We assess the information content of order flow for the cross-section of cryptocurrency returns. Our analysis is based on a set of international order flows denominated in 11 major currencies that reflect world order flow. We find that world order flow has strong explanatory and predictive power for cryptocurrency returns. Order flow tends to dominate economic fundamentals for out-of-sample prediction, especially in the context of non-linear machine learning models, and its performance cannot be explained by limits to arbitrage. Overall, our findings indicate that order flow has a permanent effect on cryptocurrency returns.

We introduce robust kurtosis, which is a new quantile-based measure for the kurtosis of stock returns. For approximately normal distributions, robust kurtosis is equivalent to the traditional moment-based kurtosis. For fat-tailed distributions, when kurtosis matters the most, robust kurtosis provides a distinct and reliable measure. Using a cross-section of international stock index returns, we find that robust kurtosis carries a significant negative premium: higher robust kurtosis is related to lower future stock returns. This contrasts with the positive premium associated with robust skewness identified in previous research.

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors - the dividend price ratio, earnings price ratio, and book-to-market ratio - to predict the median, shoulders, and tails of the stock return distribution.

Can higher uncertainty increase the valuation (market-to-book value) of young firms compared to more established ones? As the current market shows higher levels of uncertainty about companies’ expected cash flows and changes in firm value, the question of the fundamental convex relationship between the two becomes more relevant. This paper aims to study how cash flow uncertainty affects the capital structure/leverage of a firm over time. A simple Bayesian learning framework is employed to assess leverage ratios in the presence of parameter uncertainty about expected cash flow. This study provides an analytical solution for leverage as a function of firm age and explores the implications using numerical results. The model links market leverage with expected cash flow volatility and firm age. Young firms face uncertainty about their expected cash flows and hence their firm value. Managers continuously update their evaluation of leverage ratios when they observe realized cash flow until firms reach maturity. Therefore, the paper provides a novel explanation of why the leverage ratio for many start-ups increases over time: the resolution of uncertainty decreases upside shock expectations as the firm ages. This result is useful both for academics, who can test the formulas derived in this paper for various industries, countries, and conditions, and for practitioners, who can use them to calibrate algorithmic trading models when linking uncertainty and firm valuation.

It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the conventional cointegration tests may suggest lack of cointegration. This chapter studies the possibility of having cointegrated errors that are characterized simultaneously by high persistence (near-unit root behavior) and very small (near zero) variance. The proposed dual parameterization induces the cointegration error process to be stochastically bounded which prevents the variables in the cointegrating system from drifting apart over a reasonably long horizon. More specifically, this chapter develops the appropriate asymptotic theory (rate of convergence and asymptotic distribution) for the estimators in unconditional and conditional vector error correction models (VECM) when the error correction term is parameterized as a dampened near-unit root process (local-to-unity process with local-to-zero variance). The important differences in the limiting behavior of the estimators and their implications for empirical analysis are discussed. Simulation results and an empirical analysis of the forward premium regressions are also provided.

We incorporate low-frequency information from demographic variables into a simple predictive model to forecast stock valuations and returns using demographic projections. The demographics appear to be an important determinant of stock valuations, such as the dividend–price ratio. The availability of long-term demographic projections allows us to provide (very) long-horizon forecasts of stock market valuations and returns. We also exploit the strong contemporaneous correlation between returns and valuations to improve return forecasts — something which is not possible in a predictive regression with only lagged predictors. Extensive pseudo out-of-sample forecast comparisons and tests demonstrate the predictive value that an accurate demographic projection can deliver. Although the availability of historical Census Bureau projections is limited, we demonstrate that they could have been employed in real time to improve true long-horizon stock return prediction. We show how the model can be used to adjust predictions under alternative demographic assumptions, incorporating, for example, the demographic impact of COVID-19 or recent changes to immigration policy.

The large-scale diversion of crops into mandates-driven biofuels since early 2000s, has raised concerns about impacts of biofuel policies on food prices. This study examines crude oil-corn-livestock dynamic linkages from January 1987 until December 2019 in Ontario, Canada. A significant structural break is identified in March 2011 as biofuel policy impacts become fully implemented and splits the three-decade period into pre- and post-break sub-periods. A nonlinear autoregressive distributed lag (NARDL) approach is employed since it allows prices to be tied by asymmetric relationships both in the short- and long-run. The NARDL model bounds test results indicate that crude oil and corn prices have a long-run connection with livestock prices in both sub-periods. In the post-break period, corn price has an asymmetric effect on cattle price in the long-run, with negative shocks in the corn price leading to a greater intensity on the cattle price than positive shocks. The presence of short-run asymmetry is evident in the impacts of crude oil price on both cattle and hog prices. However, the above asymmetric effect is insignificant in the pre-break period.

Peter Phillips has had a tremendous impact on econometric theory and practice [...]

