发布求助
文献互助 智能选刊 最新文献

Econometric Modeling: Capital Markets - Forecasting eJournal最新文献

筛选
英文 中文
Contagion and Return Predictability in Asset Markets: An Experiment With Two Lucas Trees 资产市场的传染与回报可预测性:两棵卢卡斯树的实验
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-03-01 DOI: 10.2139/ssrn.3445324
C. Noussair, Andreea Popescu
{"title":"Contagion and Return Predictability in Asset Markets: An Experiment With Two Lucas Trees","authors":"C. Noussair, Andreea Popescu","doi":"10.2139/ssrn.3445324","DOIUrl":"https://doi.org/10.2139/ssrn.3445324","url":null,"abstract":"Abstract Using a laboratory experiment, we investigate whether comovement can emerge between two risky assets, despite their fundamentals not being correlated. The ‘Two trees’ asset pricing model developed by Cochrane et al. (2007) guides our experimental design and its predictions serve as our source of hypotheses. The model makes time-series and cross-section return predictions following a shock to one of the two assets’ dividend distributions. As the model predicts, we observe (1) positive contemporaneous correlation between the two assets, (2) positive autocorrelation in the shocked asset, and (3) time-series and cross-sectional return predictability from the dividend-price ratio. In line with the rational foundations of the model, the model's predictions have stronger support in markets with relatively sophisticated agents.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88245209","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: 6
Macro vs. Micro Earnings, 'Macro-Earnings Negativity', and an Introduction to a Composite Valuation Model 宏观与微观收益，“宏观收益负性”，以及综合估值模型简介
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-02-13 DOI: 10.2139/ssrn.2222008
S. Jones
{"title":"Macro vs. Micro Earnings, 'Macro-Earnings Negativity', and an Introduction to a Composite Valuation Model","authors":"S. Jones","doi":"10.2139/ssrn.2222008","DOIUrl":"https://doi.org/10.2139/ssrn.2222008","url":null,"abstract":"Earnings of the overall market are typically viewed in the same perspective as earnings of individual companies. Conflicts between these perceptions are revealed with the use of Kalecki’s profit function to reveal the identification of negative characteristics with macro earnings, introduce the concept of “macro-earnings negativity”, and demonstrate the theoretical and statistical superiority of MV/GDP valuation measure versus earnings-based measures. Based on the MV/GDP metric, a multi-variable forecasting model is developed which utilizes both new and prior-researched variables, the most effective of which is a demographic measure. The resulting composite model is statistically superior to popular metrics, and, relative to popular benchmarks, forecasts considerably lower returns for the coming decade.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86212151","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
Empirical Analysis and Forecasting of Multiple Yield Curves 多重收益率曲线的实证分析与预测
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-01-08 DOI: 10.2139/ssrn.3311998
Christoph Gerhart, E. Lütkebohmert
{"title":"Empirical Analysis and Forecasting of Multiple Yield Curves","authors":"Christoph Gerhart, E. Lütkebohmert","doi":"10.2139/ssrn.3311998","DOIUrl":"https://doi.org/10.2139/ssrn.3311998","url":null,"abstract":"Abstract In this paper we perform a thorough empirical study of tenor-dependent term structures which reveals important cross-tenor dependencies of yields as a persistent feature of post-crisis interest rate markets. Based on this analysis, we develop tractable dynamic factor models to forecast multiple yield curves. We show that our method outperforms existing single-curve forecasting methods by taking into account the connections between rates of different tenor structures. Our results have important implications e.g. for risk management in finance and insurance as the disregard of tenor dependencies may lead to an underestimation of risks.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77507174","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: 6
Does Economic Policy Uncertainty Affect Analyst Forecast Accuracy? 经济政策的不确定性会影响分析师预测的准确性吗?
