Search

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a Fama-Macbeth-type predictive performance evaluation. In addition to robust predictability evidence, there are four main findings. First of all, we find the lagged corporate bond market return as the most important predictor, suggesting a short-term market reversal story. Second, this paper concludes that equity information is conditionally redundant for similar public and private company bond performance. Third, a model-forecast-implied long-short strategy delivers 1.48% monthly returns and 1.4% alpha during the last two decades, which substantially drops if we do not consider private company bonds. Finally, the return predictability is mainly due to the cash flow component instead of the discount rate component.

Large-scale shocks directly affect some firms and households and indirectly affect others through general equilibrium spillovers. In this paper, I describe how researchers can directly estimate spillovers using quasi-experimental or experimental variation. I then argue that spillover estimates suffer from distinct sources of mechanical bias that standard empirical tools cannot resolve. These biases are particularly relevant in finance and macroeconomics, where multiple spillover channels and nonlinear effects are common. I offer guidance on how to detect and overcome mechanical biases. An application and several examples highlight that the suggested methods are broadly relevant and can inform policy and multiplier calculations. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Despite the growing interest in global inequality, assessing inequality trends is a major challenge becauseindividual data on income or consumption is not often available. Nevertheless, the periodic release of cer-tain summary statistics of the income distribution has become increasingly common. Hence, groupeddata in form of income shares have been conventionally used to construct inequality trends based onlower bound approximations of inequality measures. This approach introduces two potential sourcesof measurement error: first, these estimates are constructed under the assumption of equality of incomeswithin income shares; second, the highest income earners are not included in the household surveysfrom which grouped data is obtained. In this paper, we propose to deploy a flexible parametric model,which addresses these two issues in order to obtain a reliable representation of the income distributionand accurate estimates of inequality measures. This methodology is used to estimate the recent evolutionof global interpersonal inequality from 1990 to 2015 and to examine the effect of survey under-coverageof top incomes on the level and direction of global inequality. Overall, we find that item non-response atthe top of the distribution substantially biases global inequality estimates, but, more importantly, itmight also affect the direction of the trends. The authors hereby acknowledge UNU-WIDER and the project World Inequality where an earlier version of this study was published. The authors are grateful to Stephen Jenkins, Branko Milanovic, Nora Lustig, Juan Gabriel Rodriguez, Gustavo Marrero, Roy Van der Weide and participants at the UM Sustainability and Development Conference, Seventh ECINEQ Meeting, the 33th Annual Congress of the European Economic Association, and UNU-WIDER internal seminar series for helpful comments on earlier versions of this paper. Vanesa Jorda wishes to acknowledge financial support from the Ministerio de Economía y Competitividad (Project ECO2016-76203-C2-1-P).

The ambition of this paper is to catch hidden information inside the Securities and Exchange Commission’s (SEC)13F public holding data in order to construct an equity portfolio that maximizes returns. The 13F ling data give us the quarterly stock trading decisions of included funds, but we’re not given any insight on how they made their decisions or if information has been shared between funds. To remedy this lack of knowledge, this paper used feature extraction in order to lter out the best performing funds through several criteria. We propose a method employing powerful machine learning techniques (Deep Reinforcement Learning) to try to catch the missing pieces of information behind the decision process and use them as a prediction tool to construct quarterly equity portfolios. This approach reached an annualized return of 21% with a sharpe ratio of 1.8 outperforming the S&P 500 both in returns and stability through historical backtesting.

Stochastic processes is one of the key operations research tools for analysis of complex phenomenon. This paper has a unique application to the study of mean changing models in stock markets. The idea is to enter and exit stock markets like Apple Computer and the broad S&P500 index at good times and prices (long and short). Research by Chopra and Ziemba showed that mean estimation was far more important to portfolio success than variance or co-variance estimation. The idea in the stochastic process model is to determine when the mean changes and then reverse the position direction. This is applied to Apple Computer stock in 2012 when it rallied dramatically then had a large fall and Apple Computer and the S&P500 in the 2020 Covid-19 era. The results show that the mean changing model greatly improves on a buy and hold strategy even for securities that have has large gains over time but periodic losses which the model can exploit. This type of model is also useful to exit bubble-like stock markets and a number of these in the US, Japan, China and Iceland are described. An innovation in this paper is the exit entry long short feature which is important in financial markets.

