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There is much controversy in the academic literature on the presence of short-term trends in financial markets and the trend-following strategy's profitability. This paper restricts its attention to the study of time-series momentum (TSMOM) in the US stock market. The paper aims to suggest answers to several important questions regarding TSMOM and to explain the existing controversy. Our answer to the question, whether short-term trends exist, is strongly affirmative. For the first time, we suppose that the returns follow a p-order autoregressive process with p>1 and evaluate this process's parameters. Fairly accurate knowledge of the momentum generating process allows us to provide analytical results on the profitability of the TSMOM strategy. Our answer to the question, whether the TSMOM strategy is superior to the buy-and-hold strategy, is also affirmative. Finally, we explain the lack of scientific evidence on the TSMOM strategy's profitability. We estimate the power of the statistical test for superiority of the TSMOM strategy and find that the power is much below the acceptable level. Consequently, all empirical tests for the TSMOM strategy's profitability suffer from the low power problem and tend not to reject the false null hypothesis.

This paper studies the impact on welfare of changes in coverage levels within deposit insurance schemes. The paper builds on previous literature by adding the possibility of bailouts for too-big-to- fail banks and incorporating a time lag between deposit payout and recoveries. Banks are also allowed to adjust deposit rates, and I include the effect this has on welfare. I show how to link theoretical results in this expanded model to observable variables, and I apply it to Colombia’s 2017 increase in its coverage level. I estimate all the model’s parameters from data and calculate the impact on welfare of this increase in coverage. Benefits outweigh costs, although the net effect is modest in size and sensitive to some of the parameters. Key variables are size, the probability of default and the impact the change in the coverage level has on the amount of insured deposits. Bailouts have mixed effects but overall raise the costs of increasing coverage levels. So does including a time lag between payout and recoveries. Allowing banks to adjust deposit rates also leads to larger costs because of higher deposit rates.

Objectives: The present paper aspires to measure the performance, benchmark and rank various public sector banks using statistical as well as mathematical tools. The present study also uses Sensitivity analysis in DEA to know the stability and instability of efficient DMUs. Design Methodology: Data are assemble from secondary sources for the year 2015-16 and these data are examine by using Multiple Regression analysis, Spearman Rank correlation test, Data Envelopment analysis, DEA Super efficiency and Sensitivity Analysis. In resent paper various inputs and outputs criteria are taken. Findings: Multiple Regression analysis result shows for same year, also result fluctuates, when changing the output. Result of Spearman’s Rank correlation test shows that there is no correlation between different ranks when taking different set of output.Result of DEA under CRS and VRS model 6 and 10 banks are with efficiency score 1. To get most efficient bank DEA super-efficiency analysis is used, result indicates State Bank of Patiala is appeared as super efficient in both models. Sensitivity result shows in both model (CRS and VRS) after deleting input and output one after another in some cases efficient DMUs maintain its stability and in some case it became unstable. Implication/Conclusion: The study provides valuable information on performance measurement, benchmarking, and ranking of public sector banks. The study provide information dealing with valuable factors need to be focused and pay attention to enhance the performance. The study concludes that MCDM technique is a more powerful technique to assess the performance while taking multiple inputs and outputs data simultaneously.

This paper provides a basis for combining the information from econometric learning-EL and machine learning-ML, to understand the hidden economic behavioral processes and systems, and predict the distributive impacts of economic policies and sharp changes in the direction of the economy. We use the key concepts of entropy methods, to develop a theoretical economic framework for combining EL and ML, in self-organizing-optimizing economic behavior systems, that involves nonlinear dynamics at the macro-level and stochastic individual behavior at the micro level. The paper contains both the theory and empirical applications relating to combining EL and ML, and evaluating the resulting nature of the economic behavior system.

This paper investigates the influence of macroeconomic forces on Corporate Governance (CG) performance in India. The main goal is to identify the macroeconomic factors and their nature of effects on CG through econometric analysis. As part of this investigation, statistical significance of corporate characteristics such as age, size and industry membership on CG performance index (CGPI) is examined. Joint effects and independent effects of corporate characteristics on CGPI are investigated. A list of macroeconomic variables identified from international studies are used to understand the factors influencing CGPI in different categories. Generalized Linear Model (GLM) and Ordinary Least Squares (OLS) regression are utilized in this study for econometric modeling. This study help rate the progress of economic reforms from the point of CG performance, plan further reforms and enable investors to choose the right firms. Major findings of the paper are; there exists statistically significant influence from age and size on the CG performance in every category of CGPI. OLS examination for identifying macroeconomic factors that influences CGPI reveals different sub-sets of the factors influencing different categories of CGPI. GLM analysis enables characterizing ideal high CGPI corporate as from services industry, with corporate age beyond 58 and market capitalization around INR 1430 crores with average CGPI of 66.

This paper presents a formal model that elucidates how sustained performance heterogeneity emerges from competitive amplification due to endogenous resource investment under uncertainty. Specifically, the model shows that if resources are scale free, any small resource differences are amplified into large performance differences, leading to high firm-specific heterogeneity in an industry, and vice versa for resources with low scalability. This finding provides a theoretical explanation for the empirical observation that firm-specific components of performance heterogeneity in the variance decomposition studies are significantly larger in some sectors than in others. Consistent with the predictions of the model, the empirical analysis in this paper confirms that industries with more scale free resources on average exhibit higher firm-specific heterogeneity.

