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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
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.
We highlight the implications of combining underwriting services and lending for the choice of underwriters and for competition in the underwriting business. We show that cross-selling can increase underwriters’ incentives, and we explain three phenomena: first, that cross-selling is important for universal banks to enter the investment banking business; second, that cross-selling is particularly attractive for highly leveraged borrowers; third, that less-than-market rates are no prerequisite for cross-selling to benefit a bank’s clients. In our model, cross-selling reduces rents in the underwriting business.
The recent financial crisis has led to a vigorous debate about the pros and cons of fair-value accounting (FVA). This debate presents a major challenge for FVA going forward and standard setters’ push to extend FVA into other areas. In this article, we highlight four important issues as an attempt to make sense of the debate. First, much of the controversy results from confusion about what is new and different about FVA. Second, while there are legitimate concerns about marking to market (or pure FVA) in times of financial crisis, it is less clear that these problems apply to FVA as stipulated by the accounting standards, be it IFRS or U.S. GAAP. Third, historical cost accounting (HCA) is unlikely to be the remedy. There are a number of concerns about HCA as well and these problems could be larger than those with FVA. Fourth, although it is difficult to fault the FVA standards per se, implementation issues are a potential concern, especially with respect to litigation. Finally, we identify several avenues for future research. JEL Classification: G14, G15, G30, K22, M41, M42
Content A. EXECUTIVE SUMMARY, INCLUDING MAJOR RECOMMENDATIONS B. COMPLETE REPORT 1. INTRODUCTION 2. RISK MAP 2.1 Why a Risk Map is needed, and for what purpose 2.1.1 Creating a unified data base 2.1.2 Assessing systemic risk 2.1.3 Allowing for coordinated policy action 2.2 Recommendations 3. GLOBAL REGISTER FOR LOANS (CREDIT REGISTER) AND BONDS (SECURITIES REGISTER) 3.1 Objectives of a credit register 3.2 Credit registers in Europe (and beyond) 3.3 Suggestions for a supra-national Credit Register 3.4 Integrating a supra-national Securities Register 3.5 Recommendations 4. HEDGE FUNDS: REGULATION AND SUPERVISION 4.1 What are hedge funds (activities, location, size, regulation)? 4.2 What are the risks posed by hedge funds (systematic risks, interaction with prime brokers)? 4.3 Routes to better regulation (direct, indirect) 4.4 Recommendations 5. RATING AGENCIES: REGULATION AND SUPERVISION 5.1 The role of ratings in bond and structured finance markets, past and present 5.2 Elements of rating integrity (independence, compensation and incentives, transparency) 5.3 Recommendations (registration, transparency, annual report on rating performance) 6. PROCYCLICALITY: PROBLEMS AND POTENTIAL SOLUTIONS 6.1 What is meant by “procyclicality” and why is it a problem? 6.2 The roots of procyclicality and the lessons it suggests for policymakers 6.2.1 Underpinnings of the phenomenon 6.2.2 Lessons to be learned 6.3 Characteristics of a macrofinancial stability framework 6.4 Recommendations 7. THE ROLE OF INTERNATIONAL INSTITUTIONS AND FORA, IN PARTICULAR THE IMF, BIS AND FSF 7.1 Legitimacy 7.2 Re-focusing the work 7.3 Recommendations
Content New Financial Architecture (Short Version) 1. Purpose of the paper – causes of the crisis 2. Recommendations 2.1. Incentives 2.2. Transparency 2.3. Regulation and Supervision 2.4. International Institutions 3. Concluding remarks Appendix (Full text) A 1. Causes of the crisis A 2. Improving the Framework A 2.1. Incentives A 2.2. Transparency A 2.3. Regulation and Supervision A 2.4. International Institutions A 3. Concluding remarks
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
Suppliers play a major role in innovation processes. We analyze ownership allocations and the choice of R&D technology in vertical R&D cooperations. Given incomplete contracts on the R&D outcome, there is a tradeoff between R&D specifically designed towards a manufacturer (increasing investment productivity) and a general technology (hold-up reduction). We find that the market solution yields the specific technology in too few cases. More intense product market competition shifts optimal ownership towards the supplier. The use of exit clauses increases the gains from the collaboration. JEL Classification: L22, L24, O31, O32
Venture capital exit rights
(2009)
Theorists argue that exit rights can mitigate hold-up problems in venture capital. Using a hand-collected data-set of venture capital contracts from Germany we show that exit rights are included more frequently in venture capital contracts when a hold-up problem associated with the venture capitalist's exit decision is likely. Examples include drag-along and tag-along rights. Additionally, we find that almost all exit rights are allocated to the venture capitalist rather than to the entrepreneur. In addition, we show that besides the basic hold-up mechanism there are other mechanisms such as ex-ante bargaining power and the degree of pledgeable income that drive the allocation of exit rights. JEL Classification: G24, G34, D80
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.
