Monitoring Leverage

We discuss how leverage can be monitored for institutions, individuals, and assets. While traditionally the interest rate has been regarded as the important feature of a loan, we argue that leverage is sometimes even more important. Monitoring leverage provides information about how risk builds up during booms as leverage rises and how crises start when leverage on new loans sharply declines. Leverage data is also a crucial input for crisis management and lending facilities. Leverage at the asset level can be monitored by down payments or margin requirement or and haircuts, giving a model-free measure that can be observed directly, in contrast to other measures of systemic risk that require complex estimation. Asset leverage is a fundamental measure of systemic risk and so is important in itself, but it is also the building block out of which measures of institutional leverage and household leverage can be most accurately and informatively constructed.Leverage, Loan to value, Margins, Haircuts, Monitor, Regulate, Leverage on new loans, Asset leverage, Investor leverage

Predatory Trading

This paper studies predatory trading: trading that induces and/or exploits other investors' need to reduce their positions. We show that if one trader needs to sell, others also sell and subsequently buy back the asset. This leads to price overshooting, and a reduced liquidation value for the distressed trader. Hence, the market is illiquid when liquidity is most needed. Further, a trader prots from triggering another trader's crisis, and the crisis can spill over across traders and across assetsasset pricing

Monitoring Leverage

We discuss how leverage can be monitored for institutions, individuals, and assets. While traditionally the interest rate has been regarded as the important feature of a loan, we argue that leverage is sometimes even more important. Monitoring leverage provides information about how risk builds up during booms as leverage rises and how crises start when leverage on new loans sharply declines. Leverage data is also a crucial input for crisis management and lending facilities. Leverage at the asset level can be monitored by down payments or margin requirement or and haircuts, giving a model-free measure that can be observed directly, in contrast to other measures of systemic risk that require complex estimation. Asset leverage is a fundamental measure of systemic risk and so is important in itself, but it is also the building block out of which measures of institutional leverage and household leverage can be most accurately and informatively constructed

Measuring systemic risk

We present a simple model of systemic risk and show how each financial institution’s contribution to systemic risk can be measured and priced. An institution’s contribution, denoted systemic expected shortfall (SES), is its propensity to be undercapitalized when the system as a whole is undercapitalized, which increases in its leverage, volatility, correlation, and tail-dependence. Institutions internalize their externality if they are “taxed” based on their SES. Through several examples, we demonstrate empirically the ability of components of SES to predict emerging systemic risk during the nancial crisis of 2007-2009.Systemic risk ; Risk

Betting Against Beta

We present a model in which some investors are prohibited from using leverage and other investors’ leverage is limited by margin requirements. The former investors bid up high-beta assets while the latter agents trade to profit from this, but must de-lever when they hit their margin constraints. We test the model’s predictions within U.S. equities, across 20 global equity markets, for Treasury bonds, corporate bonds, and futures. Consistent with the model, we find in each asset class that a betting-against-beta (BAB) factor which is long a leveraged portfolio of low-beta assets and short a portfolio of high-beta assets produces significant risk-adjusted returns. When funding constraints tighten, betas are compressed towards one, and the return of the BAB factor is low.

How Sovereign is Sovereign Credit Risk?

We study the nature of sovereign credit risk using an extensive sample of CDS spreads for 26 developed and emerging-market countries. Sovereign credit spreads are surprisingly highly correlated, with just three principal components accounting for more than 50 percent of their variation. Sovereign credit spreads are generally more related to the U.S. stock and high-yield bond markets, global risk premia, and capital flows than they are to their own local economic measures. We find that the excess returns from investing in sovereign credit are largely compensation for bearing global risk, and that there is little or no country-specific credit risk premium. A significant amount of the variation in sovereign credit returns can be forecast using U.S. equity, volatility, and bond market risk premia.

Carry Trades and Currency Crashes

This paper documents that carry traders are subject to crash risk: i.e. exchange rate movements between high-interest-rate and low-interest-rate currencies are negatively skewed. We argue that this negative skewness is due to sudden unwinding of carry trades, which tend to occur in periods in which risk appetite and funding liquidity decrease. Funding liquidity measures predict exchange rate movements, and controlling for liquidity helps explain the uncovered interest-rate puzzle. Carry-trade losses reduce future crash risk, but increase the price of crash risk. We also document excess co-movement among currencies with similar interest rate. Our findings are consistent with a model in which carry traders are subject to funding liquidity constraints.

Smartphone-Bases Reality Capture for Subsurface Utilities:Experiences from water utility companies in Denmark

Inaccurate and inconsistent documentation of subsurface utilities is a reoccurring problem in the construction industry affecting not only the end-users, but all actors involved in designing, constructing, and maintaining pipes, cables and other utilities hidden underground. In this study, a new method for 3D capturing of subsurface utilities, based on a newly developed Smartphone-based Reality Capture (RC) solution is explored. The research was divided into two parts. Firstly a testing of the method accuracy and secondly, an investigation of the usability of the method. The research results firstly showed that the RC solution is a feasible surveying method, that facilitate capturing of as-built utility assets, which can be used as a supporting tool to conventional surveying methods or alone, as the testing showed an accuracy of ±5 cm for the generated point clouds. Secondly the usability testing revealed that the RC solution benefited the utility owners by allowing time-savings on construction projects, as well as generating visual-realistic 3D models of exposed subsurface utilities to be used for quality assurance and planning of future utility work