The motives that firms have for investing in energy efficiency have been widely analysed in the literature. Particularly, there is a huge literature on barriers to energy efficiency investment and adoption. This paper reviews the econometric analyses carried out in this field. The main objective is to provide a general overview of the state of the econometric literature on the barriers and drivers of energy efficiency. We examine the main features of these studies and particularly the results of the explanatory variables used. We have classified them into three groups, barriers, drivers and firm characteristics. The paper ends with some suggestions for further analysis in order to improve our knowledge of energy efficiency investment.

Recent difficulties with certain factor models have increased interest in finding methods to “time” factor investing better. So far, however, the consensus is that factor timing is difficult. As inspiration for a possible solution, this paper reviews one of the best long-term return prediction models for the S&P 500 – the level of equity ownership in investor portfolios – which handily outperforms commonly-cited valuation-based forecast methods by relying on the more fundamental dynamics of supply and demand. Indeed, it has been called the “greatest predictor of future stock market returns” you’ve (probably) never heard of! For example, it can explain the earnings-less bull market of the 1980s, and overcomes the negative-PE problem of the 2008 Financial Crisis for which the traditional methods masked a good buying opportunity. The first section of the paper recreates and compares the various long-term S&P 500 forecasting methods, using our own robust fitting procedures. This paper then suggests a roadmap for applying this methodology to factor forecasting, since it is already known that standard valuation (or other) methods are not good estimators of factor performance.

Increasing healthcare costs are a big concern for the well-being of liquidity-constrained households. This paper evaluates the effect of binding liquidity constraints on healthcare spending decisions. Further, the paper compares the effect of liquidity constraints on healthcare expenditure with the effect on the non-health consumption in particular on the food consumption. I extend a standard incomplete markets model with a health capital in the felicity function. Theoretically, I show that households reduce their healthcare expenditure due to the binding liquidity constraints in the current period, whereas expenditure declines in the next period due to the expected binding constraints one period ahead. I use the extended model to test the incidence of binding liquidity constraints with a linearized Euler equation. Empirically, I show that the test of liquidity constraints for healthcare expenditure reveals different implications than a standard test of liquidity constraints for nondurable consumption. In particular, current binding constraints and expected binding constraints lead to the opposite direction of bias when the liquidity constraints are omitted. The resulting overall bias depends on which constraint has a stronger effect. Moreover, the income elasticity of healthcare expenditure varies significantly between asset poor and rich families, more than the elasticity of non-health consumption among wealth quintiles. Altogether, my findings show that the effects of liquidity constraints are heterogeneous across households and across expenditure categories.

PurposeA large empirical literature has found positive effects of economic freedom on economic outcomes, such as output and per capita growth. However, several variables in the index are very likely to decline in conjunction with recessions. The purpose of this paper is to determine whether, in the absence of these variable, whether the positive relationship between economic freedom and economic output remains.Design/methodology/approachThis paper makes use of a dynamic panel to compare the performance of economic freedom with and without variables endogenous to business cycles, which pertain to levels of government spending, rates of inflation, government borrowing and interest rates.FindingsTwo specifications fall in their statistical significance from the 1 to the 10 per cent level when variables relating to inflation are omitted. The worst case considered finds one specification size of the effect is still 66.3 per cent of the effect size of the standard measure of economic freedom.Practical implicationsThese findings are consistent with this kind of endogeneity being a minor problem with the data set when imperfect identification strategies are used, but the issue should be strongly considered when business cycles are pertinent to a research question that makes use of economic freedom data.Originality/valueThis paper contributes to the small literature focused on the robustness of the effect of economic freedom on output, while raising a specific concern that has not yet been explicitly addressed.

Unemployment has always been a very focused issue causing the nation as a whole to lose its economic and financial contribution. Unemployment rate prediction of a country is a crucial factor for the country's economic and financial growth planning and a challenging job for policymakers. Traditional stochastic time series models, as well as modern nonlinear time series techniques, were employed for unemployment rate forecasting previously. These macroeconomic data sets are mostly nonstationary and nonlinear in nature. Thus, it is atypical to assume that an individual time series forecasting model can generate a white noise error. This paper proposes an integrated approach based on linear and nonlinear models that can predict the unemployment rates more accurately. The proposed hybrid model of the unemployment rate can improve their forecasts by refecting the unemployment rate's asymmetry. The model's applications are shown using seven unemployment rate data sets from various countries, namely, Canada, Germany, Japan, Netherlands, New Zealand, Sweden, and Switzerland. The results of computational tests are very promising in comparison with other conventional methods. The results for asymptotic stationarity of the proposed hybrid approach using Markov chains and nonlinear time series analysis techniques are given in this paper which guarantees that the proposed model cannot show `explosive' behavior or growing variance over time.