Recent volatility in food prices in the grain market has generated much interest among agricultural market participants. This study examines the nonlinear dynamic relationship between spot and futures prices in grain markets. The empirical results provide strong evidence of price asymmetries. The corn spot price adjusts faster to futures price increases than futures price decreases, whereas the soybean spot price adjusts faster to futures price decreases than futures price increases. Although this asymmetric adjustment is found for a single market in Ontario, Canada, the results may also provide insights on the spot‐futures price convergence issues in other commodity markets.

This paper investigates the factors affecting the corn basis in Ontario with particular emphasis on the effect of ethanol production given the projected detrimental effect its expansion could have on the red meat sector. We estimate a location‐specific and panel vector error correction models (VECM) for seven elevators in Ontario from 2006 to 2013. We find a long‐run equilibrium relationship exists between the basis and factors affecting local supply and demand including ethanol capacity and that the direction of causality is from these factors to changes in corn price. A one‐time increase in ethanol capacity of 100 million liters is projected to increase the basis by approximately 30 cents per bushel within two years. However, the impact is insignificant for elevators located in the livestock‐intensive regions of the province. The demand for corn as livestock feed is a determinant of the local corn price for all elevators. The decline in the number of hogs and beef cattle along with the 50% increase in corn supply have resulted in the observed decline in the local corn price despite the significant increase in demand from ethanol.L'impact de la production locale d′éthanol sur le prix de base du maïs en Ontario Cet article cherche à comprendre les facteurs ayant un effet sur le prix de base du maïs en Ontario, en particulier sur les effets de la production d′éthanol étant donné les effets négatifs attendus sur le secteur de la viande rouge causés par son expansion. Nous estimons des modèles vectoriels à correction d'erreurs (MVCE) à emplacements précis et panel entre 2006 et 2013, pour 7 silos‐élévateurs en Ontario. Nous constatons une relation d′équilibre à long terme entre le prix de base et les facteurs ayant un effet sur l'offre et la demande locale incluant la capacité pour l′éthanol. Nous constatons aussi que le sens de la causalité passe de ces facteurs aux changements du prix du maïs. L'on s'attend à voir une augmentation d'environ 30 cents du prix de base du boisseau suivant l'augmentation unique de 100 millions de litres de la capacité d′éthanol. Par contre, l'impact est négligeable pour les silos‐élévateurs situés dans les régions d′élevage intensif de la province. La demande pour le maïs comme aliment pour le bétail est un facteur déterminant du prix local du maïs pour tous les silos‐élévateurs. Le déclin du porc et des bovins ainsi que l'augmentation de 50 % de l'offre de maïs ont mené à la diminution notée du prix du maïs local malgré l'augmentation significative de la demande pour l′éthanol.

This paper investigates the factors affecting the corn basis in Ontario with particular emphasis on the effect of ethanol production given the projected detrimental effect its expansion could have on the red meat sector. We estimate a location-specific and panel vector error correction models (VECM) for seven elevators in Ontario from 2006 to 2013. We find a long-run equilibrium relationship exists between the basis and factors affecting local supply and demand including ethanol capacity and that the direction of causality is from these factors to changes in corn price. A one-time increase in ethanol capacity of 100 million liters is projected to increase the basis by approximately 30 cents per bushel within two years. However, the impact is insignificant for elevators located in the livestock-intensive regions of the province. The demand for corn as livestock feed is a determinant of the local corn price for all elevators. The decline in the number of hogs and beef cattle along with the 50% increase in corn supply have resulted in the observed decline in the local corn price despite the significant increase in demand from ethanol. L'impact de la production locale d′éthanol sur le prix de base du maïs en Ontario Cet article cherche à comprendre les facteurs ayant un effet sur le prix de base du maïs en Ontario, en particulier sur les effets de la production d′éthanol étant donné les effets négatifs attendus sur le secteur de la viande rouge causés par son expansion. Nous estimons des modèles vectoriels à correction d'erreurs (MVCE) à emplacements précis et panel entre 2006 et 2013, pour 7 silos-élévateurs en Ontario. Nous constatons une relation d′équilibre à long terme entre le prix de base et les facteurs ayant un effet sur l'offre et la demande locale incluant la capacité pour l′éthanol. Nous constatons aussi que le sens de la causalité passe de ces facteurs aux changements du prix du maïs. L'on s'attend à voir une augmentation d'environ 30 cents du prix de base du boisseau suivant l'augmentation unique de 100 millions de litres de la capacité d′éthanol. Par contre, l'impact est négligeable pour les silos-élévateurs situés dans les régions d′élevage intensif de la province. La demande pour le maïs comme aliment pour le bétail est un facteur déterminant du prix local du maïs pour tous les silos-élévateurs. Le déclin du porc et des bovins ainsi que l'augmentation de 50 % de l'offre de maïs ont mené à la diminution notée du prix du maïs local malgré l'augmentation significative de la demande pour l′éthanol.

