The Role of Risk Aversion and Technical Trading in the Behaviour of Financial Markets

2008 ◽  
pp. 169-179
Author(s):  
José Antonio Pascual
Keyword(s):  
Financial Markets ◽  
Financial Economics ◽  
Stock Markets ◽  
Financial Time Series ◽  
Unit Roots ◽  
Social Simulation ◽  
Statistical Features ◽  
Volatility Clustering ◽  
Agent Based ◽  
Excess Volatility

In this paper we show how agent based social simulation helps us to improve some of the traditional models and theories in financial economics. In particular, we explore the links between the micro-behaviour of investors and the aggregated behaviour of Stock Markets. First, we build an agent based model of an artificial financial market, populated only with rational investors. We observe that the statistical features of this market are in agreement with the theoretical markets suggested by mainstream financial economics, but far away from the features shown by real financial markets, like the Spanish Ibex-35, the Spanish Stock Market main Index. In order to fill the gap, we introduce heterogeneity in the model. We add psychological investors, as suggested by Kahnemen and Tversky (1979), and we are able to reproduce non-normality, excess kurtosis, excess volatility, and volatility clustering. Then, we introduce technical traders, and we also get from the model higher levels of excess volatility and unit roots. In other words, psychological dealers seem to be responsible for volatility clustering, whereas technical traders trend to introduce unit roots into the process. All these “financial patterns” are a common feature not only for Spanish Ibex-35, also the most important stock markets. We conclude that agent based social simulation helps us to fill the gap between economic theory and real markets, as we explain the statistical features of financial time series from the bottom-up.

scholarly journals Behavioral agent-based framework for interacting financial markets

2020 ◽  
Vol 5 (2) ◽  
pp. 94-115
Author(s):  
Heba M. Ezzat
Keyword(s):  
Financial Markets ◽  
Trading Strategy ◽  
Past Performance ◽  
Time Step ◽  
Monetary Policies ◽  
Content Type ◽  
Agent Based ◽  
Market Makers ◽  
Novel Approach ◽  
Excess Volatility

Purpose This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored. Design/methodology/approach The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders. Findings The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure. Practical implications Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability. Originality/value This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

scholarly journals MARKET FLUCTUATIONS EXPLAINED BY DIVIDENDS AND INVESTOR NETWORKS

2017 ◽  
Vol 20 (08) ◽  
pp. 1750007 ◽  
Author(s):  
MATTHEW OLDHAM
Keyword(s):  
Stock Market ◽  
Complex Systems ◽  
Financial Markets ◽  
Network Topology ◽  
Network Science ◽  
Agent Based ◽  
Excess Volatility ◽  
Artificial Stock Market ◽  
Analytical Frameworks ◽  
Insight Into

The inability of investors and academics to consistently predict, and understand the behavior of financial markets has forced the search for alternative analytical frameworks. Analyzing financial markets as complex systems is a framework that has demonstrated great promises, with the use of agent-based models (ABMs) and the inclusion of network science playing an important role in increasing the relevance of the framework. Using an artificial stock market created via an ABM, this paper provides a significant insight into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. The paper demonstrates that the network topology that investors form and the dividend policy of firms significantly affect the behavior of the market. However, if investors have a bias to following their neighbors then the topology becomes redundant. By successfully addressing these issues this paper helps refine and shape a variety of additional research tasks for the use of ABMs in uncovering the dynamics of financial markets.

scholarly journals MODELLING VOLATILITY SPILLOVERS, CROSS-MARKET CORRELATION AND CO-MOVEMENTS BETWEEN STOCK MARKETS IN EUROPEAN UNION: AN EMPIRICAL CASE STUDY

2021 ◽  
Vol 19 (01) ◽  
pp. 70-90
Author(s):  
Jatin Trivedi ◽  
Cristi Spulbar ◽  
Ramona Birau ◽  
Amir Mehdiabadi
Keyword(s):  
European Union ◽  
Financial Markets ◽  
Empirical Analysis ◽  
Stock Markets ◽  
The European Union ◽  
Volatility Clustering ◽  
Volatility Spillovers ◽  
Considerable Impact ◽  
European Stock Markets ◽  
The Empirical Analysis

Purpose – This article examines volatility spillovers, cross-market correlation, and comovements between selected developed and former communist emerging stock markets in the European Union. Modelling the behavioural dynamics of European stock markets represents a vital topic in a fascinating context, but also a current challenge of great interest. Research Methodology – We propose to estimate and model volatility using GARCH family models for selected European markets. We aim to explore volatility movement, presence of leverage effect/ asymmetry in selected financial markets. Findings – The econometric approach includes GARCH (1, 1) models for the sample period from 1, January 2000 to 12, July 2018. The empirical results revealed that exists a significant presence of volatility clustering in all selected financial markets except Poland and Croatia. The empirical analysis also indicates that both recent and past news generate a considerable impact on present volatility. Research limitations – Our empirical study has certain limitations regarding the relatively small number of only eight stock markets. Practical implications – It can provide a useful perspective for researchers, academics, investors, investment managers, decision-makers, and scientists. Originality/Value – The empirical analysis is focused on 8 European stock markets, which are classified as developed (Spain, UK, Germany, and France) and emerging (Poland, Hungary, Croatia, and Romania).

scholarly journals More on Calendar Effects on Islamic Stock Markets

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Christoph S. Weber ◽  
Philipp Nickol
Keyword(s):  
Financial Markets ◽  
Stock Markets ◽  
Efficient Market Hypothesis ◽  
Comprehensive Analysis ◽  
Seasonal Patterns ◽  
Gregorian Calendar ◽  
Volatility Clustering ◽  
Calendar Effects ◽  
Calendar Anomalies ◽  
Developed Stock Markets

AbstractThere is a long tradition in detecting anomalies of the Efficient Market Hypothesis. Among these are calendar anomalies, first described by French back in 1980. Whilst there is a plethora of studies for well-developed stock markets, there is still a lack of comprehensive studies