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We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix. As an empirical framework, we propose a flexible econometric setting for portfolio optimization under transaction costs, which incorporates parameter uncertainty and combines predictive distributions of individual models using optimal prediction pooling. We consider predictive distributions resulting from highfrequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others. Using data capturing several hundred Nasdaq stocks over more than 10 years, we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1=N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains.
A counterparty credit limit (CCL) is a limit imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures, their implementation restricts the liquidity that institutions can access in an otherwise centralized pool. We address the question of how this mechanism impacts trade prices and volatility, both empirically and via a new model of trading with CCLs. We find empirically that CCLs cause little impact on trade. However, our model highlights that in extreme situations, CCLs could serve to destabilize prices and thereby influence systemic risk.
Exploiting NASDAQ order book data and difference-in-differences methodology, we identify the distinct effects of trading pause mechanisms introduced on U.S. stock exchanges after May 2010. We show that the mere existence of such a regulation constitutes a safeguard which makes market participants behave differently in anticipation of a pause. Pauses tend to break local price trends, make liquidity suppliers revise positions, and enhance price discovery. In contrast, pauses do not have a “cool off” effect on markets, but rather accelerate volatility and bid-ask spreads. This implies a regulatory trade-off between the protective role of trading pauses and their adverse effects on market quality.
Effective market discipline incentivizes financial institutions to limit their risk-taking behavior, making it a key element for financial regulation. However, without adequate incentives to monitor and control the risk-taking behavior of financial institutions market discipline erodes. As a consequence, bailing out financial institutions, as happened unprecedentedly during the recent financial crisis, may impose indirect costs to financial stability if bailout expectations of investors change. Analyzing US data covering the period between 2004 and 2014, Hett und Schmidt (2017) find that market participants adjusted their bailout expectations in response to government interventions, undermining market discipline mechanisms. Given these findings, policymakers need to take into account the potential effects on market discipline when deciding about public support to troubled financial institutions in the future. Considering the parallelism of events and public responses during the financial crisis as well as the recent developments of Italian banks, these results not only concern the US, but also have important implications for European financial markets and policy makers.
This Chapter explores how an environment of persistent low returns influences saving, investing, and retirement behaviors, as compared to what in the past had been thought of as more “normal” financial conditions. Our calibrated lifecycle dynamic model with realistic tax, minimum distribution, and Social Security benefit rules produces results that agree with observed saving, work, and claiming age behavior of U.S. households. In particular, our model generates a large peak at the earliest claiming age at 62, as in the data. Also in line with the evidence, our baseline results show a smaller second peak at the (system-defined) Full Retirement Age of 66. In the context of a zero-return environment, we show that workers will optimally devote more of their savings to non-retirement accounts and less to 401(k) accounts, since the relative appeal of investing in taxable versus tax-qualified retirement accounts is lower in a low return setting. Finally, we show that people claim Social Security benefits later in a low interest rate environment.
Given rising life expectations around the world, it seems that old-age pension benefits will need to be cut and pension contributions boosted in many nations. Yet our research on old-age system reforms does not require raising mandatory retirement ages or contributions. Instead, we offer ways to enhance incentives for people to work longer and delay retirement. There are good reasons to incentivize older people to work longer and delay retirement. These include rising longevity, the shrinking workforce, and emerging evidence indicating that working longer can be associated with better mental and physical health for many people. Nevertheless, old age Social Security systems in many nations find that people tend to claim benefits early, usually leading to reduced benefits.In the United States, for instance, a majority of Americans claim their Social Security benefits at the earlier feasible age, namely 62, even though their monthly benefits would be 75% higher if they waited until age 70. To test whether this is the result of people underweighting the economic value of higher lifetime benefit streams, we examine whether people would claim later and work longer if they were rewarded with a lump sum instead of a higher lifetime benefit stream for deferring. Two arguments have been offered to explain early claiming. One is that workers claim early to avoid potentially “forfeiting” their deferred benefits should they die too soon (Brown et al., 2016). A second explanation is that many people underweight the economic value of lifetime benefit streams (Brown et al., 2017). This latter rationale motivates the present study.