In a 2016 paper (Fang, Huang, and Karpoff, 2016), we report that firms exposed to an increase in the prospect of short selling during the Reg SHO pilot program have lower discretionary accruals during the pilot period. Black, Desai, Litvak, Yoo, and Yu (2019, hereafter, BDLYY) argue that this result is not replicable. We show that BDLYY’s claim is incorrect. The accruals result previously was replicated in papers by Massa, Zhang, and Zhang (2015) and Heath, Ringgenberg, Samadi, and Werner (2019), and is easily replicable using data and code that we have shared widely since 2014 – including with the BDLYY team in 2015 – and that we recently posted publicly. The accruals result also is robust to a wide range of specification changes, including those implied by the BDLYY paper, which include: various measures of performance-matched discretionary accruals and total accruals; using our original 2012 Compustat data or currently available 2019 Compustat data; including both firm and year fixed effects; including or excluding other covariates in the difference-in-differences (DiD) tests; and using unbalanced rather than balanced panels. We conjecture that BDLYY’s results are inconsistent with prior results because they rely partly on non-standard accruals measures and/or use samples that differ from those used by Fang et al. (2016), Massa et al. (2015), and Heath et al. (2019). We conclude by discussing two theoretical concerns. First, we reiterate that an observed increase in short selling during the Reg SHO period is neither necessary nor sufficient to establish that the prospect of short selling has a disciplinary effect on earnings management, as managers’ endogenous adjustments affect short sellers’ opportunities and observed short selling. Second, we discuss a concern that the Reg SHO change appears to be too small to explain a wide range of firm outcomes, as recent empirical findings suggest.

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the corporate bond market. We find a significantly positive intertemporal relation between expected return and risk in the bond market and the time-series predictability is driven by aggregate systematic risk instead of aggregate idiosyncratic risk. We also propose a new measure of systematic risk for corporate bonds and find a positive link between systematic risk and the cross-section of future bond returns. We provide an explanation for the significance of systematic (idiosyncratic) risk based on different investor preferences and informational frictions in the bond (equity) market. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In several areas of the world, hail is one of the most detrimental atmospheric phenomenon for agriculture, causing a significant loss of output and, consequently, of farms' revenues. Despite being a highly stochastic and localized phenomenon, thus allowing for a sustainable insurance market to hedge against its detrimental effects, this last is often subsidized. The present paper tries to figure out if the promotion of an alternative hedging instrument, anti-hail nets, could help to increase the actuarial soundness of the hail insurance market. In the first part of the paper a simple model is presented showing that the relation between the differential profitability of anti-hail nets versus insurance and the plot specific versus the average expected damage has an inverse U-shape. This implies that incentives to anti-hail nets could cause low risk farmers to exit the insurance market more likely than high risk ones. Such finding is confirmed by the empirical investigation, further showing that higher per-hectare output values and being located in an area strongly affected by hail increase the chance of a plot to be hedged through anti-hail nets.

The trend real interest rate plays an important role in monetary policy decision making. Many papers have estimated it. However, the uncertainty surrounding these estimates is substantial. In this paper, we construct a new measure of the trend real interest rate in a data-rich environment using a large time-varying local mean Bayesian autoregression (VAR). This new measure is more precisely estimated and can provide valuable information to policymakers. The ranges of the 95% credible intervals of our proposed estimates are from 1.25% to 1.76%. Using the US data, we find that the trend real interest rate has declined substantially since 1984 and becomes negative after 2012.

With a tremendous increase of the usage of machine learning (ML) in recent years, a method called reinforcement learning (RL) which is a branch of ML has gained a huge attraction, as it has addressed the problem of learning automation of decisions making over time. In case of the financial trading, many approaches such as descriptive, fundamental, and technical analysis are used to make decision on stocks investment. Another approach that this paper aims to explore is the Deep Q-Learning which is also a suitable method to deal with the much more practical problem of financial trading. This paper applies the listed methods of analysis (Descriptive, technical and the Deep Q-Learning) on apple stocks index (AAPL). The paper founds that these techniques can be beneficial to traders and can also help making both long-term and short-term trading investment.

The implied volatility smile surface is the basis of option pricing, and the dynamic evolution of the option volatility smile surface is difficult to predict. In this paper, attention mechanism is introduced into LSTM, and a volatility surface prediction method combining deep learning and attention mechanism is pioneeringly established. LSTM's forgetting gate makes it have strong generalization ability, and its feedback structure enables it to characterize the long memory of financial volatility. The application of attention mechanism in LSTM networks can significantly enhance the ability of LSTM networks to select input features. This paper considers the discrete points of the implied volatility smile surface as an overall prediction target, extracts the daily, weekly, and monthly option implied volatility as input features and establishes a set of LSTM-Attention deep learning systems. Using the dropout mechanism in training reduces the risk of over-fitting. For the prediction results, we use arbitrage-free smoothing to form the final implied volatility smile surface. This article uses the S&P 500 option market to conduct an empirical study. The research shows that the error curve of the LSTM-attention prediction system converges, and the prediction of the implied volatility surface is more accurate than other predicting system. According to the implied volatility surface of the 3-year rolling forecast, the BS formula is used to pricing the option contract, and then a time spread strategy and a butterfly spread strategy are constructed respectively. The experimental results show that the two strategies constructed using the predicted implied volatility surfaces have higher returns and sharp ratios than that the volatility surfaces are not predicted. This paper confirms that the use of AI to predict the implied volatility surface has theoretical and economic value. The research method provides a new reference for option pricing and strategy.

