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We consider an economy where individuals privately choose effort and trade competitively priced securities that pay off with effort-determined probability. We show that if insurance against a negative shock is sufficiently incomplete, then standard functional form restrictions ensure that individual objective functions are optimized by an effort and insurance combination that is unique and satisfies first- and second-order conditions. Modeling insurance incompleteness in terms of costly production of private insurance services, we characterize the constrained inefficiency arising in general equilibrium from competitive pricing of nonexclusive financial contracts.
We propose a new classification of consumption goods into nondurable goods, durable goods and a new class which we call “memorable” goods. A good is memorable if a consumer can draw current utility from its past consumption experience through memory. We construct a novel consumption-savings model in which a consumer has a well-defined preference ordering over both nondurable goods and memorable goods. Memorable goods consumption differs from nondurable goods consumption in that current memorable goods consumption may also impact future utility through the accumulation process of the stock of memory. In our model, households optimally choose a lumpy profile of memorable goods consumption even in a frictionless world. Using Consumer Expenditure Survey data, we then document levels and volatilities of different groups of consumption goods expenditures, as well as their expenditure patterns, and show that the expenditure patterns on memorable goods indeed differ significantly from those on nondurable and durable goods. Finally, we empirically evaluate our model’s predictions with respect to the welfare cost of consumption fluctuations and conduct an excess-sensitivity test of the consumption response to predictable income changes. We find that (i) the welfare cost of household-level consumption fluctuations may be overstated by 1.7 percentage points (11.9% points as opposed to 13.6% points of permanent consumption) if memorable goods are not appropriately accounted for; (ii) the finding of excess sensitivity of consumption documented in important papers of the literature might be entirely due to the presence of memorable goods.
There is mounting evidence that retail investors make predictable, costly investment mistakes, including underinvestment, naïve diversification, and payment of excessive fund fees. Over the past thirty-five years, however, participant-directed 401(k) plans have largely replaced professionally managed pension plans, requiring unsophisticated retail investors to navigate the financial markets themselves. Policy-makers have struggled with regulatory interventions designed to improve the quality of investment decisions without a clear understanding of the reasons for investor mistakes. Absent such an understanding, it is difficult to design effective regulatory responses. This article offers a first step in understanding the investor decision-making process. We use an internet-based experiment to disentangle possible explanations for inefficient investment decisions. The experiment employs a simplified construct of an employee’s allocation among the options in a retirement plan coupled with technology that enables us to collect data on the specific information that investors choose to view. In addition to collecting general information about the process by which investors choose among mutual fund options, we employ an experimental manipulation to test the effect of an instruction on the importance of mutual fund fees. Pairing this instruction with simplified fee disclosure allows us to distinguish between motivation-limits and cognition-limits as explanations for the widespread findings that investors ignore fees in their investment decisions. Our results offer partial but limited grounds for optimism. On the one hand, within our simplified experimental construct, our subjects allocated more money, on average, to higher-value funds. Furthermore, subjects who received the fees instruction paid closer attention to mutual fund fees and allocated their investments into funds with lower fees. On the other hand, the effects of even a blunt fees instruction were limited, and investors were unable to identify and avoid clearly inferior fund options. In addition, our results suggest that excessive, naïve diversification strategies are driving many investment decisions. Although our findings are preliminary, they suggest valuable avenues for future research and important implications for regulation of retail investing.
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.
Model case procedures have some fundamentals in common with collective redress in civil law countries. This is particularly true in the field of investor protection which is highly regulated and marked by resulting enforcement failures, which led the German legislator to the enactment of the KapMuG and its recent amendment which highlight exemplary elements of model case procedure. A survey of the ongoing activities of the European Union in the area of collective redress and of its repercussions on the member state level therefore forms a suitable basis for the following analysis of the 2012 amendment of the KapMuG. It clearly brings into focus a shift from sector-specific regulation with an emphasis on the cross-border aspect of protecting consumers towards a “coherent approach” strengthening the enforcement of EU law. As a result, regulatory policy and collective redress are two sides of the same coin today. With respect to the KapMuG such a development brings about some tension between its aim to aggregate small individual claims as efficiently as possible and the dominant role of individual procedural rights in German civil procedure. This conflict can be illustrated by some specific rules of the KapMuG: its scope of application, the three-tier procedure of a model case procedure, the newly introduced notification of claims and the new opt-out settlement under the amended §§ 17-19.
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.
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.
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.
The 2011 European short sale ban on financial stocks: a cure or a curse? : [version 31 july 2013]
(2013)
Did the August 2011 European short sale bans on financial stocks accomplish their goals? In order to answer this question, we use stock options’ implied volatility skews to proxy for investors’ risk aversion. We find that on ban announcement day, risk aversion levels rose for all stocks but more so for the banned financial stocks. The banned stocks’ volatility skews remained elevated during the ban but dropped for the other unbanned stocks. We show that it is the imposition of the ban itself that led to the increase in risk aversion rather than other causes such as information flow, options trading volumes, or stock specific factors. Substitution effects were minimal, as banned stocks’ put trading volumes and put-call ratios declined during the ban. We argue that although the ban succeeded in curbing further selling pressure on financial stocks by redirecting trading activity towards index options, this result came at the cost of increased risk aversion and some degree of market failure.
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.