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An Event Study of Chinese Tourists to Taiwan 中国大陆赴台游客事件研究
ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-01-11 DOI: 10.2139/ssrn.3100211
Chia‐Lin Chang, Shu-Han Hsu, M. McAleer
{"title":"An Event Study of Chinese Tourists to Taiwan","authors":"Chia‐Lin Chang, Shu-Han Hsu, M. McAleer","doi":"10.2139/ssrn.3100211","DOIUrl":"https://doi.org/10.2139/ssrn.3100211","url":null,"abstract":"The number of Chinese tourists visiting Taiwan has been closely related to the political relationship across the Taiwan Strait. The occurrence of political events and disasters or accidents have had, and will continue to have, a huge impact on the Taiwan tourism market. To date, there has been relatively little empirical research conducted on this issue. In this paper, tourists are characterized as being involved in one of three types of tourism: group tourism (group-type), individual tourism (individualtype), and medical cosmetology (medical-type). We use McAleer’s (2015) fundamental equation in tourism finance to examine the correlation that exists between the rate of change in the number of tourists and the rate of return on tourism. Second, we use the event study method to observe whether the numbers of tourists have changed abnormally before and after the occurrence of major events on both sides of the Strait. Three different types of conditional variance models, namely, GARCH (1,1), GJR (1,1) and EGARCH (1,1), are used to estimate the abnormal rate of change in the number of tourists. The empirical results concerning the major events affecting the changes in the numbers of tourists from China to Taiwan are economically significant, and confirm which types of tourists are most likely to be affected by such major events.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129423514","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: 1
Asset Growth and Stock Market Returns: A Time-Series Analysis 资产增长与股票市场收益:一个时间序列分析
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-10-01 DOI: 10.2139/ssrn.2420467
Quan Wen
{"title":"Asset Growth and Stock Market Returns: A Time-Series Analysis","authors":"Quan Wen","doi":"10.2139/ssrn.2420467","DOIUrl":"https://doi.org/10.2139/ssrn.2420467","url":null,"abstract":"I examine whether the firm-level total asset growth effect in Cooper, Gulen, and Schill (2008) extends to the aggregate stock market. I find that aggregate asset growth negatively predicts future market returns both in and out-of-sample and this result is robust across G7 countries. I further show that aggregate asset growth contains information about future market returns not captured by traditional macroeconomic variables and other measures of investment or growth. The forecasting ability of asset growth is strongly correlated with its propensity to predict equity issuance timing and growth in cash savings, earnings surprise, and systematic errors in investors' expectations.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132655822","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: 17
Robust Filtering for Financial Time Series 金融时间序列的鲁棒滤波
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-09-21 DOI: 10.2139/ssrn.3141554
Constantin Lisson
{"title":"Robust Filtering for Financial Time Series","authors":"Constantin Lisson","doi":"10.2139/ssrn.3141554","DOIUrl":"https://doi.org/10.2139/ssrn.3141554","url":null,"abstract":"The sequential inference regarding the state of a system based on new observations is commonly known as the filtering problem. The present work discusses and implements the basic particle filter algorithm of Gordon, Salmond, and Smith (1993) and its robustified counterpart developed by Calvet, Czellar, and Ronchetti (2015). I test the algorithm in simulations and an empirical setting and show that the robustified particle filter performs better than its non-robust counterpart both from a statistical perspective and in terms of numerical stability. I discuss a model for contamination by replacement outliers and show that the performance of the robust particle filter is superior to that of the standard filter in simulations. I apply the algorithm to a time series of daily natural gas futures trading volumes and conclude by highlighting how some existing financial industry applications may benefit from robustification of the model observation density.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133443284","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
Forecasting Using Random Subspace Methods 基于随机子空间方法的预测
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-08-11 DOI: 10.2139/ssrn.2835293
Tom Boot, D. Nibbering
{"title":"Forecasting Using Random Subspace Methods","authors":"Tom Boot, D. Nibbering","doi":"10.2139/ssrn.2835293","DOIUrl":"https://doi.org/10.2139/ssrn.2835293","url":null,"abstract":"Random subspace methods are a new approach to obtain accurate forecasts in high-dimensional regression settings. Forecasts are constructed by averaging over forecasts from many submodels generated by random selection or random Gaussian weighting of predictors. This paper derives upper bounds on the asymptotic mean squared forecast error of these strategies, which show that the methods are particularly suitable for macroeconomic forecasting. An empirical application to the FRED-MD data confirms the theoretical findings, and shows random subspace methods to outperform competing methods on key macroeconomic indicators.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"6 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128224056","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: 18
Dynamic Semiparametric Models for Expected Shortfall (and Value-At-Risk) 预期缺口(和风险价值)的动态半参数模型
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-07-11 DOI: 10.2139/ssrn.3000465
Andrew J. Patton, Johanna F. Ziegel, Rui Chen
{"title":"Dynamic Semiparametric Models for Expected Shortfall (and Value-At-Risk)","authors":"Andrew J. Patton, Johanna F. Ziegel, Rui Chen","doi":"10.2139/ssrn.3000465","DOIUrl":"https://doi.org/10.2139/ssrn.3000465","url":null,"abstract":"Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR). The Basel III Accord, which will be implemented in the years leading up to 2019, places new attention on ES, but unlike VaR, there is little existing work on modeling ES. We use recent results from statistical decision theory to overcome the problem of \"elicitability\" for ES by jointly modelling ES and VaR, and propose new dynamic models for these risk measures. We provide estimation and inference methods for the proposed models, and confirm via simulation studies that the methods have good finite-sample properties. We apply these models to daily returns on four international equity indices, and find the proposed new ES-VaR models outperform forecasts based on GARCH or rolling window models.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126198543","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: 160