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-01-01 DOI: 10.2139/ssrn.3407668
Rumpa Biswas
{"title":"Does Economic Policy Uncertainty Affect Analyst Forecast Accuracy?","authors":"Rumpa Biswas","doi":"10.2139/ssrn.3407668","DOIUrl":"https://doi.org/10.2139/ssrn.3407668","url":null,"abstract":"I investigate the dynamics of analyst forecast errors relative to economic policy uncertainty and find a significant positive relation between economic policy uncertainty and analyst forecast errors. A doubling of economic policy uncertainty is associated with a 4.29 percentage points increase in earnings (EPS) forecast errors, and the volatility and dispersion in analyst forecast errors are positively related to the economic policy uncertainty. Earnings forecast errors are higher for firms with higher sensitivity to the economic policy uncertainty, and the uncertainty factor retains its significance when compared to other risk factors. Additionally, firms with higher idiosyncratic risks show a higher sensitivity to the economic policy uncertainty.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80561328","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: 4
CCR KVA Relief Through CVA: A Regression-Based Monte Carlo Approach 通过CVA缓解CCR KVA:一个基于回归的蒙特卡罗方法
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2019-01-01 DOI: 10.2139/ssrn.3127856
Christoph M. Puetter, Stefano Renzitti, Allan Cowan
{"title":"CCR KVA Relief Through CVA: A Regression-Based Monte Carlo Approach","authors":"Christoph M. Puetter, Stefano Renzitti, Allan Cowan","doi":"10.2139/ssrn.3127856","DOIUrl":"https://doi.org/10.2139/ssrn.3127856","url":null,"abstract":"We present and examine, by example of a USD interest rate swap and a EUR/USD cross-currency basis swap, a regression-based Monte Carlo approach to counterparty credit default risk (CCR) capital and CCR capital valuation adjustment (KVA) calculations [assuming the standardized approach to counterparty credit risk for exposure-at-default (SA-CCR EAD) and the internal ratings-based (IRB) approach for CCR risk weights]. This approach allows to incorporate the capital lowering effect of credit valuation adjustment (CVA) in an efficient manner, without having to resort to lengthy nested Monte Carlo simulations. We find that the regression-based Monte Carlo approach works well in most situations. In other situations, the accuracy of the approach is sensitively controlled by the choice of explanatory variables. We discuss in detail the conditions and underlying dynamics under which this happens. In computing and presenting a selection of numerical examples, we also explore the impact of dynamic CCR risk weights on CCR KVA, and compare regression-based CCR KVA results with CCR KVA results from nested Monte Carlo, alternative frequently used CCR KVA simplifications, and standardized CVA KVA.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75997789","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
A Bootstrap-Based KPSS Test for Functional Time Series 基于bootstrap的功能时间序列KPSS检验
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-11-23 DOI: 10.2139/ssrn.3289445
Yichao Chen, Chi Seng Pun
{"title":"A Bootstrap-Based KPSS Test for Functional Time Series","authors":"Yichao Chen, Chi Seng Pun","doi":"10.2139/ssrn.3289445","DOIUrl":"https://doi.org/10.2139/ssrn.3289445","url":null,"abstract":"In this study, we examine bootstrap methods to construct a generalized KPSS test for functional time series. Bootstrap-based functional testing provides an intuitive and efficient estimation of the distribution of the generalized KPSS test statistic and is capable of achieving non-trivial powers against many alternative hypotheses. We derive the asymptotic distribution of the simple bootstrap-based KPSS test statistic for functional time series, which proves the bootstrap validity on average. Simulation studies are then conducted to examine the performance of the proposed KPSS tests in small and moderate sample sizes. The results demonstrate that the bootstrap-based functional KPSS test has good empirical size and power. Moreover, its implementation is more efficient than the existing KPSS test for functional time series.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"116 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79208752","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
Can Google Trends Actually Improve Housing Market Forecasts? 谷歌趋势真的能改善房地产市场预测吗?
