Danielsson, J., and de Vries, C.G. (1997) Tail Index and Quantile Estimation with Very High Frequency Data, working paper, Tinbergen Institute, Rotterdam, The Netherlands.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a normal distribution, especially over short horizons. Early studies along these lines include Mandelbrot [38] Fama [19] Praetz [44] and Blattberg and Gonedes [8] More recent investigations, some motivated by value at risk, include Bouchaud, Sornette, and Potters [9] Danielsson and de Vries [12], Eberlein and Keller [15] Eberlein, Keller, and Prause [16] Embrechts, McNeil, and Straumann [18] Hosking, Bonti, and Siegel [26] Huisman, Koedijk, Kool, and Palm [27] Koedijk, Huisman, and Pownall [33] McNeil and Frey [40] Heyde [25] Using di#erent approaches to the problem and di#erent ....

....of the t density decay at a polynomial rate of # 1, so the parameter # determines the heaviness of the tail and the number of finite moments. Empirical support for modeling univariate returns with a t distribution or t like tails can be found in Blattberg and Gonedes [8] Danielsson and de Vries [12], Hosking et al. 26] Huisman et al. 27] Hurst and Platen [28] Koedijk et al. 33] and Praetz [44] There are many possible multivariate distributions with t marginals. We follow Anderson [2] Tong [51] and others in working with a particular class of multivariate distributions having t ....

Danielsson, J., and de Vries, C.G. (1997) Tail Index and Quantile Estimation with Very High Frequency Data, working paper, Tinbergen Institute, Rotterdam, The Netherlands.

....should be considered as equally important as (if not more important than) the Normal distribution. The tails of the Normal distribution are too thin to address the extreme loss. We will not describe the Hill estimator approach in this paper; we refer the reader to the references above and also to Danielsson de Vries (1997). 2 General Theory Let X 1 ; X 2 ; be identically distributed random variables with unknown underlying distribution function F (x) PfX i xg. We work with distribution functions and not densities. The interpretation of these random risks is left to the reader. They might be: Daily ....

Danielsson, J. & de Vries, C. (1997), `Tail index and quantile estimation with very high frequency data', Journal of Empirical Finance 4, 241-257.

....the m th largest log return: It is a simple matter to convert an estimate of into estimates of the desired quantiles and probabilities. The Hill estimator has been used in empirical financial settings, ranging from early work by Koedijk, Schafgans, and de Vries (1990) to more recent work by Danielsson and de Vries (1997). It also has good theoretical properties; it can be shown, for example, that it is consistent and asymptotically normal, assuming the data are iid and that m grows at a suitable rate with sample size. But beware: if tail estimation via EVT offers opportunities, it is also fraught with pitfalls, ....

Danielsson, J., and C. G. de Vries. 1997. "Tail Index and Quantile Estimation with Very High Frequency Data." JOURNAL OF EMPIRICAL FINANCE 4: 241-57.

....is appropriate for the calculation of large loss forecasts. Furthermore, even if the time horizon is shorter, financial institutions often prefer unconditional risk forecast methods to avoid undesirable frequent changes in risk limits for traders and portfolio managers. For more in this issue, see Danielsson (2000). For a typical large portfolio (in terms of number of assets) the conditional approach mayalso just not be feasible since this requires constructing and updating huge conditional variance covariance matrices. 3 2.1.2Condi3 55P Models Building on the realization that returns exhibit ....

....one has hit a high volatilityregime. Per contrast, GARCH performs better in signalling the continuation of a high risk regime since it adapts to the new situation. The conditional GARCH methodologythus necessarilyimplies more volatile risk forecasts then the unconditional approach; see e.g. Danielsson (2000) who find that risk volatilityfrom a GARCH model can be 4 times higher than for an unconditional model. Because the GARCH methodologyquickly adapts to recent market developments, it meets the VaR constraint more frequentlythan the unconditional approach. But this frequencyis just one aspect, the ....

Danielsson, J., and C. G. de Vries (1997b): "Tail index and quantile estimation with veryhigh frequencydata," Journal of Empirical Finance, 4, 241--257.

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Danielsson, J. and C. de Vries (1997) : "Tail Index and Quantile Estimation with Very High Frequency Data", Journal of Empirical Finance, 4, 241 - 257.

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Danielsson, J., and C. de Vries (1997b): \Tail index and quantile estimation with very high frequency data," Journal of Empirical Finance, 4, 241-257.

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Danielsson, J., and C. de Vries (1997a): \Tail index and quantile estimation with very high frequency data," Journal of Empirical Finance, 4, 241-257.

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Danielsson, J. & Casper G. de Vries (1997) Tail index and quantile estimation with very high frequency data, J. Empirical Finance (4)2-3 (1997) pp. 241-258

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Danielsson, J. & Casper G. de Vries (1997) Tail index and quantile estimation with very high frequency data, J. Empirical Finance (4)2-3 (1997) pp. 241-258

No context found.

Danielsson, J. and C. de Vries (1997) : "Tail Index and Quantile Estimation with Very High Frequency Data", Journal of Empirical Finance, 4, 241 - 257.

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