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We extend the important idea of range-based volatility estimation to the multivariate case. In particular, we propose a range-based covariance estimator that is motivated by financial economic considerations (the absence of arbitrage), in addition to statistical considerations. We show that, unlike other univariate and multivariate volatility estimators, the range-based estimator is highly efficient yet robust to market microstructure noise arising from bid-ask bounce and asynchronous trading. Finally, we provide an empirical example illustrating the value of the high-frequency sample path information contained in the range-based estimates in a multivariate GARCH framework.
We develop a utility based model of fluctuations, with nominal rigidities, and unemployment. In doing so, we combine two strands of research: the New Keynesian model with its focus on nominal rigidities, and the Diamond-Mortensen-Pissarides model, with its focus on labor market frictions and unemployment. In developing this model, we proceed in two steps. We first leave nominal rigidities aside. We show that, under a standard utility specification, productivity shocks have no effect on unemployment in the constrained efficient allocation. We then focus on the implications of alternative real wage setting mechanisms for fluctuations in unemployment. We then introduce nominal rigidities in the form of staggered price setting by firms. We derive the relation between inflation and unemployment and discuss how it is influenced by the presence of real wage rigidities. We show the nature of the tradeoff between inflation and unemployment stabilization, and we draw the implications for optimal monetary policy. JEL Classification: E32, E50
A new governance architecture for european financial markets? Towards a european supervision of CCPs
(2018)
Does the new European outlook on financial markets, as voiced by the EU Commission since the beginning of the Capital Market Unions imply a movement of the EU towards an alignment of market integration and direct supervision of common rules? This paper sets out to answer this question for the case of common supervision for Central Counterparties (CCPs) in the European Union. Those entities gained crucial importance post-crisis due to new regulation which requires the mandatory clearing of standardized derivative contracts, transforming clearing houses into central nodes for cross-border financial transactions. While the EU-wide regulatory framework EMIR, enacted in 2012, stipulates common regulatory requirements, the framework still relies on home-country supervision of those rules, arguably leading to regulatory as well as supervisory arbitrage. Therefore, the regulatory reform to stabilize the OTC derivatives market replicated at its center a governance flaw, which had been identified as one of the major causes for the gravity of the financial crisis in the EU: the coupling of intense competition based on private risk management systems with a national supervision of European rules. This paper traces the history of this problem awareness and inquires which factors account for the fact that only in 2017 serious negotiations at the EU level ensued that envisioned a common supervision of CCPs to fix the flawed system of governance. Analyzing this shift in the European governance architecture, we argue that Brexit has opened a window of opportunity for a centralization of supervision for CCPs. Brexit aligns the urgency of the problem with material interests of crucial political stakeholder, in particular of Germany and France, providing the possibility for a grand European bargain.
One of the dangers of harmonisation and unification processes taking place within the framework of the EU is that they may result in the codification of the lowest common denominator. This is precisely what is threatening to happen in respect of assignment. Referring the transfer of receivables by way of assignment to the law of the assignor’s residence, as article 13 of the Proposal does, would be opting for the most conservative solution and would for many Member States be a step backward rather than forward. A conflict rule referring assignment to the law of the assignor's residence is too rigid to do justice to the dynamic nature of assignments in cross-border transactions and it is unjustly one-sided. It offers no real advantages when compared to other conflict rules; it even has serious disadvantages which make the conflict rule unsuitable for efficient assignment-based cross-border transactions. It is not unconceivable that this conflict rule would even be contrary to the fundamental freedoms of the ECTreaty. The Community legislators in particular should be careful not to needlessly adopt rules which create insurmountable obstacles for cross-border business where choice-of-law by the parties would perfectly do. Community legislation has a special responsibility to create a smooth legal environment for single market transactions.
In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development.
In the aftermath of the global financial crisis, the state of macroeconomicmodeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development
Central banks have faced a succession of crises over the past years as well as a number of structural factors such as a transition to a greener economy, demographic developments, digitalisation and possibly increased onshoring. These suggest that the future inflation environment will be different from the one we know. Thus uncertainty about important macroeconomic variables and, in particular, inflation dynamics will likely remain high.