Bartik regressions use locations' differential exposure to nationwide sector-level shocks as an instrument to estimate the effect of a location-level treatment on an outcome. In the canonical Bartik design, locations' differential exposure to industry-level employment shocks are used as an instrument to measure the effect of their employment evolution on their wage evolution. Some recent papers studying Bartik designs have assumed that the sector-level shocks are exogenous and all have the same expectation. This second assumption may sometimes be implausible. For instance, there could be industries whose employment is more likely to grow than that of other industries. We replace that second assumption by parallel trends assumptions. Under our assumptions, Bartik regressions identify weighted sums of location-specific effects, with weights that may be negative. Accordingly, such regressions may be misleading in the presence of heterogeneous effects, an issue that was not present under the assumptions maintained in previous papers. Estimating the weights attached to Bartik regressions is a way to assess their robustness to heterogeneous effects. We also propose an alternative estimator that is robust to location-specific effects. Finally, we revisit two applications. In both cases, Bartik regressions have fairly large negative weights attached to them. Our alternative estimator is substantially different from the Bartik regression coefficient in one application.

Standard Fama-French-Carhart models define ‘winners’ as those funds that generate highest excess returns given the factor risks involved; however they do not provide information on whether such winners are outperforming their prospectus benchmark or their peer-group. In addition, existing literature relying on these models by and large does not find evidence of persistence in performance. In this paper, we argue that true (unbiased) winners should be defined differently: they are funds placed in the top-ranking group (quartile, quintile, etc.), which generate the highest factor-risk-adjusted performance relative to the benchmark and the peer-group simultaneously. We prove in this paper that using this definition and selecting true winner funds based on benchmark- and peer-group-adjusted alphas jointly lead to better performance than selecting the funds using either of these two alphas separately. Utilising both adjustments at the same time results in a strong predictive ability, leading to a selection of funds that persist in performance: our true winner funds have statistically significantly superior benchmark-adjusted alphas, peer-group adjusted alphas and Sharpe ratios one-year-ahead, which are significantly different from those generated by the true loser funds. The results are robust to extended investment horizon, and alpha estimation method, and they are not driven by outliers, size of fund-sorts, or any particular period within our sample.

This paper revisits the issue of estimating the returns to schooling within a framework that allows multiple unobserved skills with potentially time-varying prices where both skills and prices are possibly correlated with schooling. A common approach to addressing the problem of ability bias emanating from the unobservability of skills is to augment the earnings-schooling regression with various measures of cognitive and non-cognitive ability as proxies for these skills. While the adequacy of such measures has long been questioned, a rigorous assessment of their effectiveness in estimating the magnitude of ability bias has received relatively less attention. This paper undertakes a formal evaluation of the proxy approach by adopting a methodology that models the skills and their prices using an unobserved factor structure in which the factor loadings represent the latent skills and the common factors their associated prices. Our factor model approach allows consistent estimation of the returns to schooling without the need to rely on proxies thereby providing a flexible test bed for quantifying the contribution of the proxies to the aggregate least squares bias. The factor model estimators may also be viewed as implicitly estimating the measurement error inherent in the proxies. Our empirical results using panel data from the NLSY79 show that the estimated ability bias lies between 39-51% for our most general specification. A bias decomposition analysis indicates that the commonly used proxies can explain very little of the estimated bias. Direct tests for the viability of the proxies further confirm their inadequacy in capturing the underlying latent skills.

This paper empirically examines the relationship between economic growth and structural change in India over recent decades. It first estimates panel regressions with state-level data, to examine the impact of structural change on growth and vice versa. Bidirectional causality is found, which holds across different specifications and estimation methods: each variable positively impacts the other, in a positive feedback loop. The second part of the analysis relaxes assumptions of constant impacts across states and over time, estimating time-varying auto-regressive models for each state and each pair of variables. The impacts of structural change and growth on each other seem to vary across different states, as well as over time, and are differentially affected by state per capita income levels and national growth rates. Similar variability across states and over time is found in an alternative method: labor productivity decompositions using employment data. Some possible reasons for these differences are discussed. The analysis suggests that national economic policy formulation may benefit from closer consideration of the structural differences across states, and the resulting differences in growth responses. The paper concludes with some possible implications for post-pandemic recovery.

This paper analyzes the use of Reinforcement Learning in trading Agricultural ETFs. The first section of the paper examines the performance of using a naive trading strategy to be used in a later comparison with Reinforcement Learning strategies. Here, returns were very volatile, with agents both severely under- and over-performing index benchmarks. The second part of this paper examines the use of the Monte Carlo Policy Gradient algorithm in trading. Finally, results of performing Dynamic Time Warping are presented for side-by-side comparisons between naive trading and trading using reinforcement learning algorithms. Overall, reinforcement learning produces more volatile results with a positive Sharpe ratio as compared to naive trading, with negative average returns.

Corporate FX risk management has gained complexity with an increased number of currencies involved and varying correlations among them. Existing literature has highlighted the need to account for cross-currency correlations when optimizing hedge ratios for portfolio management (Dowd, 1999). In this paper, we propose a Value-at-Risk (VaR) based model to estimate the optimal hedge ratio for a multi-national corporate that aims to minimize the cost of hedging at a given tolerance level of expected loss arising out of FX movement. The paper illustrates both parametric and historical methods of VaR estimation at a portfolio level as the first step in risk management. As a second step, an efficient-frontier is derived based on the expected VaR level at various hedge ratios and compared with associated hedge cost. The benefits of this approach include: identification of net exposures after correlations among currencies are accounted for in order to avoid duplication of hedges, and condensation of the parameters governing hedging decision into a single, intuitively-appealing number. The paper also highlights the need to frequently update the model’s assumptions as currency correlations and corporate exposures remain dynamic. JEL Classification Codes: C10, F31, G32, M20.

