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Financial Risk Management and Modeling
Risk is the main source of uncertainty for investors, debtholders, corporate managers and other stakeholders. For all these actors, it is vital to focus on identifying and managing risk before making decisions. The success of their businesses depends on the relevance of their decisions and consequently, on their ability to manage and deal with the different types of risk. Accordingly, the main objective of this book is to promote scientific research in the different areas of risk management, aiming at being transversal and dealing with different aspects of risk management related to corporate finance as well as market finance. Thus, this book should provide useful insights for academics as well as professionals to better understand and assess the different types of risk.
Artificial Intelligence and Big Data for Financial Risk Management
This book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment.
Could Market Making Be Profitable in the European Carbon Market?
We investigate when market making can be profitable in the European Carbon Futures market, by developing an order type selection rule, based solely on transaction level data. We employ a granular approach that uses an observable variable, i.e. trading intensity, to extract the liquidity and information price components and we investigate their impact on spreads, volatility and ultimately on the profitability of different order types. We find that market orders are always less profitable than limit orders. In addition, market makers are expected to derive most of their profits in a low trading intensity environment, mainly due to higher liquidity commissions and a lower probability of dealing with better informed agents. In contrast, an unconditional limit order submission strategy from an off-floor trader should not be preferred, apart from a medium trading intensity environment, where information and liquidity premia adequately compensate them for execution and information risk.
Liquidity and Resolution of Uncertainty in the European Carbon Futures Market
We investigate whether liquidity introduces or helps resolve uncertainty in Phase I and the first year of Phase II of the European Carbon futures market. We propose a distinction between 'absolute' or overall liquidity and that which is 'relative' to a benchmark. For this purpose, we suggest volume-weighted duration as a natural measure of trading intensity as a proxy for liquidity, and we model it as a rescaled temporal point process. The new model is called Autoregressive Conditional Weighted Duration (ACWD) and is shown to outperform its discrete modelling counterparts. Liquidity is found to play a dual role, with higher relative liquidity introducing uncertainty and higher absolute liquidity accelerating uncertainty resolution, thus, enhancing market efficiency.
Market Conditions and Order Type Preference
This paper develops an order type-selection trading rule based solely on transaction data, assuming that limit orders require adequate monetary reward for risk exposure. Their suitability is found to depend on the sensitivity of price components to expected market conditions. Limit orders should be preferred during favourable, intense (less intense) market periods when these components are positively (negatively) correlated.