This paper reviews social network analysis (SNA) as a method to be utilized in biographical research which is a novel contribution. We argue that applying SNA in the context of biography research through standardized data collection as well as visualization of networks can open up participants’ interpretations of relations throughout their lives, and allow a creative and innovative way of data collection that is responsive to participants’ own meanings and associations while allowing the researchers to conduct systematical data analysis. The paper discusses the analytical potential of SNA in biographical research, where the efficacy of this method is critically discussed, together with its limitations, and its potential within the context of biographical research.
We present an empirical study focusing on the estimation of a fundamental multi-factor model for a universe of European stocks. Following the approach of the BARRA model, we have adopted a cross-sectional methodology. The proportion of explained variance ranges from 7.3% to 66.3% in the weekly regressions with a mean of 32.9%. For the individual factors we give the percentage of the weeks when they yielded statistically significant influence on stock returns. The best explanatory power – apart from the dominant country factors – was found among the statistical constructs „success“ and „variability in markets“.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
This paper solves a dynamic model of households' mortgage decisions incorporating labor income, house price, inflation, and interest rate risk. It uses a zero-profit condition for mortgage lenders to solve for equilibrium mortgage rates given borrower characteristics and optimal decisions. The model quantifies the effects of adjustable vs. fixed mortgage rates, loan-to-value ratios, and mortgage affordability measures on mortgage premia and default. Heterogeneity in borrowers' labor income risk is important for explaining the higher default rates on adjustable-rate mortgages during the recent US housing downturn, and the variation in mortgage premia with the level of interest rates.
The Inuit inhabit a vast area of--from a European point of view--most inhospitable land, stretching from the northeastern tip of Asia to the east coast of Greenland. Inuit peoples have never been numerous, their settlements being scattered over enormous distances. But nevertheless, from an ethnological point of view, all Inuit peoples shared a distinct culture, featuring sea mammal and caribou hunting, sophisticated survival skills, technical and social devices, including the sharing of essential goods and strategies for minimizing and controlling aggression.
On average, "young" people underestimate whereas "old" people overestimate their chances to survive into the future. We adopt a Bayesian learning model of ambiguous survival beliefs which replicates these patterns. The model is embedded within a non-expected utility model of life-cycle consumption and saving. Our analysis shows that agents with ambiguous survival beliefs (i) save less than originally planned, (ii) exhibit undersaving at younger ages, and (iii) hold larger amounts of assets in old age than their rational expectations counterparts who correctly assess their survival probabilities. Our ambiguity-driven model therefore simultaneously accounts for three important empirical findings on household saving behavior.
Based on a cognitive notion of neo-additive capacities reflecting likelihood insensitivity with respect to survival chances, we construct a Choquet Bayesian learning model over the life-cycle that generates a motivational notion of neo-additive survival beliefs expressing ambiguity attitudes. We embed these neo-additive survival beliefs as decision weights in a Choquet expected utility life-cycle consumption model and calibrate it with data on subjective survival beliefs from the Health and Retirement Study. Our quantitative analysis shows that agents with calibrated neo-additive survival beliefs (i) save less than originally planned, (ii) exhibit undersaving at younger ages, and (iii) hold larger amounts of assets in old age than their rational expectations counterparts who correctly assess their survival chances. Our neo-additive life-cycle model can therefore simultaneously accommodate three important empirical findings on household saving behavior.
We consider an imperfectly competitive loan market in which a local relationship lender has an information advantage vis-à-vis distant transaction lenders. Competitive pressure from the transaction lenders prevents the local lender from extracting the full surplus from projects, so that she inefficiently rejects marginally profitable projects. Collateral mitigates the inefficiency by increasing the local lender’s payoff from precisely those marginal projects that she inefficiently rejects. The model predicts that, controlling for observable borrower risk, collateralized loans are more likely to default ex post, which is consistent with the empirical evidence. The model also predicts that borrowers for whom local lenders have a relatively smaller information advantage face higher collateral requirements, and that technological innovations that narrow the information advantage of local lenders, such as small business credit scoring, lead to a greater use of collateral in lending relationships. JEL classification: D82; G21 Keywords: Collateral; Soft infomation; Loan market competition; Relationship lending