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 provides a joint analysis of household stockholding participation, stock location among stockholding modes, and participation spillovers, using data from the US Survey of Consumer Finances. Our multivariate choice model matches observed participation rates, conditional and unconditional, and asset location patterns. Financial education and sophistication strongly affect direct stockholding and mutual fund participation, while social interactions affect stockholding through retirement accounts only. Household characteristics influence stockholding through retirement accounts conditional on owning retirement accounts, unlike what happens with stockholding through mutual funds. Although stockholding is more common among retirement account owners, this fact is mainly due to their characteristics that led them to buy retirement accounts in the first place rather than to any informational advantages gained through retirement account ownership itself. Finally, our results suggest that, taking stockholding as given, stock location is not arbitrary but crucially depends on investor characteristics. JEL Classification: G11, E21, D14, C35
We investigate, using the 2002 US Health and Retirement Study, the factors influencing individuals’ insecurity and expectations about terrorism, and study the effects these last have on households’ portfolio choices and spending patterns. We find that females, the religiously devout, those equipped with a better memory, the less educated, and those living close to where the events of September 2001 took place worry a lot about their safety. In addition, fear of terrorism discourages households from investing in stocks, mostly through the high levels of insecurity felt by females. Insecurity due to terrorism also makes single men less likely to own a business. Finally, we find evidence of expenditure shifting away from recreational activities that can potentially leave one exposed to a terrorist attack and towards goods that might help one cope with the consequences of terrorism materially (increased use of car and spending on the house) or psychologically (spending on personal care products by females in couples).
We document significant and robust empirical relationships in cross-country panel data between government size or social expenditure on the one hand, and trade and financial development indicators on the other. Across countries, deeper economic integration is associated with more intense government redistribution, but more developed financial markets weaken that relationship. Over time, controlling for country-specific effects, public social expenditure appears to be eroded by globalization trends where financial market development can more easily substitute for it.
The paper provides novel insights on the effect of a firm’s risk management objective on the optimal design of risk transfer instruments. I analyze the interrelation between the structure of the optimal insurance contract and the firm’s objective to minimize the required equity it has to hold to accommodate losses in the presence of multiple risks and moral hazard. In contrast to the case of risk aversion and moral hazard, the optimal insurance contract involves a joint deductible on aggregate losses in the present setting.
This paper analyzes liquidity in an order driven market. We only investigate the best limits in the limit order book, but also take into account the book behind these inside prices. When subsequent prices are close to the best ones and depth at them is substantial, larger orders can be executed without an extensive price impact and without deterring liquidity. We develop and estimate several econometric models, based on depth and prices in the book, as well as on the slopes of the limit order book. The dynamics of different dimensions of liquidity are analyzed: prices, depth at and beyond the best prices, as well as resiliency, i.e. how fast the different liquidity measures recover after a liquidity shock. Our results show a somewhat less favorable image of liquidity than often found in the literature. After a liquidity shock (in the spread or depth or in the book beyond the best limits), several dimension of liquidity deteriorate at the same time. Not only does the inside spread increase, and depth at the best prices decrease, also the difference between subsequent bid and ask prices may become larger and depth provided at them decreases. The impacts are both econometrically and economically significant. Also, our findings point to an interaction between different measures of liquidity, between liquidity at the best prices and beyond in the book, and between ask and bid side of the market.
Previous evidence suggests that less liquid stocks entail higher average returns. Using NYSE data, we present evidence that both the sensitivity of returns to liquidity and liquidity premia have significantly declined over the past four decades to levels that we cannot statistically distinguish from zero. Furthermore, the profitability of trading strategies based on buying illiquid stocks and selling illiquid stocks has declined over the past four decades, rendering such strategies virtually unprofitable. Our results are robust to several conventional liquidity measures related to volume. When using liquidity measure that is not related to volume, we find just weak evidence of a liquidity premium even in the early periods of our sample. The gradual introduction and proliferation of index funds and exchange traded funds is a possible explanation for these results.
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.
Innovative automated execution strategies like Algorithmic Trading gain significant market share on electronic market venues worldwide, although their impact on market outcome has not been investigated in depth yet. In order to assess the impact of such concepts, e.g. effects on the price formation or the volatility of prices, a simulation environment is presented that provides stylized implementations of algorithmic trading behavior and allows for modeling latency. As simulations allow for reproducing exactly the same basic situation, an assessment of the impact of algorithmic trading models can be conducted by comparing different simulation runs including and excluding a trader constituting an algorithmic trading model in its trading behavior. By this means the impact of Algorithmic Trading on different characteristics of market outcome can be assessed. The results indicate that large volumes to execute by the algorithmic trader have an increasing impact on market prices. On the other hand, lower latency appears to lower market volatility.