This paper examines how sentiment affects homebuyers’ housing transaction decisions with a focus on auction sales. Utilizing housing transaction data in Sydney from 2000 to 2014, we find that transaction prices are significantly higher for auction sales compared with private sales, consistent with the notion of a “winner’s curse.” Four sentiment proxies, including three weather-based sentiment proxies and a survey-based sentiment index, are used to derive novel measures of sentiment, revealing that higher sentiment is associated with higher transaction prices, particularly for auction sales. Moreover, sentiment boosts sale prices particularly when the housing market is in boom; however, investors are not more prone to sentiment bias than are owner-occupiers. Sentiment exhibits an asymmetric effect in that low sentiment dampens price to a greater extent compared with the price-boosting effect of high sentiment. Our result is robust to using national sport events as sentiment shocks, selection bias, and participation constraints. Potential mechanisms include present-biased preferences and sentiment-related projection bias. Overall, our evidence sheds light on the role of sentiment in housing market decisions. This paper was accepted by Tyler Shumway, finance.

In an important paper, Bollerslev, Li, and Zhao (2019) find that there is significant predictive content in signed total jump variation. In this paper, we further decompose jumps into small and large (signed) variation. Our primary purpose is to investigate the marginal predictive content of small versus large jump variation, when forecasting oneweek ahead cross-sectional equity returns. However, we also examine earnings announcements, in order to shed new light on the linkages between (small and large) jumps and news. One of our key findings is that sorting on signed small jump variation leads to greater value-weighted return differentials between stocks in our highest and lowest quintile portfolios (i.e., high-low spreads) than when either signed total jump or signed large jump variation is sorted on. Moreover, in a key case, the high-low spread is not significantly different from zero when signed large jump variation is sorted on. Indeed, including large jump variation can actually decrease predictive accuracy, in the sense that average returns and alphas for high-low portfolios are lower when total jump variation is utilized in our prediction experiments rather than small jump variation. These results suggest that there may be a threshold, beyond which “large” jump variation contains no marginal predictive ability, relative to that contained in small jump variation. Analysis of returns and alphas based on industry double-sorts indicates that the benefit of small signed jump variation investing is driven by stock selection within an industry, rather than industry bets. Investors prefer stocks with a high probability of large positive jump variation, but they also tend to overweight safer industries. Additionally, the fact that large and small (signed) jump variation have differing marginal predictive content is explained at least in part by our observation that in double-sorted portfolios, the content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns, even when controlling for total jump variation or realized skewness. Further, we find that large jumps are closely associated with “big” news, as might be expected. In particular, large earning announcement surprises increase both the magnitude and occurrence of large jumps. While such news related information is embedded in large jump variation, the information is generally short-lived, and dissipates too quickly to provide marginal predictive content for subsequent weekly returns. Finally, we find that while large jump variation is closely associated with large earnings surprises (“big” news), small jumps tend to be more closely associated with idiosyncratic risks, and can be diversified away.

The paper applies momentum-based investment strategies, such as trend and reversal, on government bond futures. It thereby introduces an innovative yet simple approach to harvest time horizon diversification in the signal generation process. Signal diversification over multiple lookback windows enhances returns, independent of the signal aggregation algorithm (naive diversification, maximum diversification, average correlation, Ward distance, equal risk contribution, or principal component analysis). The paper builds two distinct strategies from the same aggregated input signals. The first, time-series momentum (TSMOM), uses time-series aggregation and the second, cross-sectional momentum reversal (XSREV), is calculated in a cross-sectional framework. Specifically, XSREV grounds on government bond pairs, an approach that has a longstanding history within the fixed income space. The paper states that government bond returns are highly correlated and driven by common global factors, which suggests that a reversal strategy is more suitable. Further analysis reveals that XSREV captures a cross-sectional value return factor. With its formulation of XSREV, the paper fills a void in the literature, as existing value measures in fixed income suffer from data quality issues.

In a choice experiment with 1866 Amazon Mechanical Turk workers in the domain of cars, we test some of the predictions of a family of multi-attribute choice models that incorporate a menu-dependent reference point whose value in every attribute is the minimum value of that attribute among the available options. In particular, these models predict that in two of the scenarios we consider in this paper, one should expect a significant attraction effect when an asymmetrically dominated option is added to the choice set, due to a change in the reference point. However, in the third scenario, these models predict no change in the choice probabilities of the existing options after adding the asymmetrically dominated option, and thus, no attraction effect, as the reference point remains intact. These models also make a number of predictions regarding the presence or the absence of another context effect called the compromise effect, which are tested in this paper. This amounts to a total of 8 predictions. We find support for 7 of these predictions, suggesting the aforementioned reference point has a significant impact on preferences and choices.

Time series data often use by financial analysis for making an investment decision. The paper chooses US currency strength and interest rate as variable to use in APT method with S&P500 (US market stock index). The paper predicts US currency strength and interest rate in the next six months by using moving average (lag 1, 2, and 3) time series data to analyze. For US currency strength, lag 1 was the best time to predict the future for non-seasonality and lag 2 was the best time to predict the future for seasonality. For the interest rate, lag 1 was the best time to predict the future at non-seasonality and seasonality. Then using the new prediction number in APT method. The result showed that the different between the expected return and the actual return were smaller. By using the time series data analysis and moving average, it helps to improve the accuracy of predicting the expected return.