Using finite sample simulation methods, we assess the power of long-horizon predictive tests and compare them to their short-run counterparts, when the true underlying model contains financial asset bubbles. Our results indicate that long-run predictive tests using valuation predictors – specifically the dividend price ratio – do pick up the in-sample return predictability inherent in the asset bubbles. However, after size-adjustment, the long-run predictive framework has little advantage over its short-run counterpart when the predictor is highly persistent, but can provide non-trivial, yet still modest, power improvements when the predictor is moderately persistent. Finally, we provide a brief intuitive explanation for why a model with temporary collapsing bubbles may yield in-sample predictive power without implying the existence of profitable out-of-sample trading strategies.

The article examines the factors affecting the basis for corn and soybeans using several time-series techniques to account for potential structural breaks, seasonality, residual serial correlation and structural breaks, as well as potential endogeneity and nonstationarity. The spatio-temporal empirical framework is based on storage and trade theories which assume the relationship between nondelivery location’s spot price and futures price of a storable commodity depends on opportunity cost of capital, warehousing costs, a convenience yield and shipping costs. The interest rate effect is strong for both crops with shipping costs also affecting soybean basis and own inventory levels positively correlated with corn basis. The effect of the wedge between the price of carrying physical grain and the maximum storage rate on basis is positive for both crops. The empirical results, which are robust to multiple estimators, provide stronger evidence of a structural break for the soybean basis than for the corn basis.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

Predictability tests with long memory regressors may entail both size distortion and incompatibility between the orders of integration of the dependent and independent variables. Addressing both problems simultaneously, this paper proposes a two-step procedure that rebalances the predictive regression by fractionally differencing the predictor based on a first-stage estimation of the memory parameter. Extensive simulations indicate that our procedure has good size, is robust to estimation error in the first stage, and can yield improved power over cases in which an integer order is assumed for the regressor. We also extend our approach beyond the standard predictive regression context to cases in which the dependent variable is also fractionally integrated, but not cointegrated with the regressor. We use our procedure to provide a valid test of forward rate unbiasedness that allows for a long memory forward premium.

Previous literature has introduced causality tests with conventional limiting distributions in I(0)/I(1) vector autoregressive (VAR) models with unknown integration orders, based on an additional surplus lag in the specification of the estimated equation, which is not included in the tests. By extending this surplus lag approach to an infinite order VARX framework, we show that it can provide a highly persistence-robust Granger causality test that accommodates i.a stationary, nonstationary, local-to-unity, long-memory, and certain (unmodelled) structural break processes in the forcing variables within the context of a single χ2 null limiting distribution.

This article clarifies the empirical source of the debate on the effect of technology shocks on hours worked. We find that the contrasting conclusions from levels and differenced vector autoregression specifications, documented in the literature, can be explained by a small low-frequency comovement between hours worked and productivity growth that gives rise to a discontinuity in the solution for the structural coefficients identified by long-run restrictions. Whereas the low-frequency comovement is allowed for in the levels specification, it is implicitly set to 0 in the differenced vector autoregression. Consequently, even when the root of hours is very close to 1 and the low-frequency comovement is quite small, removing it can give rise to biases of sufficient size to account for the empirical difference between the two specifications.

To determine effects of baseline fitness (FIT) and Body Mass Index (BMI) on fatigue reports (FTG) and physical activity measured in steps per day (SPD) during radiation for breast cancer. Following institutional review board approvals, 51 women (55.3 ± 9.9 yrs) with breast cancer signed informed consent and completed the study. The subject sample size was chosen to reflect moderate effect size for adequate statistical power in this pilot study. A prospective, quasi-experimental repeated measures design was used to measure steps per day (SPD) using the Sense Wear © Body Monitoring System, and FTG using the Brief Fatigue Inventory (BFI). BMI, the measurement of body fat, was calculated utilizing the baseline height and weight. The BFI was completed weekly and SPD were measured during the first, fourth, and sixth weeks of radiation. FIT was determined using five SPD categories described by Tudor-Locke; sedentary, low active, somewhat active, active and high active. Significant differences in FTG and subsequent SPD were assessed using a two-way analysis of variance with between subject factors of FIT and BMI each with treatment week. Where indicated, Tukey's Honestly Significant Difference was used post-hoc analysis with p ≤ 0.05 (one-tailed). FIT levels determined by baseline SPD classified 13 subjects sedentary, 16 low-active, 9 somewhat-active, 11 active, and 2 high-active; two high-active subjects' data were combined with active group data to enable analysis. Overall FTG increased by 19.4%; however, changes were not significant (F = .325, df = 2, p = 0.723). Significant differences were found in FTG between FIT (F = 3.16, df = 3, p = 0.027) and between BMI categories (F = 2.98; df = 3; p = 0.034) with higher FIT and lower BMI reporting less FTG. Overall SPD declined 7.3%; however, changes were non-significant (F = .615, df = 2, p = .271). Significant differences in SPD were found between FIT (F = 53.74, df = 3, p<.001) and between BMI categories (F = 4.27; df = 3; p = .006) with higher FIT and lower BMI being more physically active. Study results support the impact of baseline fitness and BMI on fatigue and physical activity levels during radiation for breast cancer. These findings provide further support for the importance of remaining physically fit and maintaining a normal weight to decrease fatigue levels experienced during radiation treatment for breast cancer.