Christian Hotz-Behofsits, Florian Huber, Thomas O. Zörner
arXiv 19 Jan 2018 · Econometrics · publishedJournal of Forecasting (2018) · 7 citations (OpenAlex)
arXiv:1801.06373 · PDF · DOI · OpenAlex · Extracted main text
In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a time-varying parameter VAR with t-distributed measurement errors and stochastic volatility. To control for overparameterization, we rely on the Bayesian literature on shrinkage priors that enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data we perform a real-time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we moreover run a simple trading exercise.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Feldkircher M, Huber F and Kastner G (2017) Sophisticated and small… arXiv preprint arXiv:1711.00564 | 1.000 | 5 | 3 | 100% |
| 2 | Clark TE (2011) Real-time density forecasts from Bayesian vector aut… Journal of Business & Economic Statistics 29(3) | 0.737 | 3 | 2 | 100% |
| 3 | Huber F, Kastner G and Feldkircher M (2017) A New Approach Toward De… arXiv preprint arXiv:607.04532v3 | 0.737 | 3 | 2 | 100% |
| 4 | Bitto A and Frühwirth-Schnatter S (2016) Achieving shrinkage in a ti… arXiv preprint arXiv:1611.01310 | 0.644 | 2 | 2 | 100% |
| 5 | Griffin JE, Brown PJ et al. (2010) Inference with normal-gamma prior… Bayesian Analysis 5(1), 171–188 | 0.644 | 2 | 2 | 100% |
| 6 | Huber F and Feldkircher M (2017) Adaptive shrinkage in Bayesian vect… Journal of Business & Economic Statistics , 1–13 | 0.644 | 2 | 2 | 100% |
| 7 | Primiceri GE (2005) Time varying structural vector autoregressions a… The Review of Economic Studies 72(3), 821–852 | 0.644 | 2 | 2 | 100% |
| 8 | Amisano G and Geweke J (2017) Prediction using several macroeconomic… Review of Economics and Statistics 99(5), 912–925 | 0.511 | 2 | 1 | 100% |
| 9 | Diebold FX, Gunther TA and Tay AS (1998) Evaluating density forecast… International Economic Review 39(4), 863 | 0.511 | 2 | 1 | 100% |
| 10 | Kastner G and Frühwirth-Schnatter S (2014) Ancillarity-sufficiency i… Computational Statistics & Data Analysis 76, 408–423 | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Scalable Inference Method For Large Dynamic Economic Systems | 0.405 | 1 | 1 |