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Monetary Policy and Commercial Lending Rates Nexus: Evidence from Czechia 货币政策与商业贷款利率关系:来自捷克的证据
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-08-01 DOI: 10.2139/ssrn.3683862
Veronika Kajurová, Dagmar Linnertová
{"title":"Monetary Policy and Commercial Lending Rates Nexus: Evidence from Czechia","authors":"Veronika Kajurová, Dagmar Linnertová","doi":"10.2139/ssrn.3683862","DOIUrl":"https://doi.org/10.2139/ssrn.3683862","url":null,"abstract":"The paper investigates the interest rate pass-through in Czechia during different monetary policy stances from April 2005 to February 2020 using structural vector auto-regressive models. The effects of changes in the European money market rate on the Czech money market rate are also observed. We estimate how the changes in monetary policy setting are transmitted to commercial lending rates for both corporate and households. The results show that the responsiveness of Czech money market rate to shocks in the Euro Area money market rate is no longer true. The pass-through from the official policy rate to the money market rate was confirmed; however, the reaction varied during different monetary policy stances. After the zero lower bound period, shocks in money market rate were found to be more important for rates on corporate loans up to 30 mil CZK. The shocks in confidence significantly contributed to the variance in changes of rates on corporate loans up to 30 mil CZK and rates on consumer loans when the monetary policy tightened after the zero lower bound period.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86767387","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 Skills in Experimental Markets: Illusion or Reality? 实验市场的预测技巧:幻觉还是现实?
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-07-08 DOI: 10.2139/ssrn.3645967
Brice Corgnet, Cary A. Deck, M. Desantis, David Porter
{"title":"Forecasting Skills in Experimental Markets: Illusion or Reality?","authors":"Brice Corgnet, Cary A. Deck, M. Desantis, David Porter","doi":"10.2139/ssrn.3645967","DOIUrl":"https://doi.org/10.2139/ssrn.3645967","url":null,"abstract":"There is an ongoing debate regarding the degree to which a forecaster’s ability to draw correct inferences from market signals is real or illusory. This paper attempts to shed light on the debate by examining how personal characteristics do or do not affect forecaster success. Specifically, we investigate the role of fluid intelligence, manipulativeness, and theory of mind on forecast accuracy in experimental asset markets. We find that intelligence improves forecaster performance when market mispricing is low, manipulativeness improves forecaster performance when mispricing is high, and the degree to which theory of mind skills matter depends on both the level of mispricing and how information is displayed. All three of these results are consistent with hypotheses derived from the previous literature. Additionally, we observe that male forecasters outperform female forecasters after controlling for intelligence, manipulativeness, and theory of mind skills as well as risk aversion. Interestingly, we do not find any evidence that forecaster performance improves with experience across markets or within markets. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77918746","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: 2
Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit 媒体关注与情绪作为条件波动预测的驱动因素:英国退欧的应用
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-07-01 DOI: 10.2139/ssrn.3650975
Massimo Guidolin, Manuela Pedio
{"title":"Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit","authors":"Massimo Guidolin, Manuela Pedio","doi":"10.2139/ssrn.3650975","DOIUrl":"https://doi.org/10.2139/ssrn.3650975","url":null,"abstract":"Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models augmented to include media coverage and especially media tone scores outperform traditional GARCH models both in- and out-of-sample.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"257 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73336856","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
Increasing the Information Dynamics of Realized Volatility Forecasts 增加已实现波动率预测的信息动态
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-06-22 DOI: 10.2139/ssrn.3632984
Razvan Pascalau, Ryan Poirier
{"title":"Increasing the Information Dynamics of Realized Volatility Forecasts","authors":"Razvan Pascalau, Ryan Poirier","doi":"10.2139/ssrn.3632984","DOIUrl":"https://doi.org/10.2139/ssrn.3632984","url":null,"abstract":"This paper draws upon several distinct contributions to improve the out-of- sample forecasting performance of realized volatility models. More specifically, we retain the rolling-sample idea of Andreou and Ghysels (2002) to propose a new approach we call the Rolling Realized Volatility (RRV ), which samples consecutive high-frequency squared returns regardless of whether they originate from the same trading session like in the traditional approach. This new approach yields a sample approximately M times larger than the traditional approach, where M is the intraday sampling frequency. The new approach has at least two advantages. First, having more observations increases the informational dynamics of the OLS regression. Second, the Rolling method accounts for the serial correlation between the last returns in day t − 1 and the first returns in day t. We test competing out-of-sample forecast losses from the new approach against those of the traditional method for the S&P 500 and 26 Dow Jones Industrial Average stocks. Using several state-of-the-art realized volatility models, both a simulation and an empirical exercise strongly suggest the Rolling approach yields superior out-of-sample performance over the traditional approach.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81256524","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
Forecast Disagreement about Long-run Macroeconomic Relationships 对长期宏观经济关系的预测分歧
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-05-29 DOI: 10.2139/ssrn.3614426
P. Kuang, Li Tang, Renbin Zhang, Tongbin Zhang
{"title":"Forecast Disagreement about Long-run Macroeconomic Relationships","authors":"P. Kuang, Li Tang, Renbin Zhang, Tongbin Zhang","doi":"10.2139/ssrn.3614426","DOIUrl":"https://doi.org/10.2139/ssrn.3614426","url":null,"abstract":"Using survey forecast data, we study if professional forecasters utilize long-run co-integration relationships among macroeconomic variables to forecast future as postulated in workhorse stochastic growth models. There exists a significant heterogeneity among forecasters, the majority of whom do not use these long-run relationships and generally make more accurate forecasts (comparing with those who use). Simple parsimonious recursive forecasting models are fitted to the data as one way to approximate the expectation formation process of the forecasters who utilize (or do not utilize) the long-run relationships.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84965977","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: 2
Divide and Conquer: Financial Ratios and Industry Returns Predictability 分而治之:财务比率和行业回报可预测性
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-05-25 DOI: 10.2139/ssrn.3136368
Daniele Bianchi, K. McAlinn
{"title":"Divide and Conquer: Financial Ratios and Industry Returns Predictability","authors":"Daniele Bianchi, K. McAlinn","doi":"10.2139/ssrn.3136368","DOIUrl":"https://doi.org/10.2139/ssrn.3136368","url":null,"abstract":"We propose a novel approach for forecasting the equity premium within a data-rich environment based on ensembling small-scale linear models. The economic nature of the predictors is exploited to efficiently retain all of the information available without assuming a priori that some predictor might be irrelevant or easily reducible to a latent factor. Empirically, our results lend strong support for transparent linear predictive models and the use of accounting-based information when forecasting both industry and aggregate stock market excess returns: positive statistical and economic out-of-sample performance compared to sparse predictive regressions, forecast combination strategies and complex non-linear machine learning algorithms.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81639885","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
Do Futures Premiums Predict Commodity Producer Returns? 期货溢价能预测商品生产商的回报吗?
