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Credit boom detection methodologies (such as threshold method) lack robustness as they are based on univariate detrending analysis and resort to ratios of credit to real activity. I propose a quantitative indicator to detect atypical behavior of credit from a multivariate system - a monetary VAR. This methodology explicitly accounts for endogenous interactions between credit, asset prices and real activity and detects atypical credit expansions and contractions in the Euro Area, Japan and the U.S. robustly and timely. The analysis also proves useful in real time.
We show that the presence of high frequency trading (HFT) has significantly mitigated the frequency and severity of end-of-day price dislocation, counter to recent concerns expressed in the media. The effect of HFT is more pronounced on days when end of day price dislocation is more likely to be the result of market manipulation on days of option expiry dates and end of month. Moreover, the effect of HFT is more pronounced than the role of trading rules, surveillance, enforcement and legal conditions in curtailing the frequency and severity of end-of-day price dislocation. We show our findings are robust to different proxies of the start of HFT by trade size, cancellation of orders, and co-location.
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.
The analyses of intersectoral linkages of Leontief (1941) and Hirschman (1958) provide a natural way to study the transmission of risk among interconnected banks and to measure their systemic importance. In this paper we show how classic input-output analysis can be applied to banking and how to derive six indicators that capture different aspects of systemic importance, using a simple numerical example for illustration. We also discuss the relationship with other approaches, most notably network centrality measures, both formally and by means of a simulated network.
Can be a world order shaped by equivalents in the framework of the supranational model of Europe with the same legitimacy and with the same effectiveness? In this study was argued that Civilizing World Order (CWO) by Transnational Norm-Building Networks (TNNs) should have the legitimacy and effectiveness of the European Union supranational order. In this context, the concept of decentration (supra: centralization and infra: decentralization) which includes the nexus of voice (democratic participation) and entitlement (legal-social rights and duties) was examined. In this study as methodology published secondary data, online resources were used in order to reinforce the hypothesis.
We would study not style as such, but style 'at the scale of the sentence': the lowest level, it seemed, at which style as a distinct phenomenon became visible. Implicitly, we were defining style as a combination of smaller linguistic units, which made it, in consequence, particularly sensitive to changes in scale—from words to clauses to whole sentences.
We assess the effects of monetary policy on bank risk to verify the existence of a risk-taking channel - monetary expansions inducing banks to assume more risk. We first present VAR evidence confirming that this channel exists and tends to concentrate on the bank funding side. Then, to rationalize this evidence we build a macro model where banks subject to runs endogenously choose their funding structure (deposits vs. capital) and risk level. A monetary expansion increases bank leverage and risk. In turn, higher bank risk in steady state increases asset price volatility and reduces equilibrium output.
We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on results from the true model. We apply our method to a calibrated affine model and fine that relative wealth equivalent losses are below 1.16% if the jump size is stochastic and below 1% if the jump size is constant and γ ≥ 5. We perform robustness checks for various levels of risk-aversion, expected jump size, and jump intensity.
Regionale Wettbewerbsfähigkeit der Metropolregionen FrankfurtRheinMain und Stuttgart im Vergleich
(2013)
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.