Federal Reserve Bank of San Francisco, Working Paper Series最新文献_第9页

Federal Reserve Bank of San Francisco, Working Paper Series Pub Date : 2019-02-28 DOI: 10.24148/WP2019-06

P. Giordani, Simon H. Kwan

{"title":"Tracking Financial Fragility","authors":"P. Giordani, Simon H. Kwan","doi":"10.24148/WP2019-06","DOIUrl":"https://doi.org/10.24148/WP2019-06","url":null,"abstract":"In constructing an indicator of financial fragility, the choice of which filter (or transformation) to apply to the data series that appear to trend in sample is often considered a technicality, but in fact turns out to matter a great deal. The fundamental assumption about the likely nature of observed trends in the data, for example, the ratio of credit to GDP, has direct effects on the measured gap or vulnerability. We discuss shortcomings of the most common filters used in the literature and policy circle, and propose a fairly simple and intuitive alternative - the local level filter. To the extent that validation will always be a challenge when the number of observed financial crises (in the US) is small, we conduct a simulation exercise to make the case. We also conduct a cross country analysis to show how qualitatively different the estimated credit gaps were as of 2017, and hence their policy implications in 29 countries. Finally, we construct an indicator of financial fragility for the US economy based on the view that systemic fragility stems mainly from high level of debts (among households and corporations) associated with high valuations for collateral assets (real estate, stocks). An indicator based on the local level filter signals elevated financial fragility in the US financial system currently, whereas the HP filter and the ten-year moving average provide much more benign readings.","PeriodicalId":250744,"journal":{"name":"Federal Reserve Bank of San Francisco, Working Paper Series","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117049116","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

Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives Using Text Analysis 《言出必行:用文本分析估计央行目标的新方法》

Federal Reserve Bank of San Francisco, Working Paper Series Pub Date : 2019-01-18 DOI: 10.24148/wp2019-02

A. Shapiro, Daniel J. Wilson

引用次数: 61

Inflation Globally 全球通货膨胀

Federal Reserve Bank of San Francisco, Working Paper Series Pub Date : 2018-12-07 DOI: 10.24148/wp2018-15

Ò. Jordà, Fernanda Nechio

引用次数: 10

"Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?" “检查超额收益可预测性的来源:随机波动还是市场无效?”

Federal Reserve Bank of San Francisco, Working Paper Series Pub Date : 2018-12-03 DOI: 10.24148/WP2018-14

Kevin J. Lansing, Stephen F. LeRoy, Jun Ma

{"title":"\"Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?\"","authors":"Kevin J. Lansing, Stephen F. LeRoy, Jun Ma","doi":"10.24148/WP2018-14","DOIUrl":"https://doi.org/10.24148/WP2018-14","url":null,"abstract":"We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of model variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. From an empirical perspective, we investigate whether 1-month ahead excess returns on stocks can be predicted using measures of consumer sentiment and excess return momentum, while controlling directly and indirectly for the presence of stochastic volatility. A variable that interacts the 12-month sentiment change with recent return momentum is a robust predictor of excess stock returns both in-sample and out-of-sample. The predictive power of this variable derives mainly from periods when sentiment has been declining and return momentum is negative, forecasting a further decline in the excess stock return. We show that the sentiment-momentum variable is positively correlated with fluctuations in Google searches for the term ?stock market,? suggesting that the sentiment-momentum variable helps to predict excess returns because it captures shifts in investor attention, particularly during stock market declines.","PeriodicalId":250744,"journal":{"name":"Federal Reserve Bank of San Francisco, Working Paper Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124454668","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: 8

Efficiency in Sequential Labor and Goods Markets 顺序劳动和商品市场的效率

Federal Reserve Bank of San Francisco, Working Paper Series Pub Date : 2018-09-01 DOI: 10.24148/wp2018-13

Étienne Wasmer, Nicolas Petrosky-Nadeau, P. Weil

引用次数: 1