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We present a simple model of personal finance in which an incumbent lender has an information advantage vis-a-vis both potential competitors and households. In order to extract more consumer surplus, a lender with sufficient market power may engage in "irresponsible"lending, approving credit even if this is knowingly against a household’s best interest. Unless rival lenders are equally well informed, competition may reduce welfare. This holds, in particular, if less informed rivals can free ride on the incumbent’s superior screening ability.
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results.
The paper proposes a variation of simulation for checking and proving contextual equivalence in a non-deterministic call-by-need lambda-calculus with constructors, case, seq, and a letrec with cyclic dependencies. It also proposes a novel method to prove its correctness. The calculus’ semantics is based on a small-step rewrite semantics and on may-convergence. The cyclic nature of letrec bindings, as well as nondeterminism, makes known approaches to prove that simulation implies contextual equivalence, such as Howe’s proof technique, inapplicable in this setting. The basic technique for the simulation as well as the correctness proof is called pre-evaluation, which computes a set of answers for every closed expression. If simulation succeeds in finite computation depth, then it is guaranteed to show contextual preorder of expressions.
A question of Mesorah?
(2009)
In the upcoming Krias Hatorah in Parshat Shoftim and Parshat Ki Savo there are a number of instances where the meaning of a phrase changes completely based on the pronunciation of a single word – םד – with either a Komatz or Patah. Until recently, most Chumashim and Tikunim which generally followed the famous Yaakov Ben Hayyim 1525 edition of Mikraot Gedolot published in Venice that printed a seemingly inconsistent pattern in the pronunciation of the different occurrences of this word.
We present a tractable model of the effects of nonfinancial risk on intertemporal choice. Our purpose is to provide a simple framework that can be adopted in fields like representative-agent macroeconomics, corporate finance, or political economy, where most modelers have chosen not to incorporate serious nonfinancial risk because available methods were too complex to yield transparent insights. Our model produces an intuitive analytical formula for target assets, and we show how to analyze transition dynamics using a familiar Ramsey-style phase diagram. Despite its starkness, our model captures most of the key implications of nonfinancial risk for intertemporal choice.
We model the motives for residents of a country to hold foreign assets, including the precautionary motive that has been omitted from much previous literature as intractable. Our model captures many of the principal insights from the existing specialized literature on the precautionary motive, deriving a convenient formula for the economy’s target value of assets. The target is the level of assets that balances impatience, prudence, risk, intertemporal substitution, and the rate of return. We use the model to shed light on two topical questions: The “upstream” flows of capital from developing countries to advanced countries, and the long-run impact of resorbing global financial imbalances
We investigate methods and tools for analyzing translations between programming languages with respect to observational semantics. The behavior of programs is observed in terms of may- and mustconvergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extensions.
Algorithmic trading engines versus human traders – do they behave different in securities markets?
(2009)
After exchanges and alternative trading venues have introduced electronic execution mechanisms worldwide, the focus of the securities trading industry shifted to the use of fully electronic trading engines by banks, brokers and their institutional customers. These Algorithmic Trading engines enable order submissions without human intervention based on quantitative models applying historical and real-time market data. Although there is a widespread discussion on the pros and cons of Algorithmic Trading and on its impact on market volatility and market quality, little is known on how algorithms actually place their orders in the market and whether and in which respect this differs form other order submissions. Based on a dataset that – for the first time – includes a specific flag to enable the identification of orders submitted by Algorithmic Trading engines, the paper investigates the extent of Algorithmic Trading activity and specifically their order placement strategies in comparison to human traders in the Xetra trading system. It is shown that Algorithmic Trading has become a relevant part of overall market activity and that Algorithmic Trading engines fundamentally differ from human traders in their order submission, modification and deletion behavior as they exploit real-time market data and latest market movements.
Analyzing interest rate risk: stochastic volatility in the term structure of government bond yields
(2009)
We propose a Nelson-Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the factor volatilities follow highly persistent processes. We show that slope and curvature risk have explanatory power for bond excess returns and illustrate that the yield and volatility factors are closely related to industrial capacity utilization, inflation, monetary policy and employment growth. JEL Classification: C5, E4, G1
This paper argues that banks must be sufficiently levered to have first-best incentives to make new risky loans. This result, which is at odds with the notion that leverage invariably leads to excessive risk taking, derives from two key premises that focus squarely on the role of banks as informed lenders. First, banks finance projects that they do not own, which implies that they cannot extract all the profits. Second, banks conduct a credit risk analysis before making new loans. Our model may help understand why banks take on additional unsecured debt, such as unsecured deposits and subordinated loans, over and above their existing deposit base. It may also help understand why banks and finance companies have similar leverage ratios, even though the latter are not deposit takers and hence not subject to the same regulatory capital requirements as banks.