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The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms. 相似文献
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Manning AK Hivert MF Scott RA Grimsby JL Bouatia-Naji N Chen H Rybin D Liu CT Bielak LF Prokopenko I Amin N Barnes D Cadby G Hottenga JJ Ingelsson E Jackson AU Johnson T Kanoni S Ladenvall C Lagou V Lahti J Lecoeur C Liu Y Martinez-Larrad MT Montasser ME Navarro P Perry JR Rasmussen-Torvik LJ Salo P Sattar N Shungin D Strawbridge RJ Tanaka T van Duijn CM An P de Andrade M Andrews JS Aspelund T Atalay M Aulchenko Y Balkau B Bandinelli S Beckmann JS Beilby JP Bellis C Bergman RN Blangero J 《Nature genetics》2012,44(6):659-669
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology. 相似文献
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RA Scott V Lagou RP Welch E Wheeler ME Montasser J Luan R Mägi RJ Strawbridge E Rehnberg S Gustafsson S Kanoni LJ Rasmussen-Torvik L Yengo C Lecoeur D Shungin S Sanna C Sidore PC Johnson JW Jukema T Johnson A Mahajan N Verweij G Thorleifsson JJ Hottenga S Shah AV Smith B Sennblad C Gieger P Salo M Perola NJ Timpson DM Evans BS Pourcain Y Wu JS Andrews J Hui LF Bielak W Zhao M Horikoshi P Navarro A Isaacs JR O'Connell K Stirrups V Vitart C Hayward T Esko E Mihailov RM Fraser T Fall BF Voight 《Nature genetics》2012,44(9):991-1005
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control. 相似文献
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A theory of power-law distributions in financial market fluctuations 总被引:13,自引:0,他引:13
Insights into the dynamics of a complex system are often gained by focusing on large fluctuations. For the financial system, huge databases now exist that facilitate the analysis of large fluctuations and the characterization of their statistical behaviour. Power laws appear to describe histograms of relevant financial fluctuations, such as fluctuations in stock price, trading volume and the number of trades. Surprisingly, the exponents that characterize these power laws are similar for different types and sizes of markets, for different market trends and even for different countries--suggesting that a generic theoretical basis may underlie these phenomena. Here we propose a model, based on a plausible set of assumptions, which provides an explanation for these empirical power laws. Our model is based on the hypothesis that large movements in stock market activity arise from the trades of large participants. Starting from an empirical characterization of the size distribution of those large market participants (mutual funds), we show that the power laws observed in financial data arise when the trading behaviour is performed in an optimal way. Our model additionally explains certain striking empirical regularities that describe the relationship between large fluctuations in prices, trading volume and the number of trades. 相似文献
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Chambers JC Zhang W Sehmi J Li X Wass MN Van der Harst P Holm H Sanna S Kavousi M Baumeister SE Coin LJ Deng G Gieger C Heard-Costa NL Hottenga JJ Kühnel B Kumar V Lagou V Liang L Luan J Vidal PM Mateo Leach I O'Reilly PF Peden JF Rahmioglu N Soininen P Speliotes EK Yuan X Thorleifsson G Alizadeh BZ Atwood LD Borecki IB Brown MJ Charoen P Cucca F Das D de Geus EJ Dixon AL Döring A Ehret G Eyjolfsson GI Farrall M Forouhi NG Friedrich N Goessling W Gudbjartsson DF Harris TB Hartikainen AL Heath S 《Nature genetics》2011,43(11):1131-1138
Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function. 相似文献
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Reliable correlation forecasts are of paramount importance in modern risk management systems. A plethora of correlation forecasting models have been proposed in the open literature, yet their impact on the accuracy of value‐at‐risk calculations has not been explicitly investigated. In this paper, traditional and modern correlation forecasting techniques are compared using standard statistical and risk management loss functions. Three portfolios consisting of stocks, bonds and currencies are considered. We find that GARCH models can better account for the correlation's dynamic structure in the stock and bond portfolios. On the other hand, simpler specifications such as the historical mean model or simple moving average models are better suited for the currency portfolio. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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