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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.
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
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 analyzes the risk properties of typical asset-backed securities (ABS), like CDOs or MBS, relying on a model with both macroeconomic and idiosyncratic components. The examined properties include expected loss, loss given default, and macro factor dependencies. Using a two-dimensional loss decomposition as a new metric, the risk properties of individual ABS tranches can directly be compared to those of corporate bonds, within and across rating classes. By applying Monte Carlo Simulation, we find that the risk properties of ABS differ significantly and systematically from those of straight bonds with the same rating. In particular, loss given default, the sensitivities to macroeconomic risk, and model risk differ greatly between instruments. Our findings have implications for understanding the credit crisis and for policy making. On an economic level, our analysis suggests a new explanation for the observed rating inflation in structured finance markets during the pre-crisis period 2004-2007. On a policy level, our findings call for a termination of the 'one-size-fits-all' approach to the rating methodology for fixed income instruments, requiring an own rating methodology for structured finance instruments. JEL Classification: G21, G28
We analyze a national sample of Americans with respect to their debt literacy, financial experiences, and their judgments about the extent of their indebtedness. Debt literacy is measured by questions testing knowledge of fundamental concepts related to debt and by selfassessed financial knowledge. Financial experiences are the participants’ reported experiences with traditional borrowing, alternative borrowing, and investing activities. Overindebtedness is a self-reported measure. Overall, we find that debt literacy is low: only about one-third of the population seems to comprehend interest compounding or the workings of credit cards. Even after controlling for demographics, we find a strong relationship between debt literacy and both financial experiences and debt loads. Specifically, individuals with lower levels of debt literacy tend to transact in high-cost manners, incurring higher fees and using high-cost borrowing. In applying our results to credit cards, we estimate that as much as one-third of the charges and fees paid by less knowledgeable individuals can be attributed to ignorance. The less knowledgeable also report that their debt loads are excessive or that they are unable to judge their debt position. JEL Classification: D14, D91
The recent financial crisis has led to a major debate about fair-value accounting. Many critics have argued that fair-value accounting, often also called mark-to-market accounting, has significantly contributed to the financial crisis or, at least, exacerbated its severity. In this paper, we assess these arguments and examine the role of fair-value accounting in the financial crisis using descriptive data and empirical evidence. Based on our analysis, it is unlikely that fair-value accounting added to the severity of the current financial crisis in a major way. While there may have been downward spirals or asset-fire sales in certain markets, we find little evidence that these effects are the result of fair-value accounting. We also find little support for claims that fair-value accounting leads to excessive write-downs of banks’ assets. If anything, empirical evidence to date points in the opposite direction, that is, towards overvaluation of bank assets.
We merge administrative information from a large German discount brokerage firm with regional data to examine if financial advisors improve portfolio performance. Our data track accounts of 32,751 randomly selected individual customers over 66 months and allow direct comparison of performance across self-managed accounts and accounts run by, or in consultation with, independent financial advisors. In contrast to the picture painted by simple descriptive statistics, econometric analysis that corrects for the endogeneity of the choice of having a financial advisor suggests that advisors are associated with lower total and excess account returns, higher portfolio risk and probabilities of losses, and higher trading frequency and portfolio turnover relative to what account owners of given characteristics tend to achieve on their own. Regression analysis of who uses an IFA suggests that IFAs are matched with richer, older investors rather than with poorer, younger ones.