People who delay claiming Social Security receive higher lifelong benefits upon retirement. We survey individuals on their willingness to delay claiming later, if they could receive a lump sum in lieu of a higher annuity payment. Using a moment-matching approach, we calibrate a lifecycle model tracking observed claiming patterns under current rules and predict optimal claiming outcomes under the lump sum approach. Our model correctly predicts that early claimers under current rules would delay claiming most when offered actuarially fair lump sums, and for lump sums worth 87% as much, claiming ages would still be higher than at present.
We test two hypotheses, based on sexual selection theory, about gender differences in costly social interactions. Differential selectivity states that women invest less than men in interactions with new individuals. Differential opportunism states that women’s investment in social interactions is less responsive to information about the interaction’s payoffs. The hypotheses imply that women’s social networks are more stable and path dependent and composed of a greater proportion of strong relative to weak links. During their introductory week, we let new university students play an experimental trust game, first with one anonymous partner, then with the same and a new partner. Consistent with our hypotheses, we find that women invest less than men in new partners and that their investments are only half as responsive to information about the likely returns to the investment. Moreover, subsequent formation of students’ real social networks is consistent with the experimental results: being randomly assigned to the same introductory group has a much larger positive effect on women’s likelihood of reporting a subsequent friendship.
SAFE Newsletter : 2017, Q1
(2017)
SAFE Newsletter : 2017, Q2
(2017)
The currrent debate on monetary and fiscal policy is heavily influenced by estimates of the equilibrium real interest rate. Beyer and Wieland re-estimate the U.S. equilibrium rate with the methodology of Laubach and Williams and further modifications. They provide new estimates for the United States, the euro area and Germany and subject them to sensitivity tests. Beyer and Wieland conclude that due to the great uncertainty and sensitivity, the observed decline in the estimates is not a reliable indicator of a need for expansionary monetary and fiscal policy. Yet, if those estimates are employed to determine the appropriate monetary policy stance, such estimates are better used together with the consistent estimate of the level of potential output.
This paper examines the welfare implications of rising temperatures. Using a standard VAR, we empirically show that a temperature shock has a sizable, negative and statistically significant impact on TFP, output, and labor productivity. We rationalize these findings within a production economy featuring long-run temperature risk. In the model, macro-aggregates drop in response to a temperature shock, consistent with the novel evidence in the data. Such adverse effects are long-lasting. Over a 50-year horizon, a one-standard deviation temperature shock lowers both cumulative output and labor productivity growth by 1.4 percentage points. Based on the model, we also show that temperature risk is associated with non-negligible welfare costs which amount to 18.4% of the agent's lifetime utility and grow exponentially with the size of the impact of temperature on TFP. Finally, we show that faster adaptation to temperature shocks results in lower welfare costs. These welfare benefits become substantially higher in the presence of permanent improvements in the speed of adaptation.
Coming early to the party
(2017)
We examine the strategic behavior of High Frequency Traders (HFTs) during the pre-opening phase and the opening auction of the NYSE-Euronext Paris exchange. HFTs actively participate, and profitably extract information from the order flow. They also post "flash crash" orders, to gain time priority. They make profits on their last-second orders; however, so do others, suggesting that there is no speed advantage. HFTs lead price discovery, and neither harm nor improve liquidity. They "come early to the party", and enjoy it (make profits); however, they also help others enjoy the party (improve market quality) and do not have privileges (their speed advantage is not crucial).