The paper deals with the problem of determinants of the auction prices of the works of art. A special attention is given to the attributes of works of art i.e.: the name of artist, time of creation, format (size), theme, technique, signature and their influence on the level of auction prices. The author of the paper uses statistical methods to verify empirically the relationship between variables. The dependences have been explained by implementation of three methods ie.: analysis of variance ANOVA, multiple regression models and classification trees, with the use of the computer program STATISTICA. The analysis is based on the cross-section data on art auction results of several auction sessions in Poland. The empirical studies have shown that name of artist, year of creation and technique determine the auction prices of works of art. The influence of technique and signature is not important statistically.

We propose a new approach to automated market making (AMM) by synchronizing two constant function market makers (CFMM). Our methodology combines the advantages of each individual CFMM and reduces the downside risk for liquidity providers (LP). The approach can be extended to any combination of AMMs. The application of our technique described in this paper is suitable for assets with high volatility that can deviate from the entry price for LPs. The deviation risk, compared to a buy-and-hold strategy and often referred to as impermanent loss can counterbalance the benefit of liquidity mining, that is the earning of associated trading fees. We show in this paper how our approach provides a way to largely limit the impact of such risk and compare the behavior of the new AMM to the well known exchanges, such as Uniswap and Balancer.

Problem definition: This paper investigates the threshold effects on the consideration set formation for consumers and the associated pricing decisions for firms. Academic/practical relevance: In the class of random utility maximization choice models, consumers choose the alternative with the largest utility after resolving utility uncertainties for all options. However, in practice, consumers may not inspect all available alternatives due to bounded rationality or search cost. Methodology: This paper incorporates bounded rationality into consumer choice behavior: in the first stage, a representative consumer forms her consideration set by removing alternatives whose nominal utility is lower than the largest by a threshold; in the second stage, she examines all alternatives in her consideration set and chooses the one with the highest realized utility. Results: For the Gumbel-distributed random utilities, we derive the two-stage threshold multinomial logit model with a consideration set. For the pricing problem, we show the quasi-same-markup/same-utility policy is optimal: high-cost products charge the prices such that their profit markups are the same, and low-cost products charge prices such that their nominal utilities are the same. For the price competition, there may exist zero, one, two, or infinite Nash equilibria, depending on the magnitude of the threshold effects. Managerial implications: Our analysis shows that the consideration set and bounded rationality play an important role in consumer choice behavior, so they should be taken into account in firms’ decision making.

This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is assumed to be unknown. The main results of this paper indicate that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting.

To estimate the spatial spillover effect of China’s carbon emissions trading (CET) launched in 2013 on carbon emissions reduction, this paper conducts a quasi-natural experiment with the combination of difference-in-differences method (DID) and spatial doberman model (SDM). Moreover, this paper decomposes the policy effect into direct part and spatial spillover part, further analyzes the mechanism from the perspective of energy use, energy structure and energy efficiency. The empirical results show that: (1) From 2013 to 2016, carbon emissions in pilots achieve reduction significantly through CET. Both the results of the robustness test and the endogenous analysis support the above conclusion. (2) Carbon emissions in neighbouring region of pilots are also reduced, which proves the existence of spatial spillover effects from CET. Compared with direct part, the spatial spillover part accounted for smaller proportion in spatial carbon emissions reduction effect. (3) The emission reduction effect of CET continues to increase over the long term, while the spatial spillover effect gradually decreases as distance increases. (4) The decline of fossil energy use is main path to reduce carbon emissions, while the CET fails to increase the use of clean energy, optimize energy use structure or improve energy efficiency.

This paper takes a critical look at the effect of firm size on the profitability of Ghanaian Insurance brokerage firms. Specifically, the paper examined the effect of firm size (measured by total assets) on firm profitability (measured by ROA and ROE) from a lagged perspective (i.e. the lagged effect of size), non-linear perspective, and across quantiles, using fixed effects, random effects, robust and PCSE estimation techniques. Analyzing a unique data of 64 insurance brokers from 2007 to 2015, the findings show that firm size exhibited a significant and positive short term effect on firm profitability but the relationship turned negative in the long term showing a non-linear relationship of size on profitability, with an inflection point above the mean firm size. However, the non-linear effect was evident in the 50th and above percentile of brokers but not in the lower quantiles. The lagged values of size also significantly affected firm profitability but it was not as pronounced as the short-term effects. The study recommends larger Ghanaian insurance brokerage firms take a staggered and reflective approach in its growth measures.

