| 1. |
- Karlsson, Sune, Professor, 1960-, et al.
(författare)
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Flexible Fat-tailed Vector Autoregression
- 2021
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Konferensbidrag (refereegranskat)abstract
- We propose a general class of multivariate fat-tailed distributions which includes the normal, t and Laplace distributions as special cases as well as their mixture. Full conditional posterior distributions for the Bayesian VAR-model are derived and used to construct a MCMC-sampler for the joint posterior distribution. The framework allows for selection of a specific special case as the distribution for the error terms in the VAR if the evidence in the data is strong while at the same time allowing for considerable flexibility and more general distributions than offered by any of the special cases. As fat tails can also be a sign of conditional heteroskedasticity we also extend the model to allow for stochastic volatility. The performance is evaluated using simulated data and the utility of the general model specification is demonstrated in applications to macroeconomics and finance.
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| 2. |
- Karlsson, Sune, Professor, 1960-, et al.
(författare)
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Okuns lag i Sverige – är sambandet stabilt?
- 2021
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Ingår i: Ekonomisk Debatt. - : Nationalekonomiska Föreningen. - 0345-2646. ; 49:8, s. 39-47
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- I denna artikel undersöks huruvida förhållandet mellan förändringen i arbetslöshetsgrad och BNP-tillväxt i Sverige har varit stabilt i Sverige mellan 1982och 2019. Analysen av sambandet, vilket ofta kallas Okuns lag, genomförs mednyutvecklade bayesianska metoder som möjliggör formella jämförelser mellanolika skattade modeller. Våra resultat tyder på att sambandet har varit stabiltöver tiden. De indikerar även att högre BNP-tillväxt än förväntat sänkerarbetslöshetsgraden på kort sikt
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| 3. |
- Karlsson, Sune, Professor, 1960-, et al.
(författare)
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Statistical inference for the tangency portfolio in high dimension
- 2021
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Ingår i: Statistics. - : Taylor & Francis. - 0233-1888 .- 1029-4910. ; 55:3, s. 532-560
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study the distributional properties of the tangency portfolio (TP) weights assuming a normal distribution of the logarithmic returns. We derive a stochastic representation of the TP weights that fully describes their distribution. Under a high-dimensional asymptotic regime, i.e., the dimension of the portfolio, k, and the sample size, n, approach infinity such that k/n -> c is an element of (0, 1), we deliver the asymptotic distribution of the TP weights. Moreover, weconsider tests about the elements of the TP and derive the asymptotic distribution of the test statistic under the null and alternative hypotheses. In a simulation study, we compare the asymptotic distribution of the TP weights with the exact finite sample density. Wealso compare the high-dimensional asymptotic test with an exact small sample test. We document a good performance of the asymptotic approximations except for small sample sizes combined with c close to one. In an empirical study, we analyse the TP weights in portfolios containing stocks from the S&P 500 index.
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| 4. |
- Karlsson, Sune, Professor, 1960-, et al.
(författare)
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Vector autoregression models with skewness and heavy tails
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- With uncertain changes of the economic environment, macroeconomic downturns during recessions and crises can hardly be explained by a Gaussian structural shock. There is evidence that the distribution of macroeconomic variables is skewed and heavy tailed. In this paper, we contribute to the literature by extending a vector autoregression (VAR) model to account for a more realistic assumption of the multivariate distribution of the macroeconomic variables. We propose a general class of generalized hyperbolic skew Student’stdistribution with stochastic volatility for the error term in the VAR model that allows us to take into account skewness and heavy tails. Tools for Bayesian inference and model selection using a Gibbs sampler are provided. In an empirical study, we present evidence of skewness and heavy tails for monthly macroeconomic variables. The analysis also gives a clear message that skewness should be taken into account for better predictions during recessions and crises.
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