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The global financial crisis and the ensuing criticism of macroeconomics have inspired researchers to explore new modeling approaches. There are many new models that deliver improved estimates of the transmission of macroeconomic policies and aim to better integrate the financial sector in business cycle analysis. Policy making institutions need to compare available models of policy transmission and evaluate the impact and interaction of policy instruments in order to design effective policy strategies. This paper reviews the literature on model comparison and presents a new approach for comparative analysis. Its computational implementation enables individual researchers to conduct systematic model comparisons and policy evaluations easily and at low cost. This approach also contributes to improving reproducibility of computational research in macroeconomic modeling. Several applications serve to illustrate the usefulness of model comparison and the new tools in the area of monetary and fiscal policy. They include an analysis of the impact of parameter shifts on the effects of fiscal policy, a comparison of monetary policy transmission across model generations and a cross-country comparison of the impact of changes in central bank rates in the United States and the euro area. Furthermore, the paper includes a large-scale comparison of the dynamics and policy implications of different macro-financial models. The models considered account for financial accelerator effects in investment financing, credit and house price booms and a role for bank capital. A final exercise illustrates how these models can be used to assess the benefits of leaning against credit growth in monetary policy.
Amid increasing regulation, structural changes of the market and Quantitative Easing as well as extremely low yields, concerns about the market liquidity of the Eurozone sovereign debt markets have been raised. We aim to quantify illiquidity risks, especially such related to liquidity dry-ups, and illiquidity spillover across maturities by examining the reaction to illiquidity shocks at high frequencies in two ways:
a) the regular response to shocks using a variance decomposition and,
b) the response to shocks in the extremes by detecting illiquidity shocks and modeling those as ultivariate Hawkes processes.
We find that:
a) market liquidity is more fragile and less predictable when an asset is very illiquid and,
b) the response to shocks in the extremes is structurally different from the regular response.
In 2015 long-term bonds are less liquid and the medium-term bonds are liquid, although we observe that in the extremes the medium-term bonds are increasingly driven by illiquidity spillover from the long-term titles.
Most defined contribution pension plans pay benefits as lump sums, yet the US Treasury has recently encouraged firms to protect retirees from outliving their assets by converting a portion of their plan balances into longevity income annuities (LIA). These are deferred annuities which initiate payouts not later than age 85 and continue for life, and they provide an effective way to hedge systematic (individual) longevity risk for a relatively low price. Using a life cycle portfolio framework, we measure the welfare improvements from including LIAs in the menu of plan payout choices, accounting for mortality heterogeneity by education and sex. We find that introducing a longevity income annuity to the plan menu is attractive for most DC plan participants who optimally commit 8-15% of their plan balances at age 65 to a LIA that starts paying out at age 85. Optimal annuitization boosts welfare by 5-20% of average retirement plan accruals at age 66 (assuming average mortality rates), compared to not having access to the LIA. We also compare the optimal LIA allocation versus two default options that plan sponsors could implement. We conclude that an approach where a fixed fraction over a dollar threshold is invested in LIAs will be preferred by most to the status quo, while enhancing welfare for the majority of workers.
Systemic co-jumps
(2016)
The simultaneous occurrence of jumps in several stocks can be associated with major financial news, triggers short-term predictability in stock returns, is correlated with sudden spikes of the variance risk premium, and determines a persistent increase (decrease) of stock variances and correlations when they come along with bad (good) news. These systemic events and their implications can be easily overlooked by traditional univariate jump statistics applied to stock indices. They are instead revealed in a clearly cut way by using a novel test procedure applied to individual assets, which is particularly effective on high-volume stocks.
This paper addresses whether and to what extent econometric methods used in experimental studies can be adapted and applied to financial data to detect the best-fitting preference model. To address the research question, we implement a frequently used nonlinear probit model in the style of Hey and Orme (1994) and base our analysis on a simulation stud. In detail, we simulate trading sequences for a set of utility models and try to identify the underlying utility model and its parameterization used to generate these sequences by maximum likelihood. We find that for a very broad classification of utility models, this method provides acceptable outcomes. Yet, a closer look at the preference parameters reveals several caveats that come along with typical issues attached to financial data, and that some of these issues seems to drive our results. In particular, deviations are attributable to effects stemming from multicollinearity and coherent under-identification problems, where some of these detrimental effects can be captured up to a certain degree by adjusting the error term specification. Furthermore, additional uncertainty stemming from changing market parameter estimates affects the precision of our estimates for risk preferences and cannot be simply remedied by using a higher standard deviation of the error term or a different assumption regarding its stochastic process. Particularly, if the variance of the error term becomes large, we detect a tendency to identify SPT as utility model providing the best fit to simulated trading sequences. We also find that a frequent issue, namely serial correlation of the residuals, does not seem to be significant. However, we detected a tendency to prefer nesting models over nested utility models, which is particularly prevalent if RDU and EXPO utility models are estimated along with EUT and CRRA utility models.
