1,721,039 research outputs found
Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures
Full text linkWhen estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the plug-in estimator particularly when the data contain noise. Krätschmer et al. [Comparative and qualitative robustness for law invariant risk measures. Financ. Stoch., 2014, 18, 271–295.] propose a new framework to examine the qualitative robustness of such estimators for the tail-dependent law invariant risk measures on Orlicz spaces, which is a step further from an earlier work by Cont et al. [Robustness and sensitivity analysis of risk measurement procedures. Quant. Finance, 2010, 10, 593–606] for studying the robustness of risk measurement procedures. In this paper, we follow this stream of research to propose a quantitative approach for verifying the statistical robustness of tail-dependent law invariant risk measures. A distinct feature of our approach is that we use the Fortet–Mourier metric to quantify variation of the true underlying probability measure in the analysis of the discrepancy between the law of the plug-in estimator of the risk measure based on the true data and the one based on perturbed data. This approach enables us to derive an explicit error bound for the discrepancy when the risk functional is Lipschitz continuous over a class of admissible sets. Moreover, the newly introduced notion of Lipschitz continuity allows us to examine the degree of robustness for tail-dependent risk measures. Finally, we apply our quantitative approach to some well-known risk measures to illustrate our results and give an example of the tightness of the proposed error bound
On the quality of service of crash-recovery failure detectors
No full textWe model the probabilistic behavior of a system comprising a failure detector and a monitored crash-recovery target. We extend failure detectors to take account of failure recovery in the target system. This involves extending QoS measures to include the recovery detection speed and proportion of failures detected. We also extend estimating the parameters of the failure detector to achieve a required QoS to configuring the crash-recovery failure detector. We investigate the impact of the dependability of the monitored process on the QoS of our failure detector. Our analysis indicates that variation in the MTTF and MTTR of the monitored process can have a significant impact on the QoS of our failure detector. Our analysis is supported by simulations that validate our theoretical results
Optimal scenario-dependent multivariate shortfall risk measure and its application in risk capital allocation
No full textIn this paper, we propose a novel multivariate shortfall risk measure to evaluate the systemic risk of a financial system, where the allocation weight is scenario-dependent and optimally chosen from a predetermined feasible set, and examine its properties such as (quasi-)convexity and translation invariance. To compute the proposed risk measure, we reformulate it as a two-stage stochastic program. When the underlying risk is discretely distributed, the second-stage program is a finite convex program while for the continuous case, is a semi-infinite program. To tackle the latter, we use the polynomial decision rule to approximate it and reformulate it as a tractable optimization program via the standard sums-of-squares techniques. Some convergence results are established for the approximation scheme. Moreover, we apply the proposed risk measure to the risk capital allocation problem and introduce the scenario-dependent allocation strategy. In contrast to the existing allocation methods, the new approach considers losses of all scenarios and minimizes the systemic risk. We then carry out some numerical tests on the proposed model and computational schemes for a continuous system, a discrete system, and a risk capital allocation problem in life insurance. The results show that our allocation strategy performs better than the Euler allocation rule based on the expected shortfall and the method by Armenti et al., 2018, and is robust to the (un-)systemic changes of the considered dataset. Finally, we extend our model by incorporating the cost of risk capital and investigate its impact on the optimal total amount of risk capital.</p
Dataset for thesis entitled 'Price deviation in the stablecoin market and lead-lag relationships in the traditional cryptocurrency market'
No full textDataset supporting thesis entitled 'Price deviation in the stablecoin market and lead-lag relationships in the traditional cryptocurrency market'.</span
Social machines: how recent technological advances have aided financialisation
No full textIn recent years, financial markets have been fundamentally transformed by innovations in information technology, in particular with regard to the web, social networks, high-speed computer networks and mobile technologies. We borrow the concept of Social Machines from Web Science as a single concept that captures the essence of all these recent technological changes to argue that the emergence of these Social Machines has aided the transformation of financial markets and society. This study explores the formation of these Social Machines with three sample disruptive technologies – automated/high-frequency trading, social network analytics and smart mobile technology. Through critical reflective analysis of these three case studies, we assess the impact of information technology innovation on financialisation. We adopt three case studies – automated trading; market information extraction using social media technologies; and information diffusion and trader decision-making with mobile technology on financial and real sector changes – which demonstrate the increasing trend of transaction velocity, speculative trading, increased complex information network, accelerated inequality and leverage. Our findings demonstrate that technologically enabled financial Social Machines harness crowd wisdom, engage disparate individual traders to produce more accurate price estimations, and have enhanced decision-making capability. However, these same changes can also have a simultaneously detrimental effect on financial and real sectors, in some situations exacerbating underlying distortions, such as misinformation due to complex information networks, speculative trading behaviour, and higher volatility with transaction velocity. Overall, we conclude that these innovations have transformed the fundamental nature of key aspects of the finance industry and society as a whole
Analysing the impact of online news sentiment on individualsʼ sequential trading decisions
No full textSeveral empirical studies have reported that the consequences of prior actions can affect subsequent risky decisions. However, little work has investigated how the news environment in which prior actions were taken alters the impact of prior consequences. In this study, we examine to what extent the effects of individual traders’ prior gains on their subsequent trade size are contingent on different news sentiment environments in which these gains were secured. The results are derived from an individual-level trading dataset containing details of 285,725 trades of 4,857 traders between 2004 and 2013, complemented by a news archive that contains over 20 million news items. We find that individuals increase trade size following prior gains, but the magnitude of the increase depends on the type of the news sentiment environment - amplified/reduced by the news sentiment that was consistent/inconsistent with the prior decisions that generated gains. We suggest the news environment affects individual sequential risky decisions through its effects on emotions – prior gains lead an individual to have positive emotions, and the emotions are amplified/reduced when the individual receives positive/negative feedback from the news sentiment that is consistent/inconsistent with the gain-generating trading decisions. This study contributes to the literature on the roles of contexts and emotions in decision-theory models.<br/
Can graphical displays really improve the communication of financial risk information?