Summary: Kermack and McKendrick’s SIR compartment model (1927) has been widely used to analyze various epidemic phenomena including the current COVID-19 pandemic. Unfortunately, this model is of limited use if the duration of the epidemic prolongs. This paper uses a slightly modified compartment model, the SEIR(D) model, to analyze the time-varying transmission dynamics of the COVID-19 epidemic in Korea in multiple stages of its development. This multi-stage estimation of the model parameters offers a better model fit and shows how the COVID-19’s transmission dynamics change over time, primarily depending on, the effectiveness of the public health authority’s non-pharmaceutical interventions (NPIs). Method: This paper uses the SEIR(D) compartment model to simulate and estimate the parameters for three distinctive stages of the COVID-19 epidemic in Korea, using a compiled COVID-19 dataset from Feb. 18, 2020 to Feb. 08, 2021. The paper identifies three distinct stages of the COVID-19 epidemic in Korea, conduct multi-stage estimations of model parameters, and carefully infer context-dependent meaning of the estimation results to better understand the unique characteristics of the transmission dynamics of the COVID-19 in each stage. Findings: The original SIR compartment model may produce a poor and even misleading result if it is used to cover the entire period of the epidemic, because underlying assumptions behind this model - i.e., homogeneity of the susceptible population and invariant parameters - are easily defied by the real-world complexity. However, if we use a carefully calibrated model in distinctive stages of the COVID-19 epidemic, we can find useful insights that we can use to evaluate the effectiveness of the government’s non-pharmaceutical interventions over time.

We provide the first at-scale estimate of electric vehicle (EV) home charging. Previous estimates are either based on surveys that reach conflicting conclusions, or are extrapolated from a small, unrepresentative sample of households with dedicated EV meters. We combine billions of hourly electricity meter measurements with address-level EV registration records from California households. The average EV increases overall household load by 2.9 kilowatt-hours per day, less than half the amount assumed by state regulators. Our results imply that EVs travel 5,300 miles per year, under half of the US fleet average. This raises questions about transportation electrification for climate policy. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A large number of academic papers claim that there is a consistent advantage to rebalancing an investment portfolio relative to a simple Buy&Hold strategy, while other academic papers claim the opposite. The papers on both sides are typically based on unclear mathematical arguments with only few real-world examples. This paper does not try to give a mathematical explanation but is entirely empirical. We use daily stock-returns for more than 900 U.S. stocks between the years 2007 and 2021, where we generate thousands of random portfolios of different sizes and start/end-dates, to compare the Rebalanced and Buy&Hold strategies on different performance metrics. Although the Rebalanced portfolios may seem to perform better than the Buy&Hold portfolios in terms of e.g. the arithmetic mean daily return, it actually depends on which period we consider. We also make preliminary experiments with intraday data, where it does appear that Rebalancing is consistently better than Buy&Hold, although these data-sets are so small that we cannot conclude this advantage holds in general.

While major stock market indices are followed by large monetary investments, we document that membership decisions for S&P 500 have a nontrivial amount of discretion. We show that firms’ purchases of S&P ratings appear to improve their chance of entering the index (but purchases of Moody’s ratings do not). Furthermore, firms tend to purchase more S&P ratings when there are openings in the index membership. Such a pattern is also confirmed by an event study that explores a rule change on index membership in 2002. Finally, discretionary additions exhibit subsequent deterioration in financial performance relative to rules-based additions. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We study theoretically and empirically the relationship between investor beliefs, ownership dispersion and stock returns. We find that high dispersion, measured by high breadth or low Herfindahl index, forecasts returns positively for large stocks, as in Chen, Hong and Stein (2002), but negatively for small stocks. We explain that relationship in a difference-of-opinion model in which stocks differ in the size of investor disagreements and the extent of belief polarization. These differences are characterized by range and kurtosis, respectively. Proxying investor beliefs by analyst forecasts, we find that range and kurtosis affect ownership dispersion in the way that our model predicts. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper estimates the volatility index term structure for the Spanish bank industry (SBVX) using the implied volatility of individual banks and assuming market correlation risk premium. This methodology enables calculating a volatility index for arbitrary (non-traded) portfolios. Using data from 2015 to 2021, we find that SBVX informs about the dynamics of bank returns beyond the standard market volatility index VIBEX, especially when bank returns are negative; and that one-year SBVX beats shorter maturities in explaining bank returns. On the other hand, positive bank returns relate to the dynamics of VIBEX just as much as SBVX, which aligns with the belief that a drop in global volatility (uncertainty) positively affects firm performance and, therefore, bank value projections. We find one-month SBVX better than VIBEX to forecast monthly bank returns volatility, regardless of the tenor we use to compute VIBEX. This paper provides empirical evidence that idiosyncratic implied volatility is just as significant, or even more than global volatility, to monitor current and future banks’ share price performance. We advise using SBVX term structure, short-term VIBEX, and market correlation risk premium to monitor uncertainty and returns in the banking sector and foresee periods of stress in this industry. Our results may be of great interest to those seeking to estimate the banking sector’s sensitivity to uncertainty, volatility, and risk.

