Sustainable Architecture for Finance in Europe (SAFE)
Refine
Year of publication
Document Type
- Working Paper (803)
- Part of Periodical (492)
- Report (62)
- Article (32)
- Contribution to a Periodical (2)
- Conference Proceeding (1)
- Review (1)
Has Fulltext
- yes (1393) (remove)
Is part of the Bibliography
- no (1393)
Keywords
- Financial Institutions (88)
- Capital Markets Union (65)
- ECB (60)
- Financial Markets (58)
- Banking Union (50)
- Banking Regulation (47)
- Household Finance (41)
- Banking Supervision (40)
- Macro Finance (40)
- Monetary Policy (35)
Institute
- Sustainable Architecture for Finance in Europe (SAFE) (1393)
- Wirtschaftswissenschaften (1341)
- Center for Financial Studies (CFS) (777)
- House of Finance (HoF) (684)
- Institute for Monetary and Financial Stability (IMFS) (123)
- Rechtswissenschaft (62)
- Foundation of Law and Finance (47)
- Institute for Law and Finance (ILF) (7)
- Gesellschaftswissenschaften (6)
- Frankfurt MathFinance Institute (FMFI) (3)
Inflation and trading
(2024)
We study how investors respond to inflation combining a customized survey experiment with trading data at a time of historically high inflation. Investors' beliefs about the stock return-inflation relation are very heterogeneous in the cross section and on average too optimistic. Moreover, many investors appear unaware of inflation-hedging strategies despite being otherwise well-informed about inflation and asset returns. Consequently, whereas exogenous shifts in inflation expectations do not impact return expectations, information on past returns during periods of high inflation leads to negative updating about the perceived stock-return impact of inflation, which feeds into return expectations and subsequent actual trading behavior.
We educate investors with significant dividend holdings about the benefits of dividend reinvestment and the costs of misperceiving dividends as additional, free income. The intervention increases planned dividend reinvestment in survey responses. Using trading records, we observe a corresponding causal increase in dividend reinvestment in the field of roughly 50 cents for every euro received. This holds relative to their prior behavior and a placebo sample. Investors who learned the most from the intervention update their trading by the largest extent. The results suggest the free dividends fallacy is a significant source of dividend demand. Our study demonstrates that simple, targeted, and focused educational interventions can affect investment behavior.
How does the design of debt repayment schedules affect household borrowing? To answer this question, we exploit a Swedish policy reform that eliminated interest-only mortgages for loan-to-value ratios above 50%. We document substantial bunching at the threshold, leading to 5% lower borrowing. Wealthy borrowers drive the results, challenging credit constraints as the primary explanation. We develop a model to evaluate the mechanisms driving household behavior and find that much of the effect comes from households experiencing ongoing flow disutility to amortization payments. Our results indicate that mortgage contracts with low initial payments substantially increase household borrowing and lifetime interest costs.
This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward non-linear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE) model, a Bayesian vector autoregression (BVAR) using optimized priors as well as Greenbook and SPF forecasts. Model estimation and forecasting is based on an expanding window scheme using quarterly U.S. real-time data (1964Q2:2020Q3) for 8 macroeconomic time series (GDP, inflation, federal funds rate, spread, consumption, investment, wage, hours worked), allowing for up to 8 quarter ahead forecasts. The results show that the BVAR improves forecasts compared to the DSGE model, however there is evidence for an overall improvement of predictions when relying on ANN, or including them in a weighted average. Especially, ANN-based inflation forecasts improve other predictions by up to 50%. These results indicate that nonlinear data-driven ANNs are a useful method when it comes to macroeconomic forecasting.
Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. The author introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables between 1990 and 2015, the economy is estimated by artificial neural networks. Historical counterfactual analyses show that real effects are less pronounced than yield effects.
Disentangling the effects of the individual asset purchase programs, impulse response functions provide evidence for QE being less effective the more the crisis is overcome. The peak effects of all QE interventions during the Financial Crisis only amounts to 1.3 pp for GDP growth and 0.6 pp for inflation respectively. Hence, the time as well as the volume of the interventions should be deliberated.
When estimating misspecified linear factor models for the cross-section of expected returns using GMM, the explanatory power of these models can be spuriously high when the estimated factor means are allowed to deviate substantially from the sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. The mathematically correct global minimum of the GMM objective function can be obtained at a parameter vector that is far from the true parameters of the data-generating process. This property is not restricted to small samples, but rather holds in population. It is a feature of the GMM estimation design and applies to both strong and weak factors, as well as to all types of test assets.
We study the many implications of the Eurosystem collateral framework for corporate bonds. Using data on the evolving collateral eligibility list, we identify the first inclusion dates of bonds and issuers and use these events to find that the increased supply and demand for pledgeable collateral following eligibility (a) increases activity in the corporate securities lending market, (b) lowers eligible bond yields, and (c) affects bond liquidity. Thus, corporate bond lending relaxes the constraint of limited collateral supply and thereby improves market functioning.
This paper empirically analyses whether post-global financial crisis regulatory reforms have created appropriate incentives to voluntarily centrally clear over-the-counter (OTC) derivative contracts. We use confidential European trade repository data on single-name sovereign credit default swap (CDS) transactions and show that both seller and buyer manage counterparty exposures and capital costs, strategically choosing to clear when the counterparty is riskier. The clearing incentives seem particularly responsive to seller credit risk, which is in line with the notion that counterparty credit risk (CCR) is asymmetric in CDS contracts. The riskiness of the underlying reference entity also impacts the decision to clear as it affects both CCR capital charges for OTC contracts and central counterparty clearing house (CCP) margins for cleared contracts. Lastly, we find evidence that when a transaction helps netting positions with the CCP and hence lower margins, the likelihood of clearing is higher.