No full textInsufficient understanding of financial risks can cause individuals to make poor financial decisions, and this can have substantial deleterious effects on their wealth, health and psychological well-being. Therefore, it is crucial to promote better engagement and comprehension of risk-related financial information.While evidence shows that numerical information processing and comprehension are significantly influenced by presentation formats (e.g., graphical vs. textual formats), there is mixed evidence on the efficacy of the different formats. For example, some, but not all research shows that lay individuals who view textual-based formats develop a better comprehension of financial risk information than individuals who view graphical formats. However, none of the extant studies have explored why an individual’s comprehension of financial information might be hindered by graphical presentations or how such formats could be better designed to improve comprehension.To address this knowledge gap, 40 participants were asked to process financial information presented using two types of graphical displays (i.e., risk of financial loss from stocks presented using icon arrays; mortgage repayment risks presented using text and bar charts). Think-aloud and think-after methods were employed to collect data on the participants’ levels of comprehension, cognitive processes, and judgments. Verbal protocol analysis revealed that graphical formats were more useful to communicate gist information (general risk impression) than the verbatim information (precise risk information). That is, graphical formats more effectively helped participants to make comparisons between different pieces of financial data and to quickly identify key information. However, in terms of generating deeper insights into financial risk, the results showed that more effortful graph interpretation was required by the participants and that this was most effective when supported by written text. Furthermore, our participants emphasized that textual information was their preferred format for receiving financial risk information. Participants reasoned that textual formats provided more precise information and were easier to interpret, and that the integrations and interpretations that were required to understand the graphical formats increased the likelihood of making errors. Moreover, our study found that although arresting colours appeared helpful in the information identification process, they also elicited disproportionate attentional weighting and emotional reactions. That is, red bars (cf. green bars) were viewed as a warning signal and this led to heightened concerns about risk. Similarly, black icons (cf. grey icons) increased the salience of financial losses, which then contributed to heightened risk awareness. These findings can be utilised to improve financial risk communications
The asymmetric influence of event-based news on cryptocurrency volatility: a survival analysis study of jump and co-jump behaviour in cryptocurrency markets
No full textThis paper examines how cryptocurrency-specific news sentiment influences the timing and occurrence of volatility jumps in cryptocurrency returns, offering a behavioural framework that distinguishes between polarity-driven and state-contingent effects. Using ten years of hourly Event Sentiment Scores (ESS) from RavenPack, we analyse a panel of 87 cryptocurrencies and detect idiosyncratic and systemic jumps in volatility returns using the Bipower Variation method. These discontinuities are modelled using Cox Proportional Hazards estimation to assess how sentiment affects the hazard rate of both jump types.Our results uncover a clear polarity-based asymmetry under calm conditions: mildly positive and negative sentiment both accelerate jump risk, but optimism exerts a stronger effect—consistent with behavioural theories of speculative momentum. However, this asymmetry disappears under systemic stress. During co-jump periods, sentiment effects are symmetrically amplified, suggesting that market synchronisation lowers behavioural thresholds and increases the volatility hazard irrespective of sentiment direction. At the systemic level, both positive and negative sentiment significantly raise the hazard of co-jumps, but without statistically significant asymmetry in polarity.These findings introduce the concept of contagion asymmetry, where the amplification of sentiment-induced risk is not driven by emotional valence but by the system’s vulnerability to synchronised behaviour. Robustness checks across thresholds, coin tiers, time periods, and systemic conditions confirm that the strength and shape of sentiment effects are highly conditional. Methodologically, our use of survival analysis allows for more precise behavioural interpretation by capturing not only whether sentiment matters, but when. This study extends the sentiment-volatility literature by reframing co-jumps as state-contingent contagion events and highlights the importance of timing in modelling crypto-market instability.<br/
The influence of smart phone apps on individuals’ decision making processes: a case study of spread trading investors
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