The purpose of this paper is to present statistical analysis of the Environmental Kuznets Curve (EKC) theory, the hypothesized relationship between different environmental indicators, by analyzing empirical studies from existing literature. This paper describes the critical history and theory of the EKC, its theoretical foundations, and possible deviations with the focus on utilizing the Simultaneous Equation Model (SEM). The Kuznets Curve proposes that as an economy develops, the inequality will increase as the income per capita increases; however, it will then decrease and display an inverted U-shaped pattern. The goal is to assess if EKC aligns with the imperial analysis in the case of the Chinese economy as China has experienced the fastest economic growth over the past decade.

This paper bases long-term investing on a tradeable stochastic discount factor (SDF), relates it to the growth optimal portfolio and argues for a top-down method, where modeling efforts are directed at capturing its long-run dynamics in a generalized setting. This differs from the common, cumbersome bottom-up method of modeling many risky securities in the marketplace. Various optimal portfolio strategies can be implemented efficiently using fractional expectations of the SDF. This paper illustrates empirically for the US stock market that the proposed method leads to higher wealth, higher returns on investment and higher long-term utility levels.

This paper estimates the cross-sectional distribution of Epstein-Zin preference parameters in a large administrative panel of Swedish households. We consider life-cycle model of saving and portfolio choice that incorporates risky labor income, safe and risky financial assets inside and outside retirement accounts, and real estate. We study middle-aged stock-owning households grouped by education, industry of employment, and birth cohort as well as by their accumulated wealth and risky portfolio shares. We find some heterogeneity in risk aversion (a standard deviation of 0.47 around a mean of 5.24 and median of 5.30) and considerable heterogeneity in the time preference rate (standard deviation 6.0% around a mean of 6.2% and median of 4.1%) and elasticity of intertemporal substitution (standard deviation 0.96 around a mean of 0.99 and median of 0.42). Risk aversion and the EIS are almost cross-sectionally uncorrelated, in contrast with the strong negative correlation that we would find if households had power utility with heterogeneous risk aversion. The TPR is weakly negatively correlated with both the other parameters. We estimate lower risk aversion for households with riskier labor income and higher levels of education, and a higher TPR and lower EIS for households who enter our sample with low initial wealth. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2021 for 88 countries. We got answers for 92 countries, but we only report the results for 88 countries with more than 6 answers. Many respondents use for European countries a RF higher than the yield of the 10-year Government bonds. The coefficient of variation (standard deviation / average) of RF is higher than the coefficient of variation of MRP for the Euro countries. The paper also contains the links to previous years surveys, from 2008 to 2020.

This paper studies the effects of different productivity on current and future period output under a nonseparable production function with multiple productivity. This model relaxes two widely used assumptions in production function literature: the parametric production function structure and one single productivity. This paper establishes a novel nonparametric identification of all the unspecified elements. Concerning estimation, this paper proposes easy-to-implement kernel estimators and shows they are consistent and asymptotically normal by the functional delta method. The model is then applied to study Spanish firms' behaviors. I find that change in current period capital productivity plays a more crucial role than change in current period Hicksian productivity on current period output. Their effects on next period output become smaller and similar. Moreover, I find such effects depend on the levels of productivity.

In this paper we use risk-free short-term interest rates (OISTIRs) derived from equity derivatives prices to make the following four contributions: (i) We introduce and compare different methods how to extract these rates and show that a method based on box spreads performs best. (ii) We compare the results with other benchmark short-term interest rates and show that OISTIRs are higher than BUND rates (30 bps for 3-month tenors), lower than EURIBOR rates (16 bps for 3-month tenors), and almost equal to EUREX repo rates. (iii) In terms of informational sensitivity we find OISTIRs to Granger-cause EURIBOR rates, at least since the year 2013. (iv) We use OISTIRs to de- rive the BUND convenience yield. We identify bond offerings and expansive monetary policy decision to negatively impact these yields, while the risk-free maturity premium has a positive impact. Finally, we think this paper may also contribute to the discussion on how to further develop EURIBOR rates.

In this paper, we first develop some properties to state the relationships among central moments, stochastic dominance (SD), risk-seeking stochastic dominance (RSD), and integrals for the general utility functions and the polynomial utility functions of both risk averters and risk seekers. We then introduce the moment rule and establish the necessary and/or sufficient conditions between stochastic dominance and the moment rule for the general utility functions and the polynomial utility functions of both risk averters and risk seekers with and without the same-location-scale-family condition. Thereafter, we apply the moment rules to develop some properties of portfolio diversification for the general utility functions and the polynomial utility functions of both risk averters and risk seekers. The extensions in our paper enable academics and practitioners to draw preferences of both risk averters and risk seekers on their choices of the portfolios or assets by using different moments.