Microeconomic modeling of investors behavior in financial markets and its results crucially depends on assumptions about the mathematical shape of the underlying preference functions as well as their parameterizations. With the purpose to shed some light on the question, which preferences towards risky financial outcomes prevail in stock markets, we adopted and applied a maximum likelihood approach from the field of experimental economics on a randomly selected dataset of 656 private investors of a large German discount brokerage firm. According to our analysis we find evidence that the majority of these clients follow trading pattern in accordance with Prospect Theory (Kahneman and Tversky (1979)). We also find that observable sociodemographic and personal characteristics such as gender or age don't seem to correlate with specific preference types. With respect to the overall impact of preferences on trading behavior, we find a moderate impact of preferences on trading decisions of individual investors. A classification of investors according to various utility types reveals that the strength of the impact of preferences on an investors' rading behavior is not connected to most personal characteristics, but seems to be related to round-trip length.
Shortcomings revealed by experimental and theoretical researchers such as Allais (1953), Rabin (2000) and Rabin and Thaler (2001) that put the classical expected utility paradigm von Neumann and Morgenstern (1947) into question, led to the proposition of alternative and generalized utility functions, that intend to improve descriptive accuracy. The perhaps best known among those alternative preference theories, that has attracted much popularity among economists, is the so called Prospect Theory by Kahneman and Tversky (1979) and Tversky and Kahneman (1992). Its distinctive features, governed by its set of risk parameters such as risk sensitivity, loss aversion and decision weights, stimulated a series of economic and financial models that build on the previously estimated parameter values by Tversky and Kahneman (1992) to analyze and explain various empirical phenomena for which expected utility doesn't seem to offer a satisfying rationale. In this paper, after providing a brief overview of the relevant literature, we take a closer look at one of those papers, the trading model of Vlcek and Hens (2011) and analyze its implications on Prospect Theory parameters using an adopted maximum likelihood approach for a dataset of 656 individual investors from a large German discount brokerage firm. We find evidence that investors in our dataset are moderately averse to large losses and display high risk sensitivity, supporting the main assumptions of Prospect Theory.
Ongoing demographic change will lead to a relative scarcity of raw labor to the effect that output growth will be decreasing in the next decades, a secular stagnation. As physical capital will be relatively abundant, this decrease of output will be accompanied by reductions of asset returns. We quantify these effects for the US economy by developing an overlapping generations model with risky and risk-free assets. Without adjustments of human capital, risky returns decrease until 2035 by about 0.7 percentage point, and the risk-free rate by about one percentage point, leading to substantial welfare losses for asset rich households. Per capita output is reduced by 6%. Endogenous human capital adjustments strongly mitigate these effects. We conclude that human capital policies will be crucial in the context of labor shortages.
We study whether the presence of low-latency traders (including high-frequency traders (HFTs)) in the pre-opening period contributes to market quality, defined by price discovery and liquidity provision, in the opening auction. We use a unique dataset from the Tokyo Stock Exchange (TSE) based on server-IDs and find that HFTs dynamically alter their presence in different stocks and on different days. In spite of the lack of immediate execution, about one quarter of HFTs participate in the pre-opening period, and contribute significantly to market quality in the pre-opening period, the opening auction that ensues and the continuous trading period. Their contribution is largely different from that of the other HFTs during the continuous period.
The equity trading landscape all over the world has changed dramatically in recent years. We have witnessed the advent of new trading venues and significant changes in the market shares of existing ones. We use an extensive panel dataset from the European equity markets to analyze the market shares of five categories of lit and dark trading mechanisms. Market design features, such as minimum tick size, immediacy and anonymity; market conditions, such as liquidity and volatility; and the informational environment have distinct implications for order routing decisions and trading venues' resulting market shares. Furthermore, these implications differ distinctly for small and large trades, probably because traders jointly optimize their trade size and venue choice. Our results both confirm and go beyond current theoretical predictions on trading in fragmented markets.