Till Örebro universitet

This article studies whether asymmetries in volatility help explain the cross section of commodity returns. We decompose realized variance into upside and downside components and construct a normalized difference measure, the relative signed jump (RSJ), following Bollerslev et al. (2020). A trading strategy that goes long the top tercile of commodities with the highest RSJ and shorts the bottom tercile delivers a statistically and economically significant annualized excess return of-6.29%. We also find that our tradable RSJ factor explains the cross section of commodity returns beyond well-established factors in a multivariate price setting context. Our results also show that the pricing ability of volatility asymmetries is distinct from other higher order moments such as realized skewness.

Wealth-tax rates are formulated as fractions of a capital stock, rather than fractions of income from capital, which makes them difficult to compare with other (income-based) tax rates. We derive investor-utility comparisons between wealth-tax rates and realized capital-gains tax rates, capturing two crucial features absent in naive comparisons: Risk-aversion and investment horizon, both of which magnify the effect of wealth taxes vis- & agrave;-vis capital-gains taxes. In numerical calibrations, we show that whereas a 1-percent wealth tax might naively be judged equivalent to a 10% capital-gains tax, a more accurate figure for a long-run risk-averse investor is 25%.

In this chapter, we extend the standard Gaussian stochastic volatility Bayesian VAR by employing the generalized hyperbolic skew Student’s t distribution for the innovations. Allowing the skewness parameter to vary over time, our specification permits flexible modelling of innovations in terms of both fat tails and—potentially dynamic—asymmetry. In an empirical application using US data on industrial production, consumer prices and economic policy uncertainty, we find support—although to a moderate extent—for time-varying skewness. In addition, we find that shocks to economic policy uncertainty have a negative effect on both industrial production growth and CPI inflation.

In this paper, we extend the standard Gaussian stochastic-volatility Bayesian VAR by employing the generalized hyperbolic skew Student’s t distribution for the innovations. Allowing the skewness parameter to vary over time, our specification permits flexible modelling of innovations in terms of both fat tails and – potentially dynamic – asymmetry. In an empirical application using US data on industrial production, consumer prices and economic policy uncertainty, we find support – although to a moderate extent – for time-varying skewness. In addition, we find that shocks to economic policy uncertainty have a negative effect on both industrial production growth and CPI inflation.

We analyse micro-level data concerning four financial variables in Sveriges Riksbank's Prospera Survey to evaluate the precision of forecasts provided by professionals active in the Swedish fixed -income market. Our results indicate that for the SEK/EUR and SEK/USD exchange rates, and the five-year government bond yield, none of the market participants that frequently participate in the survey manage to significantly outperform the random-walk forecast. For the central bank's policy rate, the market participants typically have a statistically significant higher forecast pre-cision than the random-walk forecast at the three-month horizon; however, at the two-and five-year horizons, the random-walk forecast typically outperforms the market participants.

In this paper, we analyze how skewness and heavy tails affect the estimated relationship between the real economy and the corporate bond-yield spread-a popular predictor of real activity. We use quarterly US data to estimate Bayesian VAR models with stochastic volatility and various distributional assumptions regarding the innovations. In-sample, we find that-after controlling for stochastic volatility-innovations in GDP growth can be well described by a Gaussian distribution. In contrast, the yield spread appears to benefit from being modeled using non-Gaussian innovations. When it comes to real-time forecasting performance, we find that the yield spread is a relevant predictor of GDP growth at the one-quarter horizon. Having controlled for stochastic volatility, gains in terms of forecasting performance from flexibly modeling the innovations appear to be limited and are mostly found for the yield spread.

In this paper, we analyse Okun's law-a relation between the change in the unemployment rate and GDP growth-using data from Australia, the euro area, the UK and the USA. More specifically, we assess the relevance of non-Gaussianity when modelling the relation. This is done in a Bayesian VAR framework with stochastic volatility where we allow the different models' error distributions to have heavier-than-Gaussian tails and skewness. Our results indicate that accounting for heavy tails yields improvements over a Gaussian specification in some cases, whereas skewness appears less fruitful. In terms of dynamic effects, a shock to GDP growth has robustly negative effects on the change in the unemployment rate in all four economies.

Att de störningar som drabbar makroekonomin är normalfördelade och har konstant varians är två antaganden som allt oftare har övergivits i den inter-nationella forskningslitteraturen under de senaste två decennierna. I denna artikel undersöks om detta är relevant för ett antal nyckelvariabler i svensk mak-roekonomi. Sammantaget tyder våra resultat på att forskare och policyekonomer som modellerar svenska makroekonomiska variabler – t ex i syfte att beskriva riskbilden kring dem – har påtaglig anledning att åtminstone överge antagandet om konstant störningsvarians. Ett konkret problem som annars kan uppstå är att prognososäkerhet överskattas i lugna tider och underskattas i turbulenta tider.

We derive asymptotic results for the long-horizon ordinary least squares (OLS) estimator and corresponding t$$ t $$-statistic for stationary autoregressive predictors. The t$$ t $$-statistic-formed using the correct asymptotic variance-together with standard-normal critical values result in a correctly-sized test for exogenous predictors. For endogenous predictors, the test is size distorted regardless of the persistence in the predictor and adjusted critical values are necessary. The endogeneity problem stems from the long-run estimation and is distinct from the ordinary persistence-dependent "Stambaugh" bias. The bias for fully stationary predictors appears not to have been previously noted and adds further difficulty to inference in long-run predictive regressions.

The nowcasting performance of autoregressive models for GDP growth are analysed in a setting where the error term is allowed to be characterized both by conditional heteroscedasticity and non-Gaussianity. Standard, publicly available, quarterly data on GDP growth from 1979 to 2019 for six countries are employed: Australia, Canada, France, Japan, the United Kingdom and the United States. In-sample analysis suggests that when homoscedasticity is assumed, support is provided for non-Gaussian error terms; the estimated degrees of freedom of the t-distribution lie between two and seven for all countries. However, allowing for both conditional heteroscedasticity and t-distributed innovations, results indicate that conditional heteroscedasticity captures the fat-tailed behaviour of the data to a large extent. Results from out-of-sample analysis show that point nowcasts are hardly affected by taking conditional heteroscedasticity and/or non-Gaussianity into account. For the density nowcasts, it is found that accounting for conditional heteroscedasticity leads to improvements for Australia, Canada, Japan, the United Kingdom and the United States; allowing for non-Gaussianity seems less important though. This result is robust to which measure is used for assessing density nowcasting performance.