This paper determines whether road-fuel (gasoline plus diesel) income and price elasticities have changed over time in middle-income countries. To do so, the paper considers a balanced panel of 28 countries that spans 1990-2019. Also, the paper employs two methods that fully allow for cross-sectional heterogeneity, but vary to the extent that they allow for temporal heterogeneity: rolling window, mean group regressions and mean observation OLS, which estimates coefficients for each cross-section and each time period. While the elasticities demonstrate some temporal heterogeneity, such variances are less pronounced than the corresponding country-level heterogeneity. At any point in time, for middle-income countries, the average road-fuel income elasticity is between 1 and 0.8, and the average road-fuel price elasticity is very near -0.2. Lastly, we find no strong evidence that road-fuel demand has become saturated or that efficiency improvements have ma,de consumers less price sensitive in middle-income countries.

This paper presents the staggered difference-in-differences (DiD) method in an accessible language to a broad accounting research audience from an applied researcher's perspective. Specifically, the paper examines DiD design problems when multiple units are treated and when the treatment is conducted at staggered periods in time. Using the Goodman-Bacon decomposition, I show how heterogeneous treatment effects can bias the estimated treatment effect in a staggered DiD when estimated using a two-way fixed effect regression. Finally, using the staggered adoption of the 150-hour Rule as an example, I briefly review several proposals to adjust for the bias and correctly implement staggered DiD designs that a bourgeoning literature has put forward in econometrics.

Nirei and Scheinkman (2021) proposed an equilibrium model of price adjustments with menu-costs with a finite number of firms and derived a “reproduction number” for repricing and a limit functional form for the distribution of the number of simultaneously price-adjusting firms. We show that the distribution of price-changes in data from the Billion Prices Project is well fitted by this functional form and exhibits a reproduction number that is close to unity, indicating that complementarity in price-changes plays a major role in repricings. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative and narrative scenarios informed by expert judgment. At end-2020, risks from stock market corrections in the U.S. are concentrated in the energy, financial and technology sectors, and additional bank capital needs could be large. The paper concludes discussing uses of the mapping beyond PD valuation suitable for capital structure analysis, credit portfolio management, and long-term scenario planning analysis.

Estimation of the activities of the informal sector in an economy poses a serious problem for obtaining a correct estimate of GDP. This happens to be so because of the fact that informal sector activities are not registered. The literature has tried to solve the problem of estimation of the informal sector using the method of latent variables. The standard approach is generally aims to estimate for a single point. The present paper aims to model the statistical estimation of informal sector of many countries in a common framework and the evolution of the informal sector over time. Thus the adopted approach in this paper is panel estimation for a group of 22 countries, both from the developed and developing world. The study contributes in the literature by way of developing statistical model ‘Panel Dynamic Multiple Indicators and Multiple Causes’ model to account for the variations in the contribution of informal sector. Using the statistical model the paper presents modified estimates of the informal sector of the group of countries over a period ranging from 1990 through 2018. Our estimates of the informal sector are found to be generally higher, sometimes more than 10% for the developing country group while the estimates for the developed countries show little variability.

We develop an estimator and tests of a discrete time mixed proportional hazard (MPH) model of duration with unobserved heterogeneity. We allow for competing risks, observable characteristics, and censoring, and we use linear GMM, making estimation and inference straightforward. With repeated spell data, our estimator is consistent and robust to the unknown shape of the frailty distribution. We apply our estimator to the duration of price spells in weekly store data from IRI. We find substantial unobserved heterogeneity, accounting for a large fraction of the decrease in the Kaplan-Meier hazard with elapsed duration. Still, we show that the estimated baseline hazard rate is decreasing and a homogeneous firm model can accurately capture the response of the economy to a monetary policy shock even if there is significant strategic complementarity in pricing. Using competing risks and spell-specific observable characteristics, we separately estimate the model for regular and temporary price changes and find that the MPH structure describes regular price changes better than temporary ones. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper assesses different econometric approaches to working with count-based outcome variables and other outcomes with similar distributional properties, which are increasingly common in corporate finance applications. We demonstrate that the common practice of adding a constant to the outcome and then estimating log-linear regressions produces estimates with no natural interpretation that can easily have the wrong sign. In contrast, a simple fixed-effects Poisson model produces consistent and reasonably efficient estimates under more general conditions than commonly assumed. We also show through replication of existing papers that economic conclusions are frequently sensitive to the econometric approach employed.

