Working Paper

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Working Paper: Modeling Persistent Interest Rates with Volatility-Induced Stationarity

We propose a new model for the term structure of interest rates, which embraces the extreme persistence observed in interest rate data. This is achieved by introducing so-called volatility-induced stationarity. We apply the model to U.S. Treasury bond yield data and show that volatility-induced stationarity improves estimation of term premia and forecasting of interest rates compared to existing models.


Working Paper: Seeing Through the Spin: The Effect of News Sentiment on Firms' Stock Market Performance

We show that Stock market investors react only on the objective facts and not the spin in media articles. We use natural language processing tools to compute the tone of 288 thousands articles written by Reuters between 2000 and 2018, and show that it predicts the short-term stock market performance of companies. However, by exploiting a combination of unsupervised machine learning and econometric techniques, we show that this effect is only due to the informational content of the article, and not the framing of that article. The market sees through media spin and can filter informational content from irrelevant tone.


Working Paper: Macroeconomic and financial policies for climate change mitigation: A review of the literature

Climate change is one of the greatest challenges of this century. Mitigation requires a large-scale transition to a low-carbon economy. This paper provides an overview of the rapidly growing literature on the role of macroeconomic and financial policy tools in enabling this transition.


Working Paper: Real Effects of Relaxing Financial Constraints for Homeowners: Evidence from Danish Firms

We study how the introduction of interest-only mortgages in 2003 affected job creation and the skill composition of the workforce over the business cycle. The reform significantly increased household expenditure and firms reacted to this demand shock by creating jobs. These positions, however, are classified as low-skilled occupations, filled by younger and less educated workers who face earlier separations and a higher degree of unemployment ex-post.


Working Paper: The effects of macroprudential policies on house price cycles in an agent-based model of the Danish housing market

In this paper an agent-based model is used to investigate how tightening loan-to-value and loan-to-income ratios affects house price cycles. The use of an agent-based model allows for the analysis of the effects of these policies on heterogeneous households. I find that these policies reduce house price cycles in a non-linear way that depends crucially on the distribution of households and highlights the importance of macroprudential policymakers taking into account the distributions of households.


Working Paper: Multiple credit constraints and time-varying macroeconomic dynamics

I build a DSGE model where households face a loan-to-value (LTV) constraint and a debt-service-to-income (DTI) constraint. From an estimation of the model, I infer when each constraint was binding over the 1975-2017 timespan in the U.S. I also infer that DTI standards were relaxed during the mid-2000s credit boom. In the light of this, the boom could have been avoided by tighter DTI limits, but not by tighter LTV limits. The role of multiple credit constraints for the emergence of nonlinear dynamics is corroborated by county panel data.


Working Paper: A new model for money demand in Denmark: Money demand in a negative interest rate environment

Within a cointegrated VAR framework I show that the traditional money-demand relation can no longer explain the recent development of monetary aggregates in Denmark. Instead, I argue that the introduction of housing wealth and the role of precautionary demand for liquidity improves both the explanatory power of money demand and the stability of the long-run estimates. Finally, I show that the negative interest rate environment has not affected the underlying determination of money demand.


Working Paper: Housing as collateral and home-equity extraction

We study the effect of house price developments on home-equity extraction and household expenditure, exploiting data covering the population of Danish homeowners between 2009 and 2016. Our findings indicate that house price increases affect home-equity extraction – and more so for homeowners close to their borrowing limits. Furthermore, the effect of house prices on expenditure is entirely driven by home-equity extraction. Our results indicate that the mortgage system plays an important role for the transmission of housing wealth increases to the real economy.


Working Paper: Macro-financial linkages in a SVAR model with application to Denmark

We analyse macro-financial linkages in the Danish economy by estimating a structural VAR model. We construct a new financial condition index for the Danish economy. We find that financial conditions stimulated GDP before the financial crisis and deepened the subsequent recession. In recent years, financial conditions have contributed to the expansion in Denmark.


Working Paper: Housing wealth effects and mortgage borrowing

We investigate the co-movement of house prices, home equity extraction and consumption in Denmark. Using survey data we develop a measure for unanticipated house price changes which can be merged on Danish administrative data. Thus, we can show how home owners who experience an unexpected positive house price shock extract home equity and increase spending. We find that the effect is driven by home owners who could potentially benefit from refinancing existing mortgages. This indicates that the wealth effect is intimately connected to the functioning of the mortgage market.


Working Paper: Firm-level Entry and Exit over the Danish Business Cycle

We use micro level registry data to study firm dynamics in Denmark. Similar to findings for the US, young firms are more likely to exit and to grow faster over time but Danish firms also take longer to reach maturity. We do not observe any signs of a slowdown in the entry rate or long-run scarring effects on firms entering in recessions. However, fluctuations in the entry rate have persistent effects on the long-run aggregate volume of value added.


Working Paper: Consistency between household-level consumption data from registers and surveys

We explore the consistency at household-level between register-imputed and survey-based consumption figures for Denmark over the period 2002-15. We find that the marginal propensities to consume out of income estimated on the basis of register data are not significantly different to those estimated on the basis of survey data.


Working Paper: Predicting distresses using deep learning of text segments in annual reports

We develop a probability-of-default model for Danish corporate firms based on deep learning that employs the managements' statements and auditors' reports of the annual reports in addition to the numerical financial variables. Our results show that the text segments provide a statistically significant enhancement of the prediction accuracy compared to models that do not employ the text segments, in particular for large firms. Our results furthermore show that the auditors' reports contain more relevant information than the managements' statements.


Working Paper: Consumption Heterogeneity: Micro Drivers and Macro Implications

This paper aims to test the microfoundations of consumption models and quantify the macro implications of heterogeneity in consumption behavior. We propose a new empirical method to estimate the sensitivity of consumption to permanent and transitory income shocks and apply it to administrative data from Denmark. We find that households who stand to lose from an interest rate hike are more sensitive to income shocks than those who stand to gain. This interest rate exposure channel is potentially more important than the standard intertemporal substitution channel.


Working Paper: Can machine learning models capture correlations in corporate distresses?

We implement a regularly top-performing machine learning model and find that the added complexity in the model does not imply that the model is better at capturing correlation in corporate distresses compared to traditional distress models. Instead, we propose a frailty model, which allows for correlations in distresses. This model demonstrates competitive performance in terms of ranking firms by their riskiness, while providing accurate risk measures of a corporate loan portfolio.