Working Papers
Working Papers
Joint with M.Mastrogiacomo and M. Mangan. Here.
This paper investigates how changes in credit availability influence house prices. We show that increases in household credit triggered by a relaxation of lending standards lead to higher transaction prices, higher shares and amounts of overbidding transactions and lower property sale times in the housing market. The impact on prices increases throughout the housing boom due to a higher take-up of credit by households. Also, it is stronger in locations with tighter housing supply and lower affordability, among liquidity constrained but credit-unconstrained buyers, as well as for more expensive properties. The findings support the credit-driven household demand hypothesis and highlight that mac roprudential policy contains systemic risk not only by reducing household leverage, but also by curbing house price growth over the cycle.
Here.
This paper quantifies the impact of a tightening of the macro-prudential Loan-to-Value (LTV) limit on the distribution of household leverage, as expressed by the LTV ratio. Using a non-parametric approach, I disentangle shifts in the LTV distributions induced by the tightening from those due to housing market developments. The findings show that, while still not imposing any down-payment constraint, a progressive tightening of the LTV limit from 106% to 100% increased the number of constrained borrowers of nearly 50%,thus containing systemic risk by curbing household leverage throughout a housing boom.
Joint with K. van Ginkel and D.J. Jansen. DNB Working Paper n. 796.
We study whether floods can affect financial stability through a credit risk channel. Our focus is onthe Netherlands, a country situated partly below sea level, where insurance policies exclude property damages caused by some types of floods. Using geocoded data for close to EUR 650 billion in real estate exposures, we consider possible implications of such floods for bank capital. For a set of 38 adverse scenarios, we estimate that flood-related property damages lead to capital declines that mostly range between 30 and 50 basis points. We highlight how starting-point loan-to-value ratios are one important driver of capital impacts. Our estimates focus on property damages as the main transmission channel and are also subject to a number of assumptions. If climate change continues, more frequent floods or flood-related macrofinancial disruptions may have stronger implications for financial stability than our estimates so far indicate.
Joint with I.Simonetti and M.Mastrogiacomo. DOI: https://doi.org/10.1017/S1474747225000034
Using household survey data linked to supervisory data of Dutch pension funds, we provide evidence of the increase in household savings caused by shocks to the financial position of pension funds. Our identification strategy exploits cross-sectional and time variations in pension funds’ funding ratios, which result from asset allocations and price corrections outside the control of fund members. The findings reveal that fluctuations in funding ratios significantly impact household savings, with a displacement effect above 40 percent. Lower funding ratios are associated with higher voluntary savings, driven primarily by members of pension funds with lower historical returns. Unlike earlier studies, this paper covers a long time span including three major economic crises, providing novel insights into the interaction between pension fund stability and individual saving behaviour.
DOI: https://doi.org/10.1016/j.jimonfin.2024.103051
This paper quantifies the credit-driven housing demand and the role of macro-prudential Loan-to-Income (LTI) and Loan-to-Value (LTV) limits. Using granular and time-varying changes in borrowing capacity, I estimate how shocks in credit availability feed into credit demand and affect household debt. The findings indicate a robust relationship between debt and borrowing capacity that amplifies throughout a housing boom. The relationship is heterogeneous, as changes in borrowing capacity have larger effects among low-income and first-time buyers and in expensive property markets. If no downpayment is required, tightening the LTV limit may not contain borrowing capacity but still curbs leverage among highly-indebted borrowers.
Joint with J.Parlevliet and M.Mastrogiacomo. DOI: https://doi.org/10.1016/j.jmacro.2023.103521
This paper investigates the employment and wage effects of contract staggering, i.e., the asynchronous and infrequent way in which wages adjust to changes in the economic environment. Using an identification strategy based on exogenous start dates of collective agreements around the Great Recession, we estimate the effect of increases in base wages on firms’ labor cost adjustments. Our analysis is based on a large employers-employees dataset merged to collective agreements in the Netherlands, a country in which collective bargaining is dominant and contract staggering is relatively pervasive. The main result is that staggered wage setting has no real effect on employment. We find significant employment losses only in sectors covered by contracts with much longer durations than those normally assumed in macroeconomic models featuring staggered wages. Instead, we show that firms adjust labor costs by curbing other pay components such as bonuses and benefits and incidental pay. The overall result supports the idea that wage rigidities are not the main source of employment fluctuations.
Joint with M.Mastrogiacomo. DOI: https://doi.org/10.1111/1540-6229.12383
This paper tests whether disregarding home improvements biases the housing wealth effect, that is, the marginal propensity to consume out of housing wealth. We decompose housing wealth changes in their unanticipated and exogenous component by filtering out previously elicited expectations of house prices and by dealing with endogenous home improvements. Results show that the bias is zero due to the zero correlation between home investments and changes in house values. Results are consistent with models with exogenous maintenance and with the evidence that maintenance contrasts depreciation and is mostly value-preserving. A comparative empirical approach excludes that results are only internally valid.
Joint with A.Cipollini and S.Muzzioli. DOI: j.eneco.2019.104536
This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market, the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GFEVD. We show that the net spillover indices used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GFEVD. Based on a data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the GFEVD is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.
Joint with A.Cipollini and S.Muzzioli. DOI: j.irfa.2018.03.001
This paper quantifies the strength and direction of semi-volatility spillovers between five stock markets over the 2000–2016 period. We use upside and downside semi-volatilities as proxies for downside risks and upside opportunities. In this way, we complement the literature which has focused mainly on the contemporaneous correlation between positive and negative returns, with the evidence of asymmetry also in semi-volatility transmission. For this purpose, we apply the Diebold and Yilmaz (2012) methodology, based on a generalized forecast error variance decomposition, to downside and upside realized semi-volatility series. While the analysis of Diebold and Yilmaz (2012) is based on a stationary VAR, we take into account the long-memory behaviour of the series, by using the multivariate extension of the HAR model (named VHAR model). Moreover, we cast light on how the choice of the normalization scheme can bias the net-spillover computation in a full sample as well as in a rolling sample analysis.
Joint with R. van der Molen and R. Verhhoeks. Here.
Joint with L. Dekker. Here.
Joint with D.J. Jansen ,R. van der Molen, H. Kho and L. Zhang. Here.
Joint with T. van den Berg, R. van der Molen, M. van Hengel, M. van de Ven and R. Verhoeks. Here.
Joint with H. Koo, D.J. Jansen, R. van der Molen and L. Zhang. Here.
Joint with D.J. Jansen and B. Schrijver. Here.
Joint with M. Bun and J. de Winter [in Dutch]. Here.