The default of a participant at Nasdaq Clearing in 2018 and the recent COVID-19 events brought to the attention of risk managers at CCPs the importance of appropriately measuring correlation breakdowns. The sizable price dislocations registered on these occasions suggested that traditional risk models may not be fully equipped to capture the breakdowns. Because correlations are directly impacted by the statistical properties of each variable, any model that lacks the capacity to deal with non-stationarity may inappropriately represent correlation or its alterations. Using a GARCH-DCC approach to accommodate such properties, the objective of the paper is to study the correlation behaviour during adverse market conditions, and the potential subsequent impact to CCP margins. A study case for energy commodities is proposed, with the specific focus on spread positions for the electricity market. The analysis suggests that the correlation breakdowns are more frequent than traditionally expected. When different types of shocks are considered (i.e. September 2018 and March-May 2020), it becomes evident that while the magnitude of the breakdown may differ, its cycle presents a number of similarities. While elevated margin due to correlation breakdown may reduce breaching amount and improve margin coverage rate, this paper also recognizes the potentially increased margin procyclicality, and highlights the challenge of balancing margin responsiveness and stability during correlation breakdown for spread positions, and calls for further study in this area.

This paper evaluates how much chaos is generating COVID-19 globally. We try to present a new indicator is called “The Levels of Uncertainty by the COVID-19 index (TLOUC-Index)”. The main objective of TLOUC-Index is to evaluate how much health, economic, and social uncertainty can COVID-19 crisis is generating until our days. The construction of the TLUC-Index is using chaos theory and Econographicology simultaneously. The TLOUC-Index applies four independent variables followed by the total COVID-19 infected cases growth rate, the total COVID-19 active cases growth rate, the total COVID-19 deaths growth rate, and the total COVID-19 recovered cases growth rate. At the same time, an independent variable is calling the TLUC-Index (+/-Y*). Finally, this paper investigates severe issues and possible solutions against the COVID-19 crisis from a holistic approach.

The impact of technological progress on jobs and wages has been subject to much empirical and some theoretical work e.g. on skill-biased technical progress. Most of this literature has focused on two causal channels; the jobs-displacing substitution effect, and the job-creating demand effect. This paper shows that a further channel shapes the quantitative and qualitative effects of technical change. This is a general-equilibrium effect, depending on the interplay between the factors and sectors impacted by change. The paper integrates these three channels and derives explicit expressions for the effects of different types of technical change on wages and sectoral employment.

At the aggregate level, the evidence that deviations from purchasing power parity (PPP) are too persistent to be explained solely by nominal rigidities has long been a puzzle (Rogoff, 1996). Another puzzle from the micro price evidence of the law of one price (LOP), which is the basic building block of PPP, is that LOP deviations are less persistent than PPP deviations. To reconcile the empirical evidence, we adapt the model of behavioral inattention in Gabaix (2014, 2020) to a simple two-country sticky-price model. We propose a simple test of behavioral inattention and find strong evidence in its favor using micro price data from US and Canadian cities. Calibrating behavioral inattention with our estimates, we show that our model reconciles the two puzzles relating to the PPP and LOP. First, the PPP deviations are more than twice as persistent as PPP deviations explained only by sticky prices. Second, the LOP deviations decrease to less than two-thirds of the PPP deviations in the degree of persistence. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Using a sample of listed non-financial firms in China from 2009 to 2019, we show that financial derivatives usage increases the firms’ (discretionary) accruals earnings smoothing (artificial or intentional smoothing). We find that state ownership, overseas operations, and public scrutiny play the moderating roles between derivatives usage and earnings smoothing. Moreover, these three moderating factors have unambiguous interactions inter se: the mitigation effects of public scrutiny would be stronger for the firms owned by the state or the firms with overseas operations; and the private firms without overseas operations are the “worst-hit areas” of the earnings smoothing. In addition, our results imply that the futures might be the only type of derivatives that are used to smooth earnings. This paper also finds that the adoption of IFRS 7 and 13 in 2014 failed to mitigate the derivatives smoothing. Overall, our paper reveals the relation of derivatives usage to intentional earnings smoothing in China, which is partially contrary to the evidence in the U.S., and the detailed dynamics of the relation.

This paper proposes a reengineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (LVaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both long-only positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios with the aid of an appropriate scaling of the multiple assets’ LVaR matrix along with GARCH-M technique to forecast conditional volatility and expected return.