This paper presents non-take-up rates of benefits from the German Income Support for Job Seekers scheme, called Unemployment Benefit II (Arbeitslosengeld II). Eligibility to these benefits is simulated by applying a microsimulation model based on data from the Socio-economic Panel for the years 2005 to 2014. To ensure the quality of the results, feasible upper and lower bounds of nontake-up are shown for different simulation assumptions. By employing a binary choice framework, determinants of the decision (not) to take-up benefits are studied by means of a cross-sectional probit regression and a fixed effects linear probability model. The findings of this study indicate that rates of non-take-up are substantial and time-stable in the decade after the Hartz IV reform of 2005. On average, the share of households that do not claim benefits despite being eligible, amounts to 55.7 percent of all eligible households in that period. The issue of non-take-up has further important implications for the determination of the standard benefit rate. Since the legally defined calculation procedure does not account for non-take-up households in the reference group, the approximated consumption expenditure that is considered as necessary for a dignified life is calculated too low. The results of this study suggest that the legally defined monthly adult lump sum amount in the year 2014 would have been twelve euros higher if the issue of non-take-up was accounted for in the methodology. Based on the findings, the paper aims to give policy recommendations.

This paper reviews the most recent empirical literature that assesses efficiency in higher education. We analyze 76 studies ranging from 1997 to 2018 and classify them according to the methodologies applied and to the definitions used to describe the outputs, inputs, quality and the context variables. We find that 72 percent of the empirical studies use nonparametric approaches. The most recent studies use panel data. The degrees completed are the most frequently used output variable, and only 9 papers include quality variables. Moreover, while only few parametric papers take observed heterogeneity into account, more than 40 percent include environmental variables to address for observed heterogeneity. This review is useful for researchers interested in measuring efficiency, for policy makers and for other educational stakeholders.

This paper investigates the state of Corporate Governance (CG) performance in India. The main goal is to ascertain the determinants of CG through content analysis. As part of this investigation, statistical significance of categories based on CG performance index (CGPI), possibility of selective improvement of themes and analysis for ascertaining scoping and focusing of limited attributes within a theme for improvement. While at a macro level categories used mean with standard deviation rules, but at micro level homogenous clusters were identified with k mean cluster analysis within a category to enable deeper profiling. For scoping of attributes; factor analysis with Principal Component Analysis and Varimax rotation is employed. Ordinary Least Squares (OLS) regression and Ordinal Logistic Regression (OLR) are utilized in this study for econometric modeling. This study help rate the progress of reforms, plan further reforms and enable investors to choose the right firms. Major findings of the paper are; there exists statistically significant differences among categories and across theme indices. High adjusted R square linear regressions and subsequent sensitivity analysis short listed critical attributes to focus. The above findings suggest high predictive ability of OLR-OLS system of equations highlighting the possibility of focusing on limited attributes within a theme, but need for holistic approach on CG with all themes as a requirement are the inferences.

This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables point-identification fails, but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identification bounds change as the support of the explanatory variables varies.

After the seminal work of Liu et al. (2015) finds that the realized volatility (RV) using 5-minute intervals performs well, economists tend to use this simple measure in applications. Existing literature in the cryptocurrency already relies on 5-minute RV, but no paper has evaluated whether 5-minute RV performs well compared with other realized measures. Following Liu et al. (2015), we show that the 5-minute RV of Bitcoin performs well compared to other realized measures, meaning that this result justifies the existing literature that already uses this simple measure. This paper also indicates that realized measures with longer intervals such as 120-minute RV could provide inaccurate estimates.

This paper uses the bottleneck model of Vickrey (1969) to empirically measure the social cost of traffic congestion in the US. Using a detailed trip-level data, we estimate extra travel time over and above hypothetical free-flow travel time, which we call “queuing time”, for each average commute trip. The estimated individual queuing time implies that the annual cost of congestion borne by all US commuters is about 29 billion dollars. We find that a higher level of congestion in a city may be attributed to a smaller per capita road stock in the city. This paper also empirically quantifies a toll that depends both on the commuter's arrival time and trip distance.

How should researchers design experiments to detect treatment effects with panel data? In this paper, we derive analytical expressions for the variance of panel estimators under non-i.i.d. error structures, which inform power calculations in panel data settings. Using Monte Carlo simulation, we demonstrate that, with correlated errors, traditional methods for experimental design result in experiments that are incorrectly powered with proper inference. Failing to account for serial correlation yields overpowered experiments in short panels and underpowered experiments in long panels. Using both data from a randomized experiment in China and a high-frequency dataset of U.S. electricity consumption, we show that these results hold in real-world settings. Our theoretical results enable us to achieve correctly powered experiments in both simulated and real data. This paper provides researchers with the tools to design well-powered experiments in panel data settings.

This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial coefficients are derived. Regularity conditions are established for the QML estimators of individual spatial coefficients, as well as their means (the mean group estimators), to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degrees of sparsity. The simulation results are in line with the paper's key theoretical findings even for panels with moderate time dimensions, irrespective of the number of cross section units. An empirical application to U.S. house price changes during the 1975-2014 period shows a significant degree of heterogeneity in spill-over effects over the 338 Metropolitan Statistical Areas considered.

Output-based carbon regulations—such as fuel economy standards and the rate-based standards in the Clean Power Plan—create well-known incentives to inefficiently increase output. Similar distortions are created by attribute-based regulations. This paper demonstrates that, despite these distortions, output and attribute-based standards can always yield greater expected welfare than “flat” emission standards given uncertainty in demand for output (or attributes), assuming locally constant marginal damages. For fuel economy standards, the welfare-maximizing amount of attribute or mileage-basing is likely small relative to current policy. For the electricity sector, however, an intensity standard may yield greater expected welfare than a flat standard. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Papers treating variance swap replication often mention that the replicating portfolio consists of a static position in an appropriately weighted continuous strip of options, and a dynamic position in the underlying asset that can be regarded as the delta-hedge of the strip of options. Most papers, however, do not explicate the impact of delta-hedging the options, and in particular do not mention what volatility to use when delta-hedging the options. Although no new results are derived, in this educational note we clarify the aforementioned two points.

