Measuring market liquidity: An introductory survey

Asset liquidity in modern financial markets is a key but elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant definition of market liquidity has, however, provided to be a difficult task. This paper provides a critical review of the frameworks currently available for modelling and estimating the market liquidity of assets. We consider definitions that stress the role of the bid-ask spread and the estimation of its components that arise from alternative sources of market friction. In this case, intra-daily measures of liquidity appear relevant for capturing the core features of a market, and for their ability to describe the arrival of new information to market participants

Modelling the Dynamics of Credit Spreads of European Corporate Bond Indices

Credit spreads are important financial tools, since they are used as indicators of economic progression, investment decisions, trading and hedging, as well as pricing credit derivatives. Their role has become more significant for the European fixed income markets since the introduction of the Euro, which reshaped the mechanics of the financial environment. The introduction of single currency provided the means for a pan-European economic growth and cross-border development, liberalized a vast inflow of capital which was once fragmented into different currencies, and provided the dynamics of cross-border investments around a unified legislative framework. Thus, the main subject of the thesis is to provide further insight into and investigate the nature and the dynamics of credit spreads of European corporate bond indices during the credit crisis period. Traditional quantitative credit risk models assume that changes in spreads are normally distributed but empirical evidence shows that they are likely to be skewed and fat-tailed, and if they are ignored then the calculation of loss probabilities will be seriously compromised. Therefore, the first area of investigation aims to provide further insight into the dynamics of higher moments and regime shifts in credit spread changes by applying a GARCH-type model that allows for time-varying volatility, skewness and kurtosis, as well as a Markov regime-switching GARCH specification to capture the structural changes in the volatility of credit spreads. Furthermore, a comparison of the different specifications is undertaken in order to assess which model better fits the empirical distribution of the data and produces best Value-at-Risk estimates. The results presented have significant implications for risk management, as well as in the pricing of credit derivatives. The second area of investigation is to assess and evaluate time-varying correlation of credit spreads. Different multivariate GARCH models, such as Orthogonal-GARCH, the Constant and Dynamic Correlation GARCH models, Risk Metrics and Diagonal-BEKK, are applied to examine the behaviour and dynamics of time-varying correlation. Additionally, the performance of the proposed models is examined by determining whether they produce accurate VaR estimates. The study finds evidence in support of time-varying correlation coefficients between credit spreads which appears to be market dependent and has implications for pricing of derivatives, portfolio selection, trading and hedging activities, as well as risk management. Finally, the impact of economic determinants of credit spreads such as the risk-free rate, inflation, as well as equity and commodity indices and volatilities, are investigated over different market conditions using regime switching models. The results highlight how the effect of the determinants on credit spreads varies across different market conditions and point to the existence of non-linear relationship between the determinants and credit spread changes. The study reveals that the regime dependent determinants have significant explanatory power only in the high volatility regime. Finally, it is shown that the feed-forward neural network model out-performs the other specifications applied in this study in terms of estimating out-of-sample mean forecasts

Measuring market liquidity: an introductory survey

Asset liquidity in modern financial markets is a key but elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant definition of market liquidity has, however, provided to be a difficult task. This paper provides a critical review of the frameworks currently available for modelling and estimating the market liquidity of assets. We consider definitions that stress the role of the bid-ask spread and the estimation of its components that arise from alternative sources of market friction. In this case, intra-daily measures of liquidity appear relevant for capturing the core features of a market, and for their ability to describe the arrival of new information to market participants.market microstructure; liquidity risk; frictions; transaction costs

Modelling the dynamics of credit spreads of European corporate bond indices

Credit spreads are important financial tools, since they are used as indicators of economic progression, investment decisions, trading and hedging, as well as pricing credit derivatives. Their role has become more significant for the European fixed income markets since the introduction of the Euro, which reshaped the mechanics of the financial environment. The introduction of single currency provided the means for a pan-European economic growth and cross-border development, liberalized a vast inflow of capital which was once fragmented into different currencies, and provided the dynamics of cross-border investments around a unified legislative framework. Thus, the main subject of the thesis is to provide further insight into and investigate the nature and the dynamics of credit spreads of European corporate bond indices during the credit crisis period. Traditional quantitative credit risk models assume that changes in spreads are normally distributed but empirical evidence shows that they are likely to be skewed and fat-tailed, and if they are ignored then the calculation of loss probabilities will be seriously compromised. Therefore, the first area of investigation aims to provide further insight into the dynamics of higher moments and regime shifts in credit spread changes by applying a GARCH-type model that allows for time-varying volatility, skewness and kurtosis, as well as a Markov regime-switching GARCH specification to capture the structural changes in the volatility of credit spreads. Furthermore, a comparison of the different specifications is undertaken in order to assess which model better fits the empirical distribution of the data and produces best Value-at-Risk estimates. The results presented have significant implications for risk management, as well as in the pricing of credit derivatives. The second area of investigation is to assess and evaluate time-varying correlation of credit spreads. Different multivariate GARCH models, such as Orthogonal-GARCH, the Constant and Dynamic Correlation GARCH models, Risk Metrics and Diagonal-BEKK, are applied to examine the behaviour and dynamics of time-varying correlation. Additionally, the performance of the proposed models is examined by determining whether they produce accurate VaR estimates. The study finds evidence in support of time-varying correlation coefficients between credit spreads which appears to be market dependent and has implications for pricing of derivatives, portfolio selection, trading and hedging activities, as well as risk management. Finally, the impact of economic determinants of credit spreads such as the risk-free rate, inflation, as well as equity and commodity indices and volatilities, are investigated over different market conditions using regime switching models. The results highlight how the effect of the determinants on credit spreads varies across different market conditions and point to the existence of non-linear relationship between the determinants and credit spread changes. The study reveals that the regime dependent determinants have significant explanatory power only in the high volatility regime. Finally, it is shown that the feed-forward neural network model out-performs the other specifications applied in this study in terms of estimating out-of-sample mean forecasts.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modelling the dynamics of credit spreads of European corporate bond indices