The design and conduct of climate change policy necessarily confronts uncertainty along multiple fronts. We explore the consequences of ambiguity over various sources and configurations of models that impact how economic opportunities could be damaged in the future. We appeal to decision theory under risk, model ambiguity and misspecification concerns to provide an economically motivated approach to uncertainty quantification. We show how this approach reduces the many facets of uncertainty into a low dimensional characterization that depends on the uncertainty aversion of a decision-maker or fictitious social planner. In our computations, we take inventory of three alternative channels of uncertainty and provide a novel way to assess them. These include i) carbon dynamics that capture how carbon emissions impact atmospheric carbon in future time periods; ii) temperature dynamics that depict how atmospheric carbon alters temperature in future time periods; iii) damage functions that quantify how temperature changes diminish economic opportunities. We appeal to geoscientific modeling to quantify the first two channels. We show how these uncertainty sources interact for a social planner looking to design a prudent approach to the social pricing of carbon emissions. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The innovation contest is well organized to provide solutions or ideas for companies. In the existing innovation contest literature, several papers assume that the contestants are risk neutral and show that a single-winner award scheme is optimal. In this paper, we assume that the contestants are risk averse and show that the risk aversion of contestants can explain the popularity of the multiple-winner, convex (unequal) allocation scheme, which generalizes the findings of a prior study and coincides with practical observations. This result also possesses a certain robustness in several cases.

This paper aims to find if the market-based measure of monetary policy expectations such as the Eurodollar future rates has predictive power in anticipating trend changes in Treasury curves. The report examines the empirical relationship between Eurodollar pack spreads and Treasury curves and develops multiple trading strategies using data from the previous eight years. The backtesting results of strategies implemented in this paper provide evidence that the inverted spread between the Eurodollar "Blue" pack and the "Green" pack has predictive power in anticipating long-term fluctuations across Tsy curves, significantly flattening fluctuations. Furthermore, the backtesting results of the constructed optimal portfolio provide evidence that the combination of technical trading signals in Eurodollar spreads and Tsy curves has a better trading performance than using only the Tsy curve technical trading signals.

This paper discusses the benefits and drawbacks of adopting Bayesian inference for practical equity valuation and fundamental analysis and derives a decision-focused valuation model for investors. Despite the inevitably subjectivist (i.e. Bayesian) nature of equity valuation, almost all research contributions on the topic follow a frequentist approach. In this paper, I perform a methodological discussion on equity valuation from the perspective of a fundamental investor and provide a starting point for a more investor-oriented equity valuation in both theory and practice. I argue that valuation theory alone supplies enough information to generate suitable, normally distributed weakly informative priors for long-term speculative variables. When combined with maximum-likelihood estimates using historical fundamentals and short-term estimates, these simple-to-implement priors should lead to more reliable value estimates. Using the specified prior and likelihood, I propose a Bayesian algorithm for the simulation of the posterior distribution of intrinsic value.

In this paper, we derive optimal hedging strategies for options in electricity futures markets. Optimality is measured in terms of minimal variance and the associated minimal variance hedging portfolios are obtained by a stochastic maximum principle. Our explicit results are particularly useful for electricity retailers, who have sold an option to a client, and now want to hedge the payoff of this option by investing into an electricity futures and into the issued option itself. Another innovative aspect of the paper lies in the derivation of the time dynamics of the stochastic option price process by Malliavin calculus methods and the Clark-Ocone formula. We also apply our theoretical results to several practical examples. Our investigations are based upon the popular arithmetic multi-factor electricity spot price model proposed by Benth, Kallsen & Meyer-Brandis (Appl. Math. Finance 14(2):153-169, 2007).

This paper quantifies the amount of noise and bias in analysts’ forecast of corporate earnings at various horizons. We first show analyst forecasts outperform statistical forecasts at short-horizons, but underperform at longer horizons. We next decompose the relative accuracy of these forecasts into three components: (i) noise, (ii) bias and (iii) analysts’ information advantage over statistical forecasts. We find the information advantage is constant across forecasting horizons, while both noise and bias are increase linearly. We then show most existing models lack a mechanism to account for these facts. To generate such a mechanism, we consider a parsimonious variant of the model of Patton and Timmermann (2010) with a noisy cognitive default and show it quantitatively fits the data. The intuition underlying this model is that forecasters rely on their biased and noisy defaults more at longer horizons, as rational forecasts are less accurate. This model also quantitatively matches two non-targeted empirical relationships: (i) analyst disagreement increases with horizon and (ii) noise is an increasing function of volatility. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper generalizes Ross (2015) recovery theory to accommodate growth, including the Black-Scholes and stochastic volatility option models. The new theory recovers information about equity risk premia and variance risk premia from options prices. In the Heston (1993) stochastic volatility model, the theory predicts an exact (negative) value for the variance risk premium as a function of the equity premium. Recovery theory also predicts that the stochastic discount factor is the reciprocal return on a model-free portfolio of index options. This paper tests the theory on returns from one-month VIX and three-month VIX3M option portfolios from 2007-2018. Recovery theory links the equity pre- mium to the values of both conditional and unconditional variance premia. It also predicts how VIX3M option variance is a biased predictor of future one-month VIX variance. Empirically, recovery theory simultaneously matches the average S&P 500 equity premium, the average variance premium, and observed biases in the variance expectations hypothesis. Autocorrelation properties of VIX indices imply a 12% annual equity premium.