US Corporate Default Swap Valuation: The Market Liquidity Hypothesis and Autonomous Credit Risk

This paper develops a reduced form three-factor model which includes a liquidity proxy of market conditions which is then used to provide implicit prices. The model prices are then compared with observed market prices of credit default swaps to determine if swap rates adequately reflect market risks. The findings of the analysis illustrate the importance of liquidity in the valuation process. Moreover, market liquidity, a measure of investors. willingness to commit resources in the credit default swap (CDS) market, was also found to improve the valuation of investors. autonomous credit risk. Thus a failure to include a liquidity proxy could underestimate the implied autonomous credit risk. Autonomous credit risk is defined as the fractional credit risk which does not vary with changes in market risk and liquidity conditions.

Valuation of a new entrant in an oligopolistic market, including its option to abandon: A real-life case

The purpose of this paper is to increase current empirical evidence on the relevance of real options for explaining firm investment decisions in oligopolistic markets. We study an actual investment case in the Spanish mobile telephony industry, the entrant in the market of a new operator, Yoigo. We analyze the option to abandon in order to show the relevance of the possibility of selling the company in an oligopolistic market where competitors are not allowed free entrance. The NPV (net present value) of the new entrant is calculated as a starting point. Then, based on the general approach proposed by Copeland and Antikarov (2001), a binomial tree is used to model managerial flexibility in discrete time periods, and value the option to abandon. The strike price of the option is calculated based on incremental EBITDA margins due to selling customers or merging with a competitor.

Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.