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-05-18 DOI: 10.2139/ssrn.3663412
Qiao Wang, Ronald J. Balvers
{"title":"Do Futures Premiums Predict Commodity Producer Returns?","authors":"Qiao Wang, Ronald J. Balvers","doi":"10.2139/ssrn.3663412","DOIUrl":"https://doi.org/10.2139/ssrn.3663412","url":null,"abstract":"We derive stock returns for firms producing nonrenewable commodities by employing the investment-based asset pricing approach. By identifying the appropriate time-varying discount rate the investment-based approach allows an alternative test of the Hotelling Valuation Principle. The empirical results support the principle and enable predicting returns from sorting firms into quintiles by expected return, producing a 19 percent realized difference between top and bottom quintile. The return differences cannot be explained by standard systematic risk factors, suggesting that at least one important risk factor is missing from standard models. The approach permits cost-of-capital estimation that circumvents identifying systematic risk factors.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91422580","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
Estimation of Split Points in Misspecified Decision Trees 错误决策树中分裂点的估计
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-05-02 DOI: 10.2139/ssrn.3591411
J. Escanciano
{"title":"Estimation of Split Points in Misspecified Decision Trees","authors":"J. Escanciano","doi":"10.2139/ssrn.3591411","DOIUrl":"https://doi.org/10.2139/ssrn.3591411","url":null,"abstract":"We establish rates of convergence for the least squares estimator of the split point in misspecified decision trees. We close the gap between the known superconsistency rate of the correctly specified case and the slow cube-root convergence of the misspecified smooth regression case. When the true regression function is discontinuous at the split point but not constant on both sides, so the simple binary tree model is misspecified, we recover the superconsistency of the least squares split point estimate and the asymptotic normality at parametric rates of the least squares level coefficients. When the regression function is continuous with a kink at the split point, we obtain rates between superconsistency and cube-root asymptotics, depending on the smoothness of the regression function around the split point. The analysis is extended to threshold regressions, where analogous rate results are obtained. In particular, we show that inference on the slope coefficients is robust to misspecification when a certain regression function is discontinuous at the split point. Monte Carlo simulations confirm the theoretical results.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80255481","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
DeltaPy: A Framework for Tabular Data Augmentation in Python DeltaPy: Python中表格数据增强的框架
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-04-22 DOI: 10.2139/ssrn.3582219
Derek Snow
{"title":"DeltaPy: A Framework for Tabular Data Augmentation in Python","authors":"Derek Snow","doi":"10.2139/ssrn.3582219","DOIUrl":"https://doi.org/10.2139/ssrn.3582219","url":null,"abstract":"A range of data abstractions have come to the fore since the re-emergence of machine learning. This includes procedures like feature engineering, extraction, transformation, and selection, as well as data pre-processing, generation, synthesisation, and augmentation. This report attempts to unify some of this terminology with the development of a bare-bones Python package, DeltaPy.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73154483","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
Machine Learning and Return Predictability Across Firms, Time and Portfolios 机器学习与跨公司、跨时间、跨投资组合的回报可预测性
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-03-25 DOI: 10.2139/ssrn.3696533
Fahiz Baba Yara
{"title":"Machine Learning and Return Predictability Across Firms, Time and Portfolios","authors":"Fahiz Baba Yara","doi":"10.2139/ssrn.3696533","DOIUrl":"https://doi.org/10.2139/ssrn.3696533","url":null,"abstract":"Previous research finds that machine learning methods predict short-term return variation in the cross-section of stocks, even when these methods do not impose strict economic restrictions. However, without such restrictions, the models' predictions fail to generalize in a number of important ways, such as predicting time-series variation in returns to the market portfolio and long-short characteristic sorted portfolios. I show that this shortfall can be remedied by imposing restrictions, that reflect findings in the financial economics literature, in the architectural design of a neural network model and provide recommendations for using machine learning methods in asset pricing. Additionally, I study return predictability over multiple future horizons, thus shedding light on the dynamics of intermediate and long-run conditional expected returns.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88395121","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
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