Financial Risk Management and Modeling

Accounting for derivatives has stirred important debate among academics, international standard setters and practitioners over the past decade. On the one hand, standard accounting with fair value measurement makes the use of derivatives more transparent, giving clear insights of the firm’s underlying risk exposure. On the other hand, if derivatives qualify for the hedge accounting treatment, the timings mismatch associated with standard accounting is alleviated, so that the temporary income statement volatility may be significantly reduced, and the firm’s risk management policy will be better reflected in financial statements. Under IFRS, hedge accounting has been covered by IFRS 9 from January 1, 2018.

In this chapter, we study the implications of IFRS 9 hedge accounting requirements from the perspective of non-financial firms that use commodity derivatives. After describing the main advances of IFRS 9, we present appropriate methods to estimate hedge ratios and measure hedge effectiveness. We show that time-varying hedge ratios could be used to rebalance hedges and maximize the benefits of hedge accounting. Finally, we use an illustrative case study to explain how a power firm can report carbon hedges in respect of IFRS 7 disclosure requirements to provide transparent and relevant information in financial statements.

This chapter broadly aims at discussing how organisations could effectively assess and manage the risk of corporate fraud. The chapter also clarifies the meaning, nature, and types of corporate fraud; explain the difference between risk assessment and risk management; and emphasise the importance of assessing and managing fraud risk.

The relationship between leverage and the managerial risk-taking behavior has been largely investigated. However, little attention has been attributed to the link between these two variables for small and medium-sized enterprises (SMEs), especially during and after the global crisis in continental Europe. Consequently, this paper tries to fill this gap by examining the impact of leverage on the risk-taking behavior of small business managers in France. Using a sample of 1403 French listed SME-observations over the period 2008 to 2016, the empirical findings show that the risk-taking behavior of corporate managers is positively and significantly related to the corporate leverage. This relationship is more striking and robust after than during the global crisis, especially for low growth firms. Thus, credit institutions seem to favor a high restriction on debt during the crisis and to limit their monitoring scope after the crisis particularly for firms with low conflict of interests in order to limit the related costs.

This paper dissects the dynamic interdependencies between Greece’s sovereign credit default swap (CDS) spreads with the CDS spreads of global systemically important banks (G-SIB) across various credit risk regimes. It seeks to map credit risk transmission channels between Greece and each of the sampled banks to determine whether contagion actually disperses from Greece to banks or vice versa. The findings herein show Greece’s credit risk was contagious for banks during the 2008–09 financial crisis but less contagious during the 2011–13 so-called ‘Greek debt crisis.’ In fact, it is shown herein that there is an increase in credit risk transmissions from G-SIBs to Greece during 2011–13. The regulatory implications of this paper are that too-big-to-fail banks significantly reduced their exposure to Greece following 2008–09 and, to some extent, may have transferred this risk to the European Central Bank (ECB). If this is the case, banks and fiscally troubled European nations, such as Greece, will become more reliant on the ECB and this may lead to a more fragile and dependent global economy. The asset pricing implications of this paper are broadly as follows. First, Greece’s CDS spreads do not exhibit a long-run cointegrating relation with bank CDS spreads and, second, credit risk transmission channels are heterogeneous across credit regimes. Finally, from a behavioral finance perspective, it can be shown that despite salacious news headlines of a ‘Grexit,’ it cannot be empirically shown that Greece was the catalyst for credit risk transmissions to the global commercial banking system during the height of the euro-area debt crisis.

In risk management and portfolio optimization it is important to know which assets move individually or in certain groups to make a diversified portfolio. The statistical uncertainty of the correlation matrix is the main problem into the optimization of a financial portfolio. Indeed, estimates of correlations are often noisy particularly in stress period and unreliable as estimation horizons are always finite. Another drawback in the classical estimation of correlations is that time series are estimated on historical data and prediction based on past data is very difficult, since finding elementary structures in data which are valid and persistent in the future is not really easy. The Markowitz optimization approaches of portfolio suffer from theses estimation errors. From the perspective of machine learning, new approaches have been proposed in the literature of applied finance. Among these techniques, clustering has been considered as a significant method to capture the natural structure of data. The objective of this research is to use data mining approaches for identifying the best clustering indicators for building optimal portfolios. Clustering is an empirical procedure for grouping financial assets into homogeneous groups. The aim of cluster analysis is to maximize similarity within groups of assets and minimize similarity between groups. The similarities and dissimilarities are based on the attribute values and frequently involve distance measures. There are different techniques used for clustering, some are Partitioning based technique, Density based technique, Model based technique, Grid based technique. In this research we consider the symbolic approach based histogram-valued data and clusters as a new approach for investment funds portfolio optimization. Firstly, it is based on aggregating individual level data into group-based summarized by symbols. In our case, symbols are histogram-valued data taking into account variability inside groups. Secondly, for partitioning, we use dynamical clustering which is an extension of K-means where, instead of the means, we use other kinds of centers called ‘kernel’ distributions in our case. After clustering, stock samples are selected from these clusters to create funds of funds optimal portfolios which impose the lowest risk measured in terms of Conditional Value at Risk for a certain return. Funds’ Portfolios are compared during the period of 2008–2016 using the conditional Sharpe ratio and the 2017 year is used to validate our results out of sample. In this research we show that the use of symbolic data clustering algorithms can improve the reliability of the portfolio in terms of the risk adjusted performance.