Regional Tourism Demand Forecasting with Machine Learning Models: Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting 基于机器学习模型的区域旅游需求预测:多输入多输出环境下的高斯过程回归与神经网络模型
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-07-06 DOI: 10.2139/ssrn.2945556
Oscar Claveria, E. Monte, Salvador Torra
{"title":"Regional Tourism Demand Forecasting with Machine Learning Models: Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting","authors":"Oscar Claveria, E. Monte, Salvador Torra","doi":"10.2139/ssrn.2945556","DOIUrl":"https://doi.org/10.2139/ssrn.2945556","url":null,"abstract":"This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131829100","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: 5
Regression Discontinuity in Time: Considerations for Empirical Applications 时间上的回归不连续:经验应用的考虑
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-07-01 DOI: 10.3386/W23602
Catherine Hausman, D. Rapson
{"title":"Regression Discontinuity in Time: Considerations for Empirical Applications","authors":"Catherine Hausman, D. Rapson","doi":"10.3386/W23602","DOIUrl":"https://doi.org/10.3386/W23602","url":null,"abstract":"Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners, we discuss several features of this regression discontinuity in time framework that differ from the more standard cross-sectional RD framework. First, many applications (particularly in environmental economics) lack cross-sectional variation and are estimated using observations far from the temporal threshold. This common empirical practice is hard to square with the assumptions of a cross-sectional RD, which is conceptualized for an estimation bandwidth shrinking even as the sample size increases. Second, estimates may be biased if the time-series properties of the data are ignored (for instance, in the presence of an autoregressive process), or more generally if short-run and long-run effects differ. Finally, tests for sorting or bunching near the threshold are often irrelevant, making the framework closer to an event study than a regression discontinuity design. Based on these features and motivated by hypothetical examples using air quality data, we offer suggestions for the empirical researcher wishing to use the RD in time framework.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994024","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: 290
Dynamic Properties of the Bitcoin and the US Market 比特币与美国市场的动态特性
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-05-11 DOI: 10.2139/ssrn.2966998
S. Stavroyiannis, Vassilios Babalos
{"title":"Dynamic Properties of the Bitcoin and the US Market","authors":"S. Stavroyiannis, Vassilios Babalos","doi":"10.2139/ssrn.2966998","DOIUrl":"https://doi.org/10.2139/ssrn.2966998","url":null,"abstract":"This paper examines the dynamic properties of Bitcoin and the Standard and Poor’s SP500 index, using a variety of econometric approaches, including univariate and multivariate GARCH models, and vector autoregressive specifications. Moreover, we explore whether Bitcoin can be classified as a possible hedge, diversifier, or safehaven with respect to the US market, and if it possesses any of the attributes Gold has. Our results indicate that Bitcoin does not actually hold any of the hedge, diversifier, or safe-haven properties; rather, it exhibits intrinsic attributes not related to US market developments.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006405","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: 37
Cross‐Sectional and Time Series Momentum Returns and Market States 横截面和时间序列动量回报与市场状态
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-05-03 DOI: 10.1111/irfi.12148
Muhammad A. Cheema, G. Nartea, Yimei Man
{"title":"Cross‐Sectional and Time Series Momentum Returns and Market States","authors":"Muhammad A. Cheema, G. Nartea, Yimei Man","doi":"10.1111/irfi.12148","DOIUrl":"https://doi.org/10.1111/irfi.12148","url":null,"abstract":"Recent evidence on momentum returns shows that the time-series (TS) strategy outperforms the cross-sectional (CS) strategy. We present new evidence that this happens only when the market continues in the same state, UP or DOWN. In fact, we find that the TS strategy underperforms the CS strategy when the market transitions to a different state. Our results also show that the difference in momentum returns between TS and CS strategies is related to both the net long and net short positions of the TS strategy.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"337 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124732993","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: 17
NPLs, Moral Hazards, and Bond Markets 不良贷款、道德风险和债券市场
ERN: Time-Series Models (Single) (Topic) Pub Date : 2017-05-01 DOI: 10.2139/ssrn.2961139
Alan T. Wang
{"title":"NPLs, Moral Hazards, and Bond Markets","authors":"Alan T. Wang","doi":"10.2139/ssrn.2961139","DOIUrl":"https://doi.org/10.2139/ssrn.2961139","url":null,"abstract":"This paper uses the FDIC time series data to examine the interrelationship between nonperforming loans (NPLs), loan growth, and high yield bond spread. We find that loan growth leads to higher NPL ratio (moral hazard hypothesis) when the economy is in recessions, banks have more liquidity, banks are less capitalized, or banks have more risk-weighted assets. An increase in high yield bond spread is spontaneously coupled with a temporary increase in commercial and industrial loans initially (bond-loan substitution effect), and then followed by decreases in loan growth subsequently for general loans (default information effect). The major implication is that high yield bond spread contains very important information for future NPLs, and aggregate loan loss provisions have not incorporated this important information enough possibly due to the GAAP guidelines. This suggests a risk-shifting from shareholders to insurers for the US financial institutions, and highlights the importance of expected loan loss approach to loan loss provisioning.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134462877","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
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