Credit spreads are important financial tools, since they are used as indicators of economic progression, investment decisions, trading and hedging, as well as pricing credit derivatives. Their role has become more significant for the European fixed income markets since the introduction of the Euro, which reshaped the mechanics of the financial environment. The introduction of single currency provided the means for a pan-European economic growth and cross-border development, liberalized a vast inflow of capital which was once fragmented into different currencies, and provided the dynamics of cross-border investments around a unified legislative framework. Thus, the main subject of the thesis is to provide further insight into and investigate the nature and the dynamics of credit spreads of European corporate bond indices during the credit crisis period. Traditional quantitative credit risk models assume that changes in spreads are normally distributed but empirical evidence shows that they are likely to be skewed and fat-tailed, and if they are ignored then the calculation of loss probabilities will be seriously compromised. Therefore, the first area of investigation aims to provide further insight into the dynamics of higher moments and regime shifts in credit spread changes by applying a GARCH-type model that allows for time-varying volatility, skewness and kurtosis, as well as a Markov regime-switching GARCH specification to capture the structural changes in the volatility of credit spreads. Furthermore, a comparison of the different specifications is undertaken in order to assess which model better fits the empirical distribution of the data and produces best Value-at-Risk estimates. The results presented have significant implications for risk management, as well as in the pricing of credit derivatives. The second area of investigation is to assess and evaluate time-varying correlation of credit spreads. Different multivariate GARCH models, such as Orthogonal-GARCH, the Constant and Dynamic Correlation GARCH models, Risk Metrics and Diagonal-BEKK, are applied to examine the behaviour and dynamics of time-varying correlation. Additionally, the performance of the proposed models is examined by determining whether they produce accurate VaR estimates. The study finds evidence in support of time-varying correlation coefficients between credit spreads which appears to be market dependent and has implications for pricing of derivatives, portfolio selection, trading and hedging activities, as well as risk management. Finally, the impact of economic determinants of credit spreads such as the risk-free rate, inflation, as well as equity and commodity indices and volatilities, are investigated over different market conditions using regime switching models. The results highlight how the effect of the determinants on credit spreads varies across different market conditions and point to the existence of non-linear relationship between the determinants and credit spread changes. The study reveals that the regime dependent determinants have significant explanatory power only in the high volatility regime. Finally, it is shown that the feed-forward neural network model out-performs the other specifications applied in this study in terms of estimating out-of-sample mean forecasts.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Forecasting Value-at-Risk with Time-Varying Variance, Skewnessn and Kurtosis in an Exponential Weighted Moving Average Framework

This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram-Charlier density in which skewness and kurtosis appear directly in the functional form of this density. In this setting VaR can be described as a function of the time-varying higher moments by applying the Cornish-Fisher expansion series of the first four moments. An evaluation of the predictive performance of the proposed model in the estimation of 1-day and 10-day VaR forecasts is performed in comparison with the historical simulation, filtered historical simulation and GARCH model. The adequacy of the VaR forecasts is evaluated under the unconditional, independence and conditional likelihood ratio tests as well as Basel II regulatory tests. The results presented have significant implications for risk management, trading and hedging activities as well as in the pricing of equity derivatives

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Measuring and Modelling the Market Liquidity of Stocks: Methods and Issues Measuring and Modelling the Market Liquidity of Stocks

Abstract The liquidity of an asset in modern financial markets is a key and, yet, elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant definition of market liquidity has, however, provided to be a difficult task. This paper provides a critical review of the frameworks currently available for modelling and estimating the market liquidity of stocks. We discuss definitions of market liquidity that stress the role of the bid-ask spread and the estimation of its components arising from alternative sources of market friction. In this case, measures of liquidity based on intra-daily data are relevant for capturing the core features of a market, and for their ability to account for the arrival of new information

Measuring and Modelling the Market Liquidity of Stocks: Methods and Issues

The liquidity of an asset in modern financial markets is a key and, yet, elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant definition of market liquidity has, however, provided to be a difficult task. This paper provides a critical review of the frameworks currently available for modelling and estimating the market liquidity of stocks. We discuss definitions of market liquidity that stress the role of the bid-ask spread and the estimation of its components arising from alternative sources of market friction. In this case, measures of liquidity based on intra-daily data are relevant for capturing the core features of a market, and for their ability to account for the arrival of new information