Refine
Year of publication
- 2015 (149) (remove)
Document Type
- Working Paper (115)
- Part of Periodical (11)
- Book (10)
- Contribution to a Periodical (5)
- Article (4)
- Doctoral Thesis (2)
- Bachelor Thesis (1)
- Periodical (1)
Is part of the Bibliography
- no (149)
Keywords
- Solvency II (4)
- Währungsunion (4)
- systemic risk (4)
- human capital (3)
- insurance (3)
- Banking Union (2)
- Basel III (2)
- Fiscal Union (2)
- Fiskalunion (2)
- Heterogeneous Agents (2)
Institute
- Wirtschaftswissenschaften (149) (remove)
We develop a dynamic recursive model where political and economic decisions interact, to study how excessive debt-GDP ratios affect political sustainability of prudent fiscal policies. Rent seeking groups make political decisions – to cooperate (or not) – on the allocation of fiscal budgets (including rents) and issuance of sovereign debt. A classic commons problem triggers collective fiscal impatience and excessive debt issuing, leading to a vicious circle of high borrowing costs and sovereign default. We analytically characterize debt-GDP thresholds that foster cooperation among rent seeking groups and avoid default. Our analysis and application helps in understanding the politico-economic sustainability of sovereign rescues, emphasizing the need for fiscal targets and possible debt haircuts. We provide a calibrated example that quantifies the threshold debt-GDP ratio at 137%, remarkably close to the target set for private sector involvement in the case of Greece.
Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks balance sheets is more complex than the single-layer-of-exposure paradigm. We use data on exposures between large European banks broken down by both maturity and instrument type to characterise the main features of the multiplex structure of the network of large European banks. This multiplex network presents positive correlated multiplexity and a high similarity between layers, stemming both from standard similarity analyses as well as a core-periphery analyses of the different layers. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a useful tool for banking regulators and supervisors. We use the dataset of exposures between large European banks to illustrate the proposed measures.
We present a network model of the interbank market in which optimizing risk averse banks lend to each other and invest in non-liquid assets. Market clearing takes place through a tâtonnement process which yields the equilibrium price, while traded quantities are determined by means of a matching algorithm. We compare three alternative matching algorithms: maximum entropy, closest matching and random matching. Contagion occurs through liquidity hoarding, interbank interlinkages and fire sale externalities. The resulting network configurations exhibits a core-periphery structure, dis-assortative behavior and low clustering coefficient. We measure systemic importance by means of network centrality and input-output metrics and the contribution of systemic risk by means of Shapley values. Within this framework we analyze the effects of prudential policies on the stability/efficiency trade-off. Liquidity requirements unequivocally decrease systemic risk but at the cost of lower efficiency (measured by aggregate investment in non-liquid assets); equity requirements tend to reduce risk (hence increase stability) without reducing significantly overall investment.
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 quantities known only in the true model. We apply our method to a calibrated affine model. Our findings are threefold: Jumps matter more, i.e. our approximation is less accurate, if (i) the expected jump size or (ii) the jump intensity is large. Fixing the average impact of jumps, we find that (iii) rare, but severe jumps matter more than frequent, but small jumps.
In an experimental setting in which investors can entrust their money to traders, we investigate how compensation schemes affect liquidity provision and asset prices. Investors face a trade-off between risk and return. At the benefit of a potentially higher return, they can entrust their money to a trader. However this investment is risky, as the trader might not be trustworthy. Alternatively, they can opt for a safe but low return. We study how subjects solve this trade-off when traders are either liable for losses or not, and when their bonuses are either capped or not. Limited liability introduces a conflict of interest because it makes traders value the asset more than investors. To limit losses, investors should thus restrict liquidity provision to force traders to trade at a lower price. By contrast, bonus caps make traders value the asset less than investors. This should encourage liquidity provision and decrease prices. In contrast to these predictions, we find that under limited liability investors contribute to asset price bubbles by increasing liquidity provision and that caps fail to tame bubbles. Overall, giving investors skin in the game fosters financial stability.
Do markets correct individual behavioral biases? In an experimental asset market, we compare the outcomes of a standard market economy to those of a an island economy that removed market interactions. We observe asset price bubbles in the market economy while prices are stable in the island economy. We also find that subjects took more risk following larger losses, resulting in larger prices and consistent with a gambling for resurrection motive. This motive can translate into bubbles in the market economy because higher prices increase average losses and thus reinforce the desire to resurrect. By contrast, the absence of such a strategic complementarity in island economies can explain the more stable outcome. These results suggest that markets do not correct behavioral biases, rather the contrary.
We investigate the relationship between anchoring and the emergence of bubbles in experimental asset markets. We show that setting a visual anchor at the fundamental value (FV) in the first period only is sufficient to eliminate or to significantly reduce bubbles in laboratory asset markets. If no FV-anchor is set, bubble-crash patterns emerge. Our results indicate that bubbles in laboratory environments are primarily sparked in the first period. If prices are initiated around the FV, they stay close to the FV over the entire trading horizon. Our insights can be related to initial public offerings and the interaction between prices set on pre-opening markets and subsequent intra-day price dynamics.
We examine firms’ simultaneous choice of investment, debt financing and liquidity in a large sample of US corporates between 1980 and 2014. We partition the sample according to the firms’ financial constraints and their needs to hedge against future shortfalls in operating income. In contrast to earlier work, our joint estimation approach shows that cash flows affect the corporate decisions of unconstrained firms more strongly than those of constrained firms. Investment-cash flow sensitivities are particularly intense for unconstrained firms with high hedging needs. Investment opportunities (as proxied by Q), however, play a larger role for constrained firms with the effects being strongest in case of low hedging needs. Interestingly, constrained firms with low hedging needs are found to employ more debt to finance their investment opportunities and build up significant cash holdings at the same time. Our results hence indicate overinvestment behavior for unconstrained firms but no underinvestment for constrained firms if they have low hedging needs.
Some observers have conjectured that oil supply shocks in the United States and in other countries are behind the plunge in the price of oil since June 2014. Others have suggested that a major shock to oil price expectations occurred when in late November 2014 OPEC announced that it would maintain current production levels despite the steady increase in non-OPEC oil production. Both conjectures are perfectly reasonable ex ante, yet we provide quantitative evidence that neither explanation appears supported by the data. We show that more than half of the decline in the price of oil was predictable in real time as of June 2014 and therefore must have reflected the cumulative effects of earlier oil demand and supply shocks. Among the shocks that occurred after June 2014, the most influential shock resembles a negative shock to the demand for oil associated with a weakening economy in December 2014. In contrast, there is no evidence of any large positive oil supply shocks between June and December. We conclude that the difference in the evolution of the price of oil, which declined by 44% over this period, compared with other commodity prices, which on average only declined by about 5%-15%, reflects oil-market specific developments that took place prior to June 2014.
Although there is much interest in the future retail price of gasoline among consumers, industry analysts, and policymakers, it is widely believed that changes in the price of gasoline are essentially unforecastable given publicly available information. We explore a range of new forecasting approaches for the retail price of gasoline and compare their accuracy with the no-change forecast. Our key finding is that substantial reductions in the mean-squared prediction error (MSPE) of gasoline price forecasts are feasible in real time at horizons up to two years, as are substantial increases in directional accuracy. The most accurate individual model is a VAR(1) model for real retail gasoline and Brent crude oil prices. Even greater reductions in MSPEs are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the EIA gasoline price forecasts and the gasoline price expectations in the Michigan Survey of Consumers. We also show that as much as 39% of the decline in gas prices between June and December 2014 was predictable.