This paper tracks the consequences of individuals’ desire to align their location with their social preferences. The social preference studied in the paper is distaste for relative deprivation, measured in a cardinal manner. Location is conceived as social space, with individuals choosing to relocate if, as a result, their relative deprivation will be reduced, holding their incomes constant. Conditions are provided under which the associated dynamics reaches a spatial steady state, the number of periods it takes to reach a steady state is specified, and light is shed on the robustness of the steady state outcome. By way of simulation it is shown that for large populations, a steady state of the relocation dynamics is almost always reached, typically in one period, and that cycles are more likely to occur when the populations’ income distributions are more equal.

Import demand has been a major research topic in international economics for the past 80 years because of its importance for analyzing trade and evaluating trade policies. The goal of this paper is to survey the literature and conduct a meta-analysis of empirical studies on import demand with the intention of clarifying the effect of economic development on income elasticity. In particular, we test the hypothesis that higher income levels are associated with a more elastic import demand. We apply a combination of parametric and non-parametric methods on estimates from a sample of 152 papers published over the period 1975-2014 and find that this relationship is significant and robust. Specifically, kernel densities of income elasticity estimates for high-income countries in North America and Europe are shown to exceed those for poorer parts of the world. The results from quartile regressions confirm this pattern and establish its robustness when controlling for the effect of model specifications.

This paper discusses the effects of Corporate Governance (CG) on corporate performance. This study investigates the nature and intensity of influence CG exercises on the firm performance. The main goal is to prove that CG is a determinant for firm performance. Main attention was paid to ANOVA based inferential statistics, coefficient of correlation and Ordinary Least Squares (OLS) based regression analysis to investigate the objective. This topic was chosen to examine whether the positive correlations between CG and firm performance in western studies are applicable in Indian context. OLS based econometric modelling identifies Equity Multiplier Ratio (EMR) and Times Interest Earned (TIE) as leading indicators of Economic Value Added (EVA) that needs CG attention. The major findings of the paper are, there are no statistically significant differences among the means of EVA on comparing across CGPI categories. However, there exist a positive correlations in all CGPI categories with EVA. The above findings suggest that by a financial value chain, CGPI activism on the leading indicators of EVA will culminate in the improvement of EVA itself.

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that estimated covariance matrices, obtained under alternative orderings of variables, are systemically different when the data exhibits independent volatility dynamics. Specifically, simulations show that estimated covariances and correlations become more different, the larger the ratio of individual volatility paths becomes. This paper shows that this property is important for empirical applications as alternative estimates on the evolution of U.S. systematic monetary policy and on inflation-gap persistence indicate that conclusions may hinge on a selected ordering of variables. The dynamic correlation Cholesky multivariate stochastic volatility model is proposed as a robust alternative.

A version of this paper is available as Guglielmo Maria Caporale & Luis A. Gil-Alana & Carlos Poza, 2019. "High and low prices and the range in the European stock markets: a long-memory approach," CESifo Working Paper Series 7652, CESifo Group Munich. This paper uses fractional integration techniques to examine the stochastic behaviour of high and low stock prices in Europe and then to test for the possible existence of long-run linkages between them by looking at the range, i.e., the difference between the two logged series. Specifically, monthly, weekly and daily data on the following five European stock market indices are analysed: DAX30 (Germany), FTSE100 (UK), CAC40 (France), FTSE MIB40 (Italy) and IBEX35 (Spain). In all cases, the order of integration of the range is lower than that of the original series, which implies the existence of a long-run equilibrium relationship between high and low prices. Further, multiple breaks are found in the high and low-price series but no breaks in the range, and the estimated fractional differencing parameter is positive in all cases, which represents evidence of long memory.

This paper focuses on the mark-to-model valuation of open foreign exchange (FX) forwards, a popular instrument for hedging FX exposures with higher degree of flexibility with respect to settlement date and settlement amount compared to (standard) closed forwards. While there is sufficient coverage of valuation models and risks of closed FX forwards in literature (see e.g. Hull 2012), we observe limited or no coverage on open forwards in spite of the increasing utilization of those hedging instruments globally. The paper introduces a standard mark-to-model approach for open FX forwards in dependence of the window period of the open FX forward, with the model being tested using swing-option pricing methods based on the Longstaff-Schwartz algorithm (Longstaff and Schwartz, 2001) as well as forward-based pricing approaches (Hull (2012), Jorion (2010)). To our knowledge, this is the first paper focusing on mark-to-model valuations of open FX forwards within the framework of a stochastic model.

The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. The technique is nonparametric and seeks to decompose a time series into a trend and a cyclical component unaided by economic theory or prior trend specification. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning about the form of economic cycles and trends. As recent research has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data. This paper proposes an easy-to-implement practical procedure of iterating the HP smoother that is intended to make the filter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP filter in view of its connection to L_2-boosting in machine learning. The paper develops limit theory to show that the boosted HP filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks – the most common trends that appear in macroeconomic data and current modeling methodology. In doing so, the boosted filter provides a new mechanism for consistently estimating multiple structural breaks. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the differences between simple HP filtering, the data-determined boosted filter, and an alternative autoregressive approach. These examples show that the boosted HP filter is helpful in analyzing a large collection of heterogeneous macroeconomic time series that manifest various degrees of persistence, trend behavior, and volatility.