During the last IAIS Global Seminar in June 2017, IAIS disclosed the agenda for a gradual shift in the systemic risk assessment methodology from the current Entity Based Approach (EBA) to a new Activity Based Approach(ABA). The EBA, which was developed in the aftermath of the 2008/2009 financial crisis, defines a list of Global Systemically Important Insurers (G-SIIs) based on a pre-defined set of criteria related to the size of the institution. These G-SIIs are subject to additional regulatory requirements since their distress or disorderly failure would potentially cause significant disruption to the global financial system and economic activity. Even if size is still a needed element of a systemic risk assessment, the strong emphasis put on the too-big-to-fail approach in insurance, i.e. EBA, might be partially missing the underlying nature of systemic risk in insurance. Not only certain activities, including insurance activities such as life or non-life lines of business, but also common exposures or certain managerial practices such as leverage or funding structures, tend to contribute to systemic risk of insurers but are not covered by the current EBA (Berdin and Sottocornola, 2015). Therefore, we very much welcome the general development of the systemic risk assessment methodology, even if several important questions still need to be answered.
Fleckenstein et al. (2014) document that nominal Treasuries trade at higher prices than inflation-swapped indexed bonds, which exactly replicate the nominal cash flows. We study whether this mispricing arises from liquidity premiums in inflation-indexed bonds (TIPS) and inflation swaps. Using US data, we show that the level of liquidity affects TIPS, whereas swap yields include a liquidity risk premium. We also allow for liquidity effects in nominal bonds. These results are based on a model with a systematic liquidity risk factor and asset-specific liquidity characteristics. We show that these liquidity (risk) premiums explain a substantial part of the TIPS underpricing.
Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used Granger causality to detect the presence of contemporaneous links between financial institutions and, in turn, to obtain a network structure. Subsequent studies combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions: we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints; and we demonstrate that Granger causality should be combined with quantile-based causality when the focus is on risk propagation. The empirical evidence supports the latter claim.
We establish a benchmark result for the relationship between the loanable funds and the money-creation approach to banking. In particular, we show that both processes yield the same allocations when there is no uncertainty and thus no bank default. In such cases, using the much simpler loanable funds approach as a shortcut does not imply any loss of generality.
The impact of network connectivity on factor exposures, asset pricing and portfolio diversification
(2017)
This paper extends the classic factor-based asset pricing model by including network linkages in linear factor models. We assume that the network linkages are exogenously provided. This extension of the model allows a better understanding of the causes of systematic risk and shows that (i) network exposures act as an inflating factor for systematic exposure to common factors and (ii) the power of diversification is reduced by the presence of network connections. Moreover, we show that in the presence of network links a misspecified traditional linear factor model presents residuals that are correlated and heteroskedastic. We support our claims with an extensive simulation experiment.
The growth and popularity of defined contribution pensions, along with the government’s increasing attention to retirement plan costs and investment choices provided, make it important to understand how people select their retirement plan investments. This paper shows how employees in a large firm altered their fund allocations when the employer streamlined its pension fund menu and deleted nearly half of the offered funds. Using administrative data, we examine the changes in plan participant investment choices that resulted from the streamlining and how these changes might affect participants’ eventual retirement wellbeing. We show that streamlined participants’ new allocations exhibited significantly lower within-fund turnover rates and expense ratios, and we estimate this could lead to aggregate savings for these participants over a 20-year period of $20.2M, or in excess of $9,400 per participant. Moreover, after the reform, streamlined participants’ portfolios held significantly less equity and exhibited significantly lower risks by way of reduced exposures to most systematic risk factors, compared to their non-streamlined counterparts.
During the 1970s, industrial countries, including the US and continental Europa, experienced a combination of slow productivity growth and high unemplyoment. Subsequent research has shown that the standard model of unemployment actually gives counterfactual predictions. Motivated by the observation that the 1970s were also characterized by high and rising inflation, Tesfaselassie and Wolters examine the effect of growth on unemployment in the presence of nominal price rigidity.
The authors demonstrate that the effect of growth on unemployment may be positive or negative. Faster growth leads to lower unemployment if the rate of inflation is high enough. There is a threshold level of inflation below which faster growth leads to higher unemployment and above which faster growth leads to lower unemployment. The threshold level in turn depends on labor market characteristics, such as hiring efficiency, the job destruction rate, workers' relative bargaining power and the opportunity cost of work.