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Our approach is distinct owing to its comprehensiveness: First, we employ a data\u2010rich forecast methodology to handle a large set of potential predictors in a Bayesian model\u2010averaging approach and, second, we take a look at multiple asset classes (equities, foreign exchange, bonds and commodities) over long time spans. We find that proxies for credit risk and funding liquidity consistently show up as common predictors of volatility across asset classes. Variables capturing time\u2010varying risk premia also perform well as predictors of volatility. While forecasts by macro\u2010finance augmented models also achieve forecasting gains out\u2010of\u2010sample relative to autoregressive benchmarks, the performance varies across asset classes and over time. Copyright \u00a9 2012 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.2298","type":"journal-article","created":{"date-parts":[[2012,8,11]],"date-time":"2012-08-11T07:09:07Z","timestamp":1344668947000},"page":"956-977","source":"Crossref","is-referenced-by-count":262,"title":["A comprehensive look at financial volatility prediction by economic variables"],"prefix":"10.1002","volume":"27","author":[{"given":"Charlotte","family":"Christiansen","sequence":"first","affiliation":[{"name":"CREATES, Department of Economics and Business, Business and Social Sciences Aarhus University  Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maik","family":"Schmeling","sequence":"additional","affiliation":[{"name":"Department of Economics Leibniz Universit\u00e4t Hannover  Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Schrimpf","sequence":"additional","affiliation":[{"name":"Bank for International Settlements (BIS) and CREATES  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2012,8,11]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00418"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1574-0706(05)01015-3"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/2938229"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhl021"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.2006.00836.x"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(02)00131-9"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.3905\/JFI.2009.18.4.024"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1257\/jep.21.2.129"},{"key":"e_1_2_8_10_1","first-page":"177","volume-title":"Proceedings of the American Statistical Association, Business and Economic Statistics Section","author":"Black F","year":"1976"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(86)90063-1"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhp008"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1086\/427633"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhn098"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1086\/593088"},{"key":"e_1_2_8_16_1","doi-asserted-by":"crossref","unstructured":"CakmakliC vanDijkD.2010.Getting the most out of macroeconomic information for predicting stock returns and volatility. 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