Nowadays, many quantitative tools exist in portfolio and risk management in order to evaluate a set of investment opportunities, track their performance over time and in the end to evaluate the performance of the entire portfolio. A relatively unknown approach is the Grey Relation Analysis (GRA) approach, developed in the 1980s in order to help when making decisions within modelling of uncertain systems. However, it is still not sufficiently explored in the portfolio and risk management. Thus, this chapter explores the possibilities of using the GRA approach of ranking the stocks in the process of portfolio selection. Two contributions are expected in the chapter. Firstly, a concise and critical overview of the previous applications within the field of portfolio management is provided for the first time in the literature. The second contribution focused on the empirical application of the GRA approach when ranking the stocks based upon the investor’s utility function theory. This research used the first four moments of return distributions when ranking the stocks by using the GRA approach in order to construct portfolios based upon the results. The results indicate that higher moments of return distributions should be taken into consideration in portfolio selection.

The aim of this chapter is to provide insight into how investors and fund managers can handle their decision-making process and foster better allocation of financial assets. Investor sentiment offers promise in helping to understand how financial markets function, as well as to better predict market dynamics. This chapter presents a theoretical framework that is capable of rethinking financial markets. Agent-based approaches to finance and nonlinear models provide insights into the driving forces underlying the stylized facts characterizing financial markets and help to provide explanations for financial instability. We also show that the interdependence of agents can be reflected in interaction networks. Indeed, investors can change from one regime to another. Adjustment delays in prices are difficult to represent by simple linear models. To capture the complexity and further non-linearity generated by investors’ interactions, agent-based models, network analysis and thresholds models are well suited. We examine the extent to which these new tools could explain the persistence of asset price deviations. We highlight how these modeling tools contribute to a better integration of risk sentiment in asset management and thus can best describe financial markets’ reality.

Despite the exponential increase in the literature related to the performance of Alternative Investment Funds (AIFs), risk management with respect to the measurement of performance persistence remains largely unexplored. In this paper, we investigate the impact of geolocation and investment strategy effects on the estimation of risk in performance persistence measurement dynamics. This aspect of risk in performance persistence is crucial as it allows us to show the combined effects of geolocation and investment strategy choice on risk-adjusted performance persistence. We report strong performance persistence when analysing the individual domicile or strategy. However, as we move to consider a combination of both domicile and the investment strategy, we can observe diminished persistence as well as its loss and reversal. The results of our cross-comparison show that the sole reliance on the individual domicile/investment strategy focused clusters can be grossly misleading and lead to capital losses.

We show that an equal-weighted portfolio has a higher total return than a value-weighted portfolio. As one may expect, this is partly because the equal-weighted portfolio has higher exposure to value and size factors, but we show that a considerable part (42%) comes from rebalancing to maintain constant weights. We then demonstrate, through four applications, that inferences from asset-pricing tests are substantially different depending on whether one uses equal- or value-weighted portfolios. These four applications are tests of the: Capital Asset Pricing Model, spanning properties of the stochastic discount factor, relation between characteristics and returns, and pricing of idiosyncratic volatility.

This paper is a comprehensive and complete research on bank failures that we examine from many different perspectives. It compromises a comprehensive dataset of ~60,000 observations for an extensive period (2005–2014) and examines different prediction horizons prior to failure. Moreover, we explore whether the addition of variables related to the diversification of the banks’ activities along with local effects, improve the predictability of the models. Seven popular and widely used machine learning techniques are compared under different performance metrics, using a bootstrap analysis. The results show that mid to long-term prediction improves significantly with the addition of diversification variables. Local effects exist and further improve the results, while, support vector machines, gradient boosting, and random forests outperform traditional models with the performance differences increasing over longer prediction horizons.

… sometimes time flows very rapidly in financial markets while in other periods it moves slowly…

Engle and Russell (1998)

When focusing on a high-frequency trading level, time becomes the most essential element in trading activity, price discovery and essentially in managing intraday risky asset allocation. Information and liquidity, which are considered to be the fundamental forces that shape price formation, are inextricably bound together in how the markets work on that level. Information is price-resolved over time through liquidity, while the intensity of liquidity is affected by the arrival time of information. Consequently, market microstructure challenges the efficient market hypothesis, because none of its assumptions can hold in high frequency trading, where time is irregularly spaced and it is a measure of liquidity, while it also carries information itself. This raises the importance of moving from “calendar time” to “economic time”, which measures not simply how time lapses, but how fast events arrive over time. The counting of (expected) risky events per unit of time can then, become an instantaneous measure of risk.

This chapter is an introduction to climate change risk for the financial sector (banks and investors). We propose to cover how the topic emerged, what it means for financial institutions, what are the specific types of approaches to address it as well as related developments in the industry, how regulation is approaching the question, and what are the next steps for academic research. The chapter is aimed at financial professionals, researchers and policymakers in the area of banking and investment who seek to understand where this new field of research currently is and what evolutions are to expect. The chapter’s ambition is to provide the reader with a snapshot of the current state of the art and guidance on the relevant literature to go further.