Correlation, price discovery and co-movement of ABS and equity

Asset-backed securitization (ABS) has become a viable and increasingly attractive risk management and refinancing method either as a standalone form of structured finance or as securitized debt in Collateralized Debt Obligations (CDO). However, the absence of industry standardization has prevented rising investment demand from translating into market liquidity comparable to traditional fixed income instruments, in all but a few selected market segments. Particularly low financial transparency and complex security designs inhibits profound analysis of secondary market pricing and how it relates to established forms of external finance. This paper represents the first attempt to measure the intertemporal, bivariate causal relationship between matched price series of equity and ABS issued by the same entity. In a two-dimensional linear system of simultaneous equations we investigate the short-term dynamics and long-term consistency of daily secondary market data from the U.K. Sterling ABS/MBS market and exchange traded shares between 1998 and 2004 with and without the presence of cointegration. Our causality framework delivers compelling empirical support for a strong co-movement between matched price series of ABS-equity pairs, where ABS markets seem to contribute more to price discovery over the long run. Controlling for cointegration, risk-free interest and average market risk of corporate debt hardly alters our results. However, once we qualify the magnitude and direction of price discovery on various security characteristics, such as the ABS asset class, we find that ABS-equity pairs with large-scale CMBS/RMBS and credit card/student loan ABS reveal stronger lead-lag relationships and joint price dynamics than whole business ABS. JEL Classifications: G10, G12, G2

European Securitisation : a GARCH model of CDO, MBS and Pfandbrief spreads

Asset-backed securitisation (ABS) is an asset funding technique that involves the issuance of structured claims on the cash flow performance of a designated pool of underlying receivables. Efficient risk management and asset allocation in this growing segment of fixed income markets requires both investors and issuers to thoroughly understand the longitudinal properties of spread prices. We present a multi-factor GARCH process in order to model the heteroskedasticity of secondary market spreads for valuation and forecasting purposes. In particular, accounting for the variance of errors is instrumental in deriving more accurate estimators of time-varying forecast confidence intervals. On the basis of CDO, MBS and Pfandbrief transactions as the most important asset classes of off-balance sheet and on-balance sheet securitisation in Europe we find that expected spread changes for these asset classes tends to be level stationary with model estimates indicating asymmetric mean reversion. Furthermore, spread volatility (conditional variance) is found to follow an asymmetric stochastic process contingent on the value of past residuals. This ABS spread behaviour implies negative investor sentiment during cyclical downturns, which is likely to escape stationary approximation the longer this market situation lasts

Risk measures for direct real estate investments with non-normal or unknown return distributions

The volatility of returns is probably the most widely used risk measure for real estate. This is rather surprising since a number of studies have cast doubts on the view that volatility can capture the manifold risks attached to properties and corresponds to the risk attitude of investors. A central issue in this discussion is the statistical properties of real estate returns—in contrast to neoclassical capital market theory they are mostly non-normal and often unknown, which render many statistical measures useless. Based on a literature review and an analysis of data from Germany we provide evidence that volatility alone is inappropriate for measuring the risk of direct real estate. We use a unique data sample by IPD, which includes the total returns of 939 properties across different usage types (56% office, 20% retail, 8% others and 16% residential properties) from 1996 to 2009, the German IPD Index, and the German Property Index. The analysis of the distributional characteristics shows that German real estate returns in this period were not normally distributed and that a logistic distribution would have been a better fit. This is in line with most of the current literature on this subject and leads to the question which indicators are more appropriate to measure real estate risks. We suggest that a combination of quantitative and qualitative risk measures more adequately captures real estate risks and conforms better with investor attitudes to risk. Furthermore, we present criteria for the purpose of risk classification

An empirical study on the decoupling movements between corporate bond and CDS spreads

Applied to the European markets, this paper analyzes the price of credit risk on the Credit Default Swap (CDS) and corporate bond markets by comparing the sensitivity of the credit spreads on each market to systematic, idiosyncratic risk factors and liquidity. Our analysis confirms the existence of a long-run relationship between the two markets, and the tendency for CDS markets to lead corporate bond markets in terms of price discovery. We find that the outbreak of the financial turmoil in the summer of 2007 induced a substantial increase in risk aversion and a shift in the pricing of credit risk, with CDS markets becoming more sensitive to systematic risk while cash bond markets priced in more information about liquidity and idiosyncratic risk. Moreover, the financial turbulence also brought about a systematic disconnection between the two markets caused by the significant change in the lead-lag relationship, with CDS markets always leading the cash bond markets. JEL Classification: G12, G14, G15corporate bond spreads, Credit Default Swap Spreads, liquidity

Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets

This paper poses a few fundamental questions regarding the attributes of the volume profile of a Limit Order Books stochastic structure by taking into consideration aspects of intraday and interday statistical features, the impact of different exchange features and the impact of market participants in different asset sectors. This paper aims to address the following questions: 1. Is there statistical evidence that heavy-tailed sub-exponential volume profiles occur at different levels of the Limit Order Book on the bid and ask and if so does this happen on intra or interday time scales ? 2.In futures exchanges, are heavy tail features exchange (CBOT, CME, EUREX, SGX and COMEX) or asset class (government bonds, equities and precious metals) dependent and do they happen on ultra-high (<1sec) or mid-range (1sec -10min) high frequency data? 3.Does the presence of stochastic heavy-tailed volume profile features evolve in a manner that would inform or be indicative of market participant behaviors, such as high frequency algorithmic trading, quote stuffing and price discovery intra-daily? 4. Is there statistical evidence for a need to consider dynamic behavior of the parameters of models for Limit Order Book volume profiles on an intra-daily time scale ? Progress on aspects of each question is obtained via statistically rigorous results to verify the empirical findings for an unprecedentedly large set of futures market LOB data. The data comprises several exchanges, several futures asset classes and all trading days of 2010, using market depth (Type II) order book data to 5 levels on the bid and ask

Establishing Credibility: Evolving Perceptions of the European Central Bank

The credibility of a central bank’s anti-inflation stance, a key determinant of its success, may reflect institutional structure or, more dynamically, the history of policy decisions. The first years of the European Central Bank (ECB) provide a natural experiment for considering whether, and how, central bank credibility evolves. In this paper, we present a model demonstrating how the high-frequency response of asset prices to news reflects market perceptions of the anti-inflation stance of a central bank. Empirical tests of this model on high frequency data, regressing both the change in the slope of the German yield curve and the change in the euro/dollar exchange rate on the surprise component of price news, suggest significant instability in the market’s perception of the policy stance of the ECB during its first five years of operation. Estimated smoothed paths of the coefficients linking news to asset prices show that these coefficients change with policies undertaken by the ECB. In contrast, there is no evidence of parameter instability for the response of the slope of the United States yield curve to price news during this period, suggesting no comparable evolution in the market perceptions of the commitment to inflation fighting by the Federal Reserve.Central Banking, European Central Bank, Federal Reserve, inflation, exchange rate, credibility, yield curve

Mission Dependency Index of Air Force Built Infrastructure: Knowledge Discovery with Machine Learning

Mission Dependency Index (MDI) is a metric developed to capture the relative criticality of infrastructure assets with respect to organizational missions. The USAF adapted the MDI metric from the United States Navy’s MDI methodology. Unlike the Navy’s MDI data collection process, the USAF adaptation of the MDI metric employs generic facility category codes (CATCODEs) to assign MDI values. This practice introduces uncertainty into the MDI assignment process with respect to specific missions and specific infrastructure assets. The uncertainty associated with USAF MDI values necessitated the MDI adjudication process. The MDI adjudication process provides a mechanism for installation civil engineer personnel to lobby for accurate MDI values for specific infrastructure assets. The MDI adjudication process requires manual identification of MDI discrepancies, documentation, and extensive coordination between organizations. Given the existing uncertainty with USAF MDI values and the effort required for the MDI adjudication process, this research pursues machine learning and the knowledge discovery in databases (KDD) process to identify and understand relationships between real property data and mission critical infrastructure. Furthermore, a decision support tool is developed for the MDI adjudication process. Specifically, supervised learning techniques are employed to develop a classifier that can identify potential MDI discrepancies. This automation effort serves to minimize the manual MDI review process by identifying a subset of facilities for potential adjudication

Estimating the expected cost of equity capital using consensus forecasts

In this study, we develop a technique for estimating a firm’s expected cost of equity capital derived from analyst consensus forecasts and stock prices. Building on the work of Gebhardt/Lee/-Swaminathan (2001) and Easton/Taylor/Shroff/Sougiannis (2002), our approach allows daily estimation, using only publicly available information at that date. We then estimate the expected cost of equity capital at the market, industry and individual firm level using historical German data from 1989-2002 and examine firm characteristics which are systematically related to these estimates. Finally, we demonstrate the applicability of the concept in a contemporary case study for DaimlerChrysler and the European automobile industry