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Institute
2013, 02
2013, 01
Sondierungsstudie im Auftrag des Bundesministeriums für Bildung und Forschung: Die jüngste Finanzkrise und die darauf folgende Staatsschuldenkrise hat sowohl wirtschaftlich als auch gesellschaftlich tiefgreifende Spuren hinterlassen. Dabei wurden auch sehr deutliche Lücken in der Forschung offenbar. Ziel dieser Studie ist es, aufbauend auf dem aktuellen Forschungsstand weiteren Forschungsbedarf in den wesentlich mit Finanzkrisen verbundenen Bereichen aufzuzeigen. Es werden fünf Forschungsbereiche mit jeweiligen Unterthemen vorgeschlagen. Diese fünf Forschungsbereiche gehen unmittelbar aus der Struktur und den Mechanismen der Finanz- und Staatsschuldenkrise hervor. Dabei wird besonderes Augenmerk auf die wirtschafts- und regulierungspolitische Relevanz der Themen sowie dem Umstand getragen, dass die Beantwortung vieler der Fragen interdisziplinäre Zusammenarbeit erfordert.
Finanzkrisen sind inherent mit dem Bankenmodell verbunden. Aufgrund von Verbindungen der Banken untereinander können Probleme einzelner Institute auf andere Institute übertragen werden. Diese systemischen Risiken können das gesamte Finanzsystem destabilisieren. Das Finanzsystem nimmt durch die Kreditvergabe und Bereitstellung von Transaktionssystemen eine herausragende Stellung in einer Volkswirtschaft ein, wodurch stabilisierende Eingriffe der Politik notwendig werden können. Eingriffe zur Wiederherstellung von Stabilität können sehr kostspielig sein und, wie aktuell eindrucksvoll belegt, die stabilisierenden Staaten selbst destabilisieren. Die alternativen Eingriffe vorab betreffen neben der Geldpolitik vor allem regulatorische Eingriffe. Im besonderen sind die Corporate Governance von Finanzinstituten und die Informationsbereitstellung bzw. Transparenz innerhalb des Finanzsektors von Bedeutung. In den vergangen Jahren wuchs vor dem Hintergrund von Regulierung zudem ein paralleles Schattenbankensystems heran, das in seiner Bedeutung dem traditionellen Bankensystem nur unwesentlich nachsteht.
Zwar sind die groben Zusammenhänge und Auswirkungen in den einzelnen Bereichen bekannt, jedoch ist für ein tiefgreifendes Verständnis als Grundlage zur Vermeidung bzw. Eindämmung zukünftiger Krisen sowie zur Folgenabschätzung von Regulierung weitere Forschung unabdingbar.
2013, 09
Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis
(2013)
Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date. This approach is based on the premise that demand for crude oil derives from the demand for refined products such as gasoline or heating oil. Oil industry analysts such as Philip Verleger and financial analysts widely believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. Our objective is to evaluate this proposition. We derive from first principles a number of alternative forecasting model specifications involving product spreads and compare these models to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and heating oil spot spreads that allows the marginal product market to change over time. We document MSPE reductions as high as 20% and directional accuracy as high as 63% at the two-year horizon, making product spread models a good complement to forecasting models based on economic fundamentals, which work best at short horizons.
2013, 19
We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks.
2013, 22
The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency realtime VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.
2013, 10
U.S. retail food price increases in recent years may seem large in nominal terms, but after adjusting for inflation have been quite modest even after the change in U.S. biofuel policies in 2006. In contrast, increases in the real prices of corn, soybeans, wheat and rice received by U.S. farmers have been more substantial and can be linked in part to increases in the real price of oil. That link, however, appears largely driven by common macroeconomic determinants of the prices of oil and agricultural commodities rather than the pass-through from higher oil prices. We show that there is no evidence that corn ethanol mandates have created a tight link between oil and agricultural markets. Rather increases in food commodity prices not associated with changes in global real activity appear to reflect a wide range of idiosyncratic shocks ranging from changes in biofuel policies to poor harvests. Increases in agricultural commodity prices in turn contribute little to U.S. retail food price increases, because of the small cost share of agricultural products in food prices. There is no evidence that oil price shocks have caused more than a negligible increase in retail food prices in recent years. Nor is there evidence for the prevailing wisdom that oil-price driven increases in the cost of food processing, packaging, transportation and distribution are responsible for higher retail food prices. Finally, there is no evidence that oil-market specific events or for that matter U.S. biofuel policies help explain the evolution of the real price of rice, which is perhaps the single most important food commodity for many developing countries.
2013, 18
Does it pay to invest in art? A selection-corrected returns perspective : [draft october 15, 2013]
(2013)
This paper shows the importance of correcting for sample selection when investing in illiquid assets with endogenous trading. Using a large sample of 20,538 paintings that were sold repeatedly at auction between 1972 and 2010, we find that paintings with higher price appreciation are more likely to trade. This strongly biases estimates of returns. The selection-corrected average annual index return is 6.5 percent, down from 10 percent for traditional uncorrected repeat sales regressions, and Sharpe Ratios drop from 0.24 to 0.04. From a pure financial perspective, passive index investing in paintings is not a viable investment strategy once selection bias is accounted for. Our results have important implications for other illiquid asset classes that trade endogenously.
2013, 15
We examine the impact of stock exchange trading rules and surveillance on the frequency and severity of suspected insider trading cases in 22 stock exchanges around the world over the period January 2003 through June 2011. Using new indices for market manipulation, insider trading, and broker-agency conflict based on the specific provisions of the trading rules of each stock exchange, along with surveillance to detect non-compliance with such rules, we show that more detailed exchange trading rules and surveillance over time and across markets significantly reduce the number of cases, but increase the profits per case.
2013, 20
We propose the realized systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market as well as balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential supervision.
2013, 14
We use responses to survey questions in the 2010 Italian Survey of Household Income and Wealth that ask consumers how much of an unexpected transitory income change they would consume. We find that the marginal propensity to consume (MPC) is 48 percent on average, and that there is substantial heterogeneity in the distribution. We find that households with low cash-on-hand exhibit a much higher MPC than affluent households, which is in agreement with models with precautionary savings where income risk plays an important role. The results have important implications for the evaluation of fiscal policy, and for predicting household responses to tax reforms and redistributive policies. In particular, we find that a debt-financed increase in transfers of 1 percent of national disposable income targeted to the bottom decile of the cash-on-hand distribution would increase aggregate consumption by 0.82 percent. Furthermore, we find that redistributing 1% of national disposable income from the top to the bottom decile of the income distribution would boost aggregate consumption by 0.33%.