| Title: | Applied Quantitative Finance |
| Authors: | Härdle, Wolfgang Karl |
| Keywords: | Quantitative Finance Statistics and Computing |
| Issue Date: | 2017 |
| Publisher: | Springer |
| Abstract: | The third edition of Applied Quantitative Finance moves the focus to risk management. As a consequence, we changed the basic structure from four to three chapters with many more contributions to market and credit risk. We revisit important market risk issues in Chap. 1. Chapter 2 introduces novel concepts in credit risk along with renewed quantitative methods being proposed accordingly. A wider range of coverage in recent development of credit risk and its management is presented in this version. The last chapter is on dynamics of risk management and includes risk analysis of energy markets and for cryptocurrencies. Digital assets, such as block chain-based currencies, become popular but are theoretically challenging when based on conventional methods. A modern text mining method called Dynamic Topic Modelling is introduced in detail and applied to the message board of Bitcoins. A time-varying LASSO technique for tail events is at the heart of a new financial risk meter. This third edition brings together modern risk analysis based on quantitative methods and textual analytics for the need of the new challenges in banking and finance. |
| Description: | In empirical finance, multivariate volatility models are widely used to capture both volatility clustering and contemporaneous correlation of asset return vectors. In higher dimensional systems, parametric specifications often become intractable for empirical analysis owing to large parameter spaces. On the contrary, feasible specifications impose strong restrictions that may not be met by financial data as, for instance, constant conditional correlation (CCC). Recently, dynamic conditional correlation (DCC) models have been introduced as a means to solve the trade off between model feasibility and flexibility. Here, we employ alternatively the CCC and the DCC modeling framework to evaluate the Value-at-Risk associated with portfolios comprising major U.S. stocks. In addition, we compare their perfor- mances with corresponding results obtained from modeling portfolio returns directly via univariate volatility models. |
| URI: | http://localhost:8080/xmlui/handle/123456789/254 |
| ISBN: | 978-3-662-54486-0 |
| Appears in Collections: | ARTS & SCIENCE |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2017_Book_AppliedQuantitativeFinance.pdf | 8.26 MB | Adobe PDF | View/Open |
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