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.
{"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}
There is an extensive literature that studies optimal monetary policy with an assumed central bank loss function, yet there has been very little study of what central bank preferences are in practice. We directly estimate the Federal Open Market Committee's (FOMC) loss function, including the implicit inflation target, from the tone of the language used in FOMC transcripts, minutes, and members' speeches. Direct estimation is advantageous because it requires no knowledge of the underlying macroeconomic structure nor observation of central bank actions. We fi nd that the FOMC had an implicit inflation target of approximately 1 1/2 percent on average over our baseline 2000-2012 sample period. We also find that the FOMC's loss depends strongly on output growth and stock market performance and less so on their perception of current slack.
{"title":"Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives Using Text Analysis","authors":"A. Shapiro, Daniel J. Wilson","doi":"10.24148/wp2019-02","DOIUrl":"https://doi.org/10.24148/wp2019-02","url":null,"abstract":"There is an extensive literature that studies optimal monetary policy with an assumed central bank loss function, yet there has been very little study of what central bank preferences are in practice. We directly estimate the Federal Open Market Committee's (FOMC) loss function, including the implicit inflation target, from the tone of the language used in FOMC transcripts, minutes, and members' speeches. Direct estimation is advantageous because it requires no knowledge of the underlying macroeconomic structure nor observation of central bank actions. We fi nd that the FOMC had an implicit inflation target of approximately 1 1/2 percent on average over our baseline 2000-2012 sample period. We also find that the FOMC's loss depends strongly on output growth and stock market performance and less so on their perception of current slack.","PeriodicalId":250744,"journal":{"name":"Federal Reserve Bank of San Francisco, Working Paper Series","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123997055","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}
{"title":"Inflation Globally","authors":"Ò. Jordà, Fernanda Nechio","doi":"10.24148/wp2018-15","DOIUrl":"https://doi.org/10.24148/wp2018-15","url":null,"abstract":"","PeriodicalId":250744,"journal":{"name":"Federal Reserve Bank of San Francisco, Working Paper Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130205606","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 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.
{"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}
This paper studies the optimal sharing of value added between consumers, producers, and labor. We first define a constrained optimum. We then compare it with the decentralized allocation. They coincide when the price maximizes the expected marginal revenue of the firm in the goods market, an outcome of the competitive search equilibrium, and when the wage exactly offsets the congestion externality of firm entry in the labor market, which is the traditional Hosios condition. Under price and wage bargaining, this allocation is achieved under a double Hosios condition combining the logic of competitive search and Hosios efficiency. The consumer receives a share of the goodsmarket trading surplus equal to the amount of externality occasioned by its search activity and the worker receives a share of the labor match surplus to offset the externality of firm entry in the matching process. A calibration of the model to the US economy indicates that the labor market is near efficient, and free-entry of consumers leads to excess excess consumer market power in setting prices. Restoring efficiency leads to a modest change in welfare.
{"title":"Efficiency in Sequential Labor and Goods Markets","authors":"Étienne Wasmer, Nicolas Petrosky-Nadeau, P. Weil","doi":"10.24148/wp2018-13","DOIUrl":"https://doi.org/10.24148/wp2018-13","url":null,"abstract":"This paper studies the optimal sharing of value added between consumers, producers, and labor. We first define a constrained optimum. We then compare it with the decentralized allocation. They coincide when the price maximizes the expected marginal revenue of the firm in the goods market, an outcome of the competitive search equilibrium, and when the wage exactly offsets the congestion externality of firm entry in the labor market, which is the traditional Hosios condition. Under price and wage bargaining, this allocation is achieved under a double Hosios condition combining the logic of competitive search and Hosios efficiency. The consumer receives a share of the goodsmarket trading surplus equal to the amount of externality occasioned by its search activity and the worker receives a share of the labor match surplus to offset the externality of firm entry in the matching process. A calibration of the model to the US economy indicates that the labor market is near efficient, and free-entry of consumers leads to excess excess consumer market power in setting prices. Restoring efficiency leads to a modest change in welfare.","PeriodicalId":250744,"journal":{"name":"Federal Reserve Bank of San Francisco, Working Paper Series","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132552869","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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