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-11-11 DOI: 10.2139/ssrn.2886705
Christopher Limnios, Hao You
{"title":"Can Google Trends Actually Improve Housing Market Forecasts?","authors":"Christopher Limnios, Hao You","doi":"10.2139/ssrn.2886705","DOIUrl":"https://doi.org/10.2139/ssrn.2886705","url":null,"abstract":"We augment linear pricing models for the housing market commonly used in the literature with google trends data in order to assess whether or not crowd-sourced search query data can improve the forecasting ability of the models. We compare various performance measures of the augmented linear model's out-of-sample, one-step ahead, dynamic forecasts against a baseline version. We find that augmenting the models to take advantage of the availability of Google trend data does not improve the forecasting performance of the models.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86367727","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: 7
The Term Structure of Systematic and Idiosyncratic Risk 系统风险和特殊风险的期限结构
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-11-06 DOI: 10.2139/ssrn.3069519
Fabian Hollstein, Marcel Prokopczuk, Chardin Wese Simen
{"title":"The Term Structure of Systematic and Idiosyncratic Risk","authors":"Fabian Hollstein, Marcel Prokopczuk, Chardin Wese Simen","doi":"10.2139/ssrn.3069519","DOIUrl":"https://doi.org/10.2139/ssrn.3069519","url":null,"abstract":"We study the term structure of variance (total risk), systematic, and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little indication of a time‐varying term premium. Turning the focus to individual stocks, we cannot reject the expectations hypothesis for systematic variance, but we strongly reject it for idiosyncratic variance. Our results are robust to jumps and potential statistical biases.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76902961","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: 3
Does the Stock Market Predict Macro-Variables? 股市能预测宏观变量吗?
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-10-19 DOI: 10.2139/ssrn.3269862
D. McMillan
{"title":"Does the Stock Market Predict Macro-Variables?","authors":"D. McMillan","doi":"10.2139/ssrn.3269862","DOIUrl":"https://doi.org/10.2139/ssrn.3269862","url":null,"abstract":"Movements in the stock market should reflect expectations regarding future economic conditions and lead the macroeconomy. However, evidence for stock returns providing such predictive power is mixed. We argue this arises as stock returns are noisy and consider the predictive ability of derived expected returns, as well as, the price-earnings ratio, VIX and the stock/bond return correlation. Results reveal that expected stock returns and the stock/bond return correlation exhibit predictive power for output and consumption growth and inflation at monthly and quarterly frequencies. The VIX has predictive power at the monthly frequency for consumption growth and inflation, while the price-earnings ratio predicts the shape of the future term structure. Results reveal that higher current expected returns are consistent with to higher future output and consumption growth, while greater risk results in lower future economic activity. The results are robust to considerations of structural breaks and alternative variables. Further, expected returns provides a noticeably more accurate recession prediction than realised returns. Thus, while stock returns are a weak predictor, expected returns and alternative proxies for stock market risk do reveal predictive power. Such information provides a leading role indicator for the macroeconomy and reveals links between financial markets and the economy.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82375787","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
Redundant Information and Predictable Intraday Returns 冗余信息和可预测的日内回报
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-10-02 DOI: 10.2139/ssrn.3283919
Michael P Carniol
{"title":"Redundant Information and Predictable Intraday Returns","authors":"Michael P Carniol","doi":"10.2139/ssrn.3283919","DOIUrl":"https://doi.org/10.2139/ssrn.3283919","url":null,"abstract":"This paper examines how well investors distinguish between genuinely novel private information and information that already is priced (labeled \"redundant information\"). We derive a structural model of stock price returns that identifies investors’ non-Bayesian weighting of redundant information distinctly from information asymmetry, transaction costs, and serially correlated liquidity trader demand. We estimate this model using five-minute, 12-minute, and 30-minute returns and find that, on average, investors behave as if over 47 percent of the information content in the immediately prior price change is private information. These results suggest an information-processing mechanism that drives momentum and mean reversion in intraday returns.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76732056","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信