S. Caserta
http://repub.eur.nl/ppl/8133/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryAuctions with Numerous Bidders
http://repub.eur.nl/pub/6590/
Tue, 01 Mar 2005 00:00:01 GMT<div>S. Caserta</div><div>C.G. de Vries</div>
We study auctions in which the number of potential bidders is large, such as in Internet auctions. With numerous bidders, the expected revenue and the optimal bid function in a first price auction result in complicated expressions, except for a few simple distribution function for the bidders' valuations. We show that these expressions can be well approximated using extreme value theory without assuming a particular distribution function. The theory is applied to data from Internet auctions.Extreme value theory and statistics for heavy tail data
http://repub.eur.nl/pub/12381/
Wed, 01 Jan 2003 00:00:01 GMT<div>S. Caserta</div><div>C.G. de Vries</div>
A scientific way of looking beyond the worst-case return is to employ statistical
extreme value methods. Extreme Value Theory (EVT) shows that the probability on
very large losses is eventually governed by a simple function, regardless the specific
distribution that underlies the return process. This limit result can be exploited to
construct semi-parametric portfolio Value at Risk (VaR) estimates around and beyond
the largest observed loss. Such extreme VaR estimates can be useful inputs for
scenario analysis and stress testing. The aim of this chapter is to introduce the reader
to extreme value theory and the statistics of extremes.More means Worse – Asymmetric Information, Spatial Displacement and Sustainable Heritage Tourism
http://repub.eur.nl/pub/6872/
Fri, 30 Mar 2001 00:00:01 GMT<div>S. Caserta</div><div>A.P. Russo</div>
This paper analyses the market transformations in heritage tourism destinations when excessive tourism demand determines the emergence of a class of excursionists among visitors. Building on the approach of Keane (1997) and Shapiro (1983), our model highlights some important dimensions of sustainable tourism development. The lesser capacity of excursionists to learn the true quality of the tourist goods increases the convenience for producers to cut back on quality. To continue to serve high quality goods and keep up the reputation of the destination as demand continues to grow, producers need to gain a mark-up on price that might not be sustained in a competitive market. Hence the decline in “high-paying” demand segments which are increasingly susbstituted by visitors with lesser quality expectations. In the end, the dynamics explained with this approach are consistent with – and represent an economic reinterpretation of – the class of evolutionary models known as “destination life cycle”, when they are applied to heritage cities. The regulator achieves a sustainable growth if it could enforce quality or information standards. However, the process should be managed at a spatial level that is rarely matched by formal administrative competencies. Traditional tourism strategies are seldom successful when they try to prevent excessive growth by discouraging daily visits. This model helps to identify alternative and more appropriate policy instruments.Abnormal Returns, Risk, and Options in Large Data Sets
http://repub.eur.nl/pub/7745/
Fri, 09 Oct 1998 00:00:01 GMT<div>S. Caserta</div><div>J. Danielsson</div><div>C.G. de Vries</div>
Large data sets in finance with millions of observations have become widely available. Such data sets enable the construction of reliable semi-parametric estimates of the risk associated with extreme price movements. Our approach is based on semi-parametric statistical extreme value analysis, and compares favourably with the conventional finance normal distribution based approach. It is shown that the efficiency of the estimator of the extreme returns may benefit from high frequency data. Empirical tail shapes are calculated for the German Mark-US Dollar foreign exchange rate, and we use the semi- parametric tail estimates in combination with the empirical distribution function to evaluate the returns on exotic options.Abnormal returns, risk and options in large date sets
http://repub.eur.nl/pub/12400/
Thu, 01 Jan 1998 00:00:01 GMT<div>S. Caserta</div><div>J. Danielsson</div><div>C.G. de Vries</div>
Large data sets in finance with millions of observations have become widely available. Such data sets enable the construction of reliable semi-parametric estimates of the risk associated with extreme price movements. Our approach is based on semi-parametric statistical extreme value analysis, and compares favorably with the conventional finance normal distribution based approach. It is shown that the efficiency of the estimator of the extreme returns may benefit from high frequency data. Empirical tail shapes are calculated for the German Mark—US Dollar foreign exchange rate, and we use the semi-parametric tail estimates in combination with the empirical distribution function to evaluate the returns on exotic options.