7 research outputs found
Behavioural Biases and Agent-based Asset Price Modelling
No full textThe price, return and volume series of virtually all traded financial assets share a set of commonly observed statistical characteristics known as the stylized facts of financial data. In the last two decades, a body of literature has developed, attempting to explain these stylized facts as emerging properties from the interaction of a large number of heterogeneous market participants. Thepresent thesis contributes to the literature on heterogeneous agent-based asset pricing models, that is, the computational study of financial markets as evolving systems of interacting agents.Taking a prominent agent-based model (Franke and Westerhoff (2012)) as an example, we observe that its price series violates one of the core properties of real financial time series - its non-stationarity. We overcome this problem by extending the original model and drastically reduce the non-stationarity of the price series generated. Next, we estimate the model's parameters andevaluate the new setting, showing it is able to match a very rich set of stylized facts observed in real financial markets.Now, a well defined agents-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. In this context, we present two new behavioural asset pricing models. First, we define a setting where agents suffer from the disposition effect and test the implications of this behavioural bias on investors' interactions and price settings. We demonstrate that it has a direct impact on the returns series produced by the model, altering important stylized facts such as its heavy tails and volatility clustering. Moreover, we show that the horizon over which investors compute their wealth has no effect on the dynamics produced by the model.Second, we present a new behavioural model of asset pricing where the agents are loss averse, and evaluate its implications. On the one hand, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. On the other hand, we assess the impact of different levels of loss aversion not only on the agents' switching mechanism, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and errors.Finally, we define a further behavioural heterogeneous agent-based asset pricing model with regret and analyse the implications of this behavioural bias on the model's dynamics. We study the coexistence of locally stable attractors of the corresponding nonlinear deterministic system, one of the most common and generic mechanisms for generating important properties observed in real financial markets. By incorporating regret in agents' expectations, we demonstrate that it can destabilise the price series and change a low volatility market regime into a highly volatile one. Consequently, we show that a change in investors' psychology contributes to the emergence of interesting new properties and that regret has the potential to explain key aspects of financialmarkets
Avoiding regret in an agent-based asset pricing model
No full textWe use an agent-based asset pricing model to test the implications of the disposition effect (avoiding regret) on investors’ interactions and price settings. We show that it has a direct impact on the returns series produced by the model, altering important stylized facts such as its heavy tails and volatility clustering. Moreover, we show that the horizon over which investors compute their wealth has no effect on the dynamics produced by the model
An asset pricing model with loss aversion and its stylized facts
No full textA well-defined agent-based model able to match the widely observed properties of financial assets is valuable for testing the implications of various empirically observed heuristics associated with investors behaviour. In this paper, we extend one of the most successful models in capturing the observed behaviour of traders, and present a new behavioural asset pricing model with heterogeneous agents. Specifically, we introduce a new behavioural bias in the model, loss aversion, and show that it causes a major difference in the agents interactions. As we demonstrate, the resulting dynamics achieve one of the major objectives of the field, replicating a rich set of the stylized facts of financial data. In particular, for the first time our model enables us to match the following empirically observed properties: conditional heavy tails of returns, gains/loss asymmetry, volume power-law and long memory and volume-volatility relations
Loss aversion in an agent-based asset pricing model
No full textA well-defined agent-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. Extending one of the most successful models in capturing traders behaviour, we present a new behavioural agent-based asset pricing model. Specifically, we introduce a well-known behavioural bias in the model, loss aversion, and evaluate its implications. First, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. Secondly, we assess the impact of different levels of loss aversion not only on the agents' switching mechanisms, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and systematic errors. Finally, we provide an in-depth analysis of the simulated time series and show the resulting dynamics replicate a rich set of the stylized facts including: absence of autocorrelation, heavy tails, volatility clustering and conditional heavy tails of returns, long memory of absolute returns, as well as volume–volatility relations, gain–loss asymmetry, power-law behaviour and long memory of volume
Avoiding regret in an agent-based asset pricing model
No full textWe use an agent-based asset pricing model to test the implications of the disposition effect (avoiding regret) on investors' interactions and price settings. We show that it has a direct impact on the returns series produced by the model, altering important stylized facts such as its heavy tails and volatility clustering. Moreover, we show that the horizon over which investors compute their wealth has no effect on the dynamics produced by the model
Loss aversion in an agent-based asset pricing model
No full textA well-defined agent-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. Extending one of the most successful models in capturing traders behaviour, we present a new behavioural agent-based asset pricing model. Specifically, we introduce a well-known behavioural bias in the model, loss aversion, and evaluate its implications. First, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. Secondly, we assess the impact of different levels of loss aversion not only on the agents' switching mechanisms, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and systematic errors. Finally, we provide an in-depth analysis of the simulated time series and show the resulting dynamics replicate a rich set of the stylized facts including: absence of autocorrelation, heavy tails, volatility clustering and conditional heavy tails of returns, long memory of absolute returns, as well as volume–volatility relations, gain–loss asymmetry, power-law behaviour and long memory of volume
Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients
No full textInternational audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed
