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Higher capital ratios are believed to improve system-wide financial stability through three main channels: (i) higher loss-absorption capacity, (ii) lower moral hazard, (iii) stabilization of the financial cycle if capital ratios are increased during good times. We examine these mechanisms in a laboratory asset market experiment with indebted participants. We find support for the loss-absorption channel: higher capital ratios reduce the bankruptcy rate. However, we do not find support for the moral hazard channel. Higher capital ratios (insignificantly) increase asset price bubbles, an aggregate measure of excessive risk-taking. Additional evidence suggests that bankruptcy aversion explains this surprising result. Finally, the evidence supports the idea that higher capital ratios in good times stabilize the financial cycle.
This paper undertakes a quantitative investigation of the effects of anticipated inflation on the distribution of household wealth and welfare. Consumer Finance Data on household financial wealth suggests that about a third of the US population holds all its financial assets in transaction accounts. The remaining two-third of the US population holds most of their financial assets outside transaction accounts. To account for this evidence, I introduce a portfolio choice in a standard incomplete markets model with heterogeneous agents. I calibrate the model economy to SCF 2010 US data and use this environment to study the distributive effects of changes in anticipated inflation. An increase in anticipated inflation leads households to reshuffle their portfolio towards real assets. This crowding-in of supply for real assets lowers equilibrium interest rates and thereby redistributes wealth from creditors to borrowers. Because borrowers have a higher marginal utility, this redistribution improves aggregate welfare. First, this paper shows that inflation acts not only a regressive consumption tax as in Erosa and Ventura (2002), but also as a progressive tax. Second, this paper shows that the welfare cost of inflation are even lower than the estimates computed by Lucas (2000) and Ireland (2009). Finally, this paper offers insights into why deflationary environments should be avoided.
A counterparty credit limit (CCL) is a limit imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures, their implementation restricts the liquidity that institutions can access in an otherwise centralized pool. We address the question of how this mechanism impacts trade prices and volatility, both empirically and via a new model of trading with CCLs. We find empirically that CCLs cause little impact on trade. However, our model highlights that in extreme situations, CCLs could serve to destabilize prices and thereby influence systemic risk.
We study nominal wage rigidity in the Netherlands using administrative data, which has three key features: (1) high-frequency (monthly), (2) high-quality (administrative records), and (3) high coverage (the universe of workers and the universe of firms). We find wage rigidity patterns in the data that are similar to wage behavior documented for other European countries. In particular we find that the hazard function has two spikes, one at 12 months and another one at 24 months and wage changes have time and state dependency components. As a novel and important piece of evidence we also uncover substantial heterogeneity in the frequency of wage changes due to explicit terms of the labor contract. In particular, contracts featuring flexible hours, such as on-call contracts, exhibit a higher probability of a change in the contract wage compared to fixed hour contracts. Once we split the sample based on contract characteristics, we also find that the response of wage changes to the time and state component is heterogeneous across different type of contracts - with relatively more downward adjustments in flexible-hour contract wages in response to aggregate unemployment.
Broad, long-term financial and economic datasets are a scarce resource, in particular in the European context. In this paper, we present an approach for an extensible, i.e. adaptable to future changes in technologies and sources, data model that may constitute a basis for digitized and structured long- term, historical datasets. The data model covers specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence achieving extensibility. Furthermore, based on historical German company and stock market data, we discuss a relational implementation of this approach.
This paper addresses and resolves the issue of microstructure noise when measuring the relative importance of home and U.S. market in the price discovery process of Canadian interlisted stocks. In order to avoid large bounds for information shares, previous studies applying the Cholesky decomposition within the Hasbrouck (1995) framework had to rely on high frequency data. However, due to the considerable amount of microstructure noise inherent in return data at very high frequencies, these estimators are distorted. We offer a modified approach that identifies unique information shares based on distributional assumptions and thereby enables us to control for microstructure noise. Our results indicate that the role of the U.S. market in the price discovery process of Canadian interlisted stocks has been underestimated so far. Moreover, we suggest that rather than stock specific factors, market characteristics determine information shares.
We revisit the role of time in measuring the price impact of trades using a new empirical method that combines spread decomposition and dynamic duration modeling. Previous studies which have addressed the issue in a vector-autoregressive framework conclude that times when markets are most active are times when there is an increased presence of informed trading. Our empirical analysis based on recent European and U.S. data offers challenging new evidence. We find that as trade intensity increases, the informativeness of trades tends to decrease. This result is consistent with the predictions of Admati and Pfleiderer’s (1988) rational expectations model, and also with models of dynamic trading like those proposed by Parlour (1998) and Foucault (1999). Our results cast doubt on the common wisdom that fast markets bear particularly high adverse selection risks for uninformed market participants. JEL Classification: G10, C32 Keywords: Price Impact of Trades, Trading Intensity, Dynamic Duration Models, Spread Decomposition Models, Adverse Selection Risk
The long-run consumption risk model provides a theoretically appealing explanation for prominent asset pricing puzzles, but its intricate structure presents a challenge for econometric analysis. This paper proposes a two-step indirect inference approach that disentangles the estimation of the model's macroeconomic dynamics and the investor's preference parameters. A Monte Carlo study explores the feasibility and efficiency of the estimation strategy. We apply the method to recent U.S. data and provide a critical re-assessment of the long-run risk model's ability to reconcile the real economy and financial markets. This two-step indirect inference approach is potentially useful for the econometric analysis of other prominent consumption-based asset pricing models that are equally difficult to estimate.
The long-run consumption risk (LRR) model is a promising approach to resolve prominent asset pricing puzzles. The simulated method of moments (SMM) provides a natural framework to estimate its deep parameters, but caveats concern model solubility and weak identification. We propose a two-step estimation strategy that combines GMM and SMM, and for which we elicit informative macroeconomic and financial moment matches from the LRR model structure. In particular, we exploit the persistent serial correlation of consumption and dividend growth and the equilibrium conditions for market return and risk-free rate, as well as the model-implied predictability of the risk-free rate. We match analytical moments when possible and simulated moments when necessary and determine the crucial factors required for both identification and reasonable estimation precision. A simulation study – the first in the context of long-run risk modeling – delineates the pitfalls associated with SMM estimation of a non-linear dynamic asset pricing model. Our study provides a blueprint for successful estimation of the LRR model.