This paper presents a thorough replication of Hamilton (2003) which in turn replicates and extends the findings of four seminal papers regarding the oil price-GDP growth relationship. Firstly, we replicate the empirical results obtained with the oil price measures of Hamilton (1983), Mork (1989), Lee et al. (1995), Hamilton (1996), and Hamilton (2003) by using an identical data set of real and nominal oil prices. Secondly, we extend the data sets to 2019Q4 and apply the same methodology. We find that for more recent data the explanatory power of the proposed oil price measures on GDP growth rates is still present, albeit on a slightly weaker magnitude. Extending the ARX(4), we only find little evidence that oil price decreases impact GDP growth rates on the full data set. Parameter stability tests suggest that the impact of oil price decreases might be limited to certain periods. 2020 Elsevier B.V. We are thankful to the editor Richard Tol, James Hamilton, with whom we had a fruitful discussion on a preliminary set of results, and an additional anonymous reviewer. Their comments significantly improved this manuscript. Part of the work has been conducted during Thomas Walther's research time as Assistant Professor at the University of St. Gallen, Institute for Operations Research and Computational Finance. This publication is part of SCCER CREST, which is supported by Innosuisse. Thomas Walther is grateful for the funding received for conducting this research. Scopus

In this paper we study investigate whether we can time the performance of stock market factors in a profitable manner. In the first part of our paper, we study a trading strategy that buys factors based on a timing strategy. We find that the factor timing strategy experiences lower maximum than our factor portfolio benchmark but does not produce the returns necessary, net of trading costs and 2% management and 20% performance fees, to outperform our factor benchmark. Additionally, the factor timing portfolio plays a very small role in an optimal portfolio that is comprised of a factor timing portfolio and our factor portfolio benchmark. In the second part of our paper, we test whether economic indicators can predict factor timing performance, net of the factor timing benchmark. We find that stagflation (a period of high inflation during a recession) is predictive of the outperformance of our factor timing strategy relative to our benchmark. We form a strategy that engages in factor timing and shorts our factor benchmark the month after a stagflation period is predicted and only buys our factor benchmark during all other times. We find that this strategy produces a Sharpe ratio of 1 and is negatively correlated to both stocks and bonds. We interpret this result as a discovery of a point of market inefficiency.

The most popular DCF method for CBA and capital budgeting has been considered as “ambiguous or anomalous” as early as 1956 (Lorie and Savage). Recently Merlo (2017) argued that the DCF methodology cannot be a correct approach in some cases. Most published works attributed the multiple IRR as one of the limitations of IRR but not as a problem with the DCF method. Reinvestment and multiple IRR are, inter alia, the symptoms of the problems with the DCF method and the underlying causes are not hitherto completely evaluated. As these symptoms often reflect on the IRR, the IRR is considered as inferior to NPV. There is no research evidence available to support that assertion. The important contribution of this paper is the identification and rectification of those underlying causes. The problems are with the nature of NCF (NNCF) and the failure of the DCF method to handle such NCFs. This analysis is conducted using a capital amortization schedule (CAS) based new method. The findings of this analysis are expected to open new vistas in CBA and capital budgeting. The important findings are: A) the CAS method introduced is found to be a perfect substitute for the DCF method. The CAS method is transparent and has many advantages discussed here, B) the CAS method, unlike the DCF method, clearly identifies and quantifies the reinvestment. Previously published works did not mention any such evidence and therefore obviously this is one of the valuable contributions of this study, C) the results demonstrate that reinvestment is common with the NNCF investments but not with the normal NCF investments. This paper adds this important finding to the literature, D) the CAS method reveals that reinvestment leads to multiple IRR and spurious NPV. Hitherto, the spurious nature of NPV caused by the reinvestment is neither studied nor reported and therefore the NPV rule has been supported, E) the modified CAS (MCAS) method eliminates the reinvestment and thereby resolves the problem of multiple IRR and leads to a unique and real IRR and eliminates the spurious NPV too, F) the CAS method allows estimating the IRR as a return on the total capital investment and on the declining balance of capital. In conclusion, the CAS method and the findings of the present analysis add great value to the literature on and practice of CBA and capital budgeting.

Sadorsky (2012) analyzes correlations and volatility spillovers between oil prices and stock price indices of clean energy and technology companies. In this paper, we replicate the original analysis of Sadorsky (2012), extend the sample period and present new findings. The extended sample analysis shows that the correlation estimates reported in the original paper have not changed since 2010 despite increased efforts to tackle climate change. A re-analysis demonstrates that return and volatility spillovers are economically negligible consistent with efficient markets and economic theory but in stark contrast with the title and focus of the original paper. We further show that a two-asset portfolio analysis yields results with limited practical relevance. Our study highlights the importance of a theoretical grounding of empirical research.

Due to their short lifespans and migrating moneyness, options are notoriously difficult to study with the factor models commonly used to analyze the risk-return trade-off in other asset classes. Instrumented principal components analysis solves this problem by tracking contracts in terms of their pricing-relevant characteristics via time-varying latent factor loadings. We find that a model with three latent factors prices the cross-section of option returns and explains more than 85% of the variation in a panel of monthly S&P 500 option returns from 1996 to 2017. In particular, we show that the IPCA factors can be rationalized via an economically plausible three-factor model consisting of a level, slope and skew factor. Finally, out-of-sample trading strategies based on insights from the IPCA model have significant alpha over previously studied option strategies. 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 provides a dataset on the currency composition of the international investment position for a group of 50 countries for the period 1990-2017. It improves available data based on estimates by incorporating actual data reported by statistical authorities and refining estimation methods. The paper illustrates current and new uses of these data, with particular focus on the evolution of currency exposures of cross-border positions.

The main focus of this paper is to show that calendar/seasonal anomalies are well-working even in the most recent periods and, additionally, to find a way how to combine them in a search for profit from the practitioner's point of view. This paper is a case study of possible usage of following anomalies: Turn of the Month in Equity Indexes, Federal Open Market Committee Meeting Effect in Stocks, Option-Expiration Week Effect and The Payday Effect. Firstly, it was found that each individual trading strategy is profitable and those strategies could or probably should be combined into one complex composite seasonal strategy. The analysis has also found that the strategy mentioned above could be enhanced by the addition of a trend factor. To be more precise, data suggest that it is worth to systematically trade only if the trend factor signals to do that. Such addition drastically reduces drawdowns and the resulting strategy simply does not enter into trading positions when the market situation is not favorable.

The paper used three asset pricing model which is Capital Asset Pricing Model, Arbitrage Pricing Theory, and Dividend Growth Model to calculate the expected return of four companies including Amazon, Southwest Airlines, JP Morgan Chase, and Exelon Corporation. Then, will use the result to compare with the actual return to test which model is more accurate and which company has the highest return. These companies are the largest one in their own industry (E-commerce, Airlines, Bank, and Energy). The data is mainly gathered from Yahoo finance, Federal Reserve Bank of St. Louis, and NASDAQ. The paper found that CAPM is the most accurate model in predicting the expected return of the asset. And when look at the expected return computed using CAPM, the company that have the highest return is JP Morgan Chase.

In the world of investment, the subject of building a portfolio concerning tail risk is still one of the frequently discussed subjects and unquestionably vital for investors. This paper seeks to examine how the risk measures, lower tail-dependence based on the copulas approach and Conditional Value-at-Risk (CVaR), affect the portfolio strategies and play important roles for tail risk management and diversify the portfolio. By using these two risk measures mentioned above, two different types of risk-based portfolios are proposed that consider for the tail risks: 1) Minimum-lower tail-dependence portfolio (RMTP) and 2) Risk Parity Portfolio based on Conditional Value-at-Risk (CRPP). The simulation results showed how those two risk-based portfolios, RMTP and CRPP, work effectively in multi-asset allocation framework with 6 assets: stocks and sovereign bonds of Japan, United States and Germany, based on the monthly rebalance rule, using 2004-2018 sample period. One of the key findings were that both RMTP and CRPP strategies delivered better performances compared with the traditional portfolio strategies in terms of sharp ratio: 1) RMTP yielded 0.92 and 2) CRPP yielded 0.99 (by adding an appropriate risk reduction to this portfolio, the sharp ratio went up to 1.76). In addition, both of these two strategies also worked effectively in terms of the average of maximum monthly drawdown related to the effect of the tail risk: 1) RMTP by 1.80% and 2) CRPP by 1.74% (by adding an appropriate risk reduction to this portfolio, maximum drawdown decreased to 0.78%). Furthermore, this paper also studies an enhancement strategy based on Risk Parity Portfolios (RPP) focusing on and using co-integration relationship (co-integration approach). According to the simulation result, this proposed enhancement strategy has a potential to yield roughly 4.5% return. Finally, this paper presents the explicit derivation of lower tail-dependence and co-integration approach.

This paper develops a computational framework for inverting Gompertz-Makeham (GM) hazard rates, in a manner consistent with a compensation law of mortality for heterogenous populations, to define a Longevity-Risk-adjusted Global (LRaG) Age. To illustrate its salience and relative magnitude, the paper implements and calibrates this framework using rates from the Human Mortality Database (HMD). Among other things, this paper demonstrates that when properly benchmarked, the {\em longevity-risk-adjusted global age} of a 55-year-old Swedish male is 48, whereas a 55-year-old Russian male is closer in age to 67. The paper also discusses the connection between the LRaG age and the related concept of *biological* age. In a world of growing mortality heterogeneity, a new and distinct LRaG age could be used for pension and retirement policy. Perhaps a salient and transparent metric that adjusts chronological age for longevity risk might help capture the public's attention, educate them and induce them to take action to increase the odds of retirement success, by, for example, working longer and retiring later.

The main focus of this paper is to show some principles that can be used to pick well-working periods for the evaluation of various indicators. This paper is focused on a momentum strategy based on a diversified ETFs, but the approaches are applicable in almost every strategy where the practitioners have to pick the period for the indicator. Firstly, there is an option to pick a larger set of periods to evade the risk of overfitting and the underperformance out-of-sample. Secondly, there is a possibility to make a decision based on the performance of each strategy that corresponds to the different evaluating periods. Each approach is examined concerning the momentum strategies based on 3- to 15-month momentum and results are compared also with a benchmark portfolio. Lastly, this study shows that periods that are almost fixed in the world of quantitative strategies, do not necessarily have to be the best.