주식시장가격 또는 주식시장가격/액면가격이 낮을수록 예측기간 상장폐지율은 높았던 것으로 나타났다. 이 결과는 비교적 단기간의 직전기간 수익률을 이용하는 장기과잉반응가설의 검정에서 생존자편의가 그다지 중요하지 않을 수 있음을 시사한다. 하지만 장기간의 누적수익에 해당되는 주식시장가격과 예측기간 상장폐지율과 (‐)의 상관관계를 갖는 것으로 나타났기 때문에 장기간의 직전 수익률이 낮았다면 예측기간 상장폐지율은 높다고 볼 수 있다.
English Abstract: This article studies whether the survivor bias is important in testing the hypothesis of long-term overreaction. The survival analysis is used including the dummy variables of the administrative issue, the cumulative returns in the reference periods, the market price of equity, and the ratio of market price to face value of equity as covariates. The main results are as follows: First, de-listings in administrative issues are large. Second, cumulative returns in the reference periods are not significant in explaining the de-listings. Third, de-listings of the stocks with a low market price and low ratio of market price to face value are large. The results imply that the survivor bias is not important in testing the hypothesis of long-term overreaction when the cumulative returns in the reference periods shorter than 36 months are used. However, for the cumulative returns in longer terms, the survivor bias may be important.
Korean Abstract:本研究在长期过度反应假说的鉴定中，是否会出现生者的便利。为此，利用生存分析方法，作为影响上市取消率的因素，对是否为管理项目、前一期间收益率、股票市场价格、股票市场价格/票面价格进行了考核。结果表明，主要内容为:第一，从管理项目的情况看，取消上市资格的比率非常高。第二，如果将前一段时间设定为6、12、24、36个月，根据前一段时间收益率的上市资格废除率差异并没有被注意。第三，观察时的股票市场价格或股票市场价格/票面价格越低，预测期间的上市取消率就越高。该结果暗示，在利用较短的前一段时间收益率的“长期过剩反应假设”的鉴定中，生存者的便利可能并不重要。但据调查，这与相当于长期累计收益的股票市场价格和预测期间取消上市资格率(‐)有关系，因此，如果长期之前收益率低，预测期间取消上市资格率就会高。英语:This article studies whether the survivor bias is important in testing the hypothesis of long-term overreaction。The survival analysis used including The dummy variables of The administrative issue, The cumulative returns in The reference periods, The market price of equityand the ratio of market price to face value of equity as covariates。The main results are as follows: First, de-listings in administrative issues are large。cumulative returns in the reference periods are not significant in explaining the de-listings。Third, de-listings of the stocks with a low market price and low ratio of market price to face value are large。The results imply that The survivor bias is not important in testing The hypothesis of long-term overreaction when The cumulative returns in The reference periods shorter than 36 months are used。However, for the cumulative returns in longer terms, the survivor bias may be important。
{"title":"상장폐지율과 생존자편의에 관한 실증연구 (An Empirical Study on the Delisting Rate and the Survivor Bias)","authors":"J. Yoon","doi":"10.2139/ssrn.3018342","DOIUrl":"https://doi.org/10.2139/ssrn.3018342","url":null,"abstract":"<b>Korean Abstract:</b> 본 연구는 장기과잉반응가설의 검정에서 생존자편의가 나타날 수 있는가를 검정하였다. 이를 위하여 생존분석방법을 이용하였으며 상장폐지율에 영향을 미치는 요인으로 관리종목 여부, 직전기간 수익률, 주식시장가격, 주식시장가격/액면가격에 대하여 검정하였다. 결과에 따르면 주요내용은 다음과 같다. 첫째 관리종목의 경우 상장폐지율은 유의하게 높았던 것으로 나타났다. 둘째 직전기간을 6, 12, 24, 36개월로 설정하였을 때 직전기간 수익률에 따른 상장폐지율의 차이는 유의하지 않았던 것으로 나타났다. 셋째 관찰시점의 주식시장가격 또는 주식시장가격/액면가격이 낮을수록 예측기간 상장폐지율은 높았던 것으로 나타났다. 이 결과는 비교적 단기간의 직전기간 수익률을 이용하는 장기과잉반응가설의 검정에서 생존자편의가 그다지 중요하지 않을 수 있음을 시사한다. 하지만 장기간의 누적수익에 해당되는 주식시장가격과 예측기간 상장폐지율과 (‐)의 상관관계를 갖는 것으로 나타났기 때문에 장기간의 직전 수익률이 낮았다면 예측기간 상장폐지율은 높다고 볼 수 있다.<br><br><b>English Abstract:</b> This article studies whether the survivor bias is important in testing the hypothesis of long-term overreaction. The survival analysis is used including the dummy variables of the administrative issue, the cumulative returns in the reference periods, the market price of equity, and the ratio of market price to face value of equity as covariates. The main results are as follows: First, de-listings in administrative issues are large. Second, cumulative returns in the reference periods are not significant in explaining the de-listings. Third, de-listings of the stocks with a low market price and low ratio of market price to face value are large. The results imply that the survivor bias is not important in testing the hypothesis of long-term overreaction when the cumulative returns in the reference periods shorter than 36 months are used. However, for the cumulative returns in longer terms, the survivor bias may be important.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114619451","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}
This paper suggests that the relevant question concerning unit root' in the U.S. real GNP time series pertains to the relative importance of difference-stationary and trend-stationary components. Various analytical approaches indicate than an accurate answer is not obtainable with existing data. The paper next considers whether trending series should be differences prior to use in regression analysis and suggests it may not matter greatly if autocorrelated residuals are avoided. Finally, the paper argues that the absence of cointegration among variables does not imply the absence of any practically useful long-run relationship.
{"title":"Unit Roots in Macroeconomic Time Series: Some Critical Issues","authors":"B. Mccallum","doi":"10.3386/W4368","DOIUrl":"https://doi.org/10.3386/W4368","url":null,"abstract":"This paper suggests that the relevant question concerning unit root' in the U.S. real GNP time series pertains to the relative importance of difference-stationary and trend-stationary components. Various analytical approaches indicate than an accurate answer is not obtainable with existing data. The paper next considers whether trending series should be differences prior to use in regression analysis and suggests it may not matter greatly if autocorrelated residuals are avoided. Finally, the paper argues that the absence of cointegration among variables does not imply the absence of any practically useful long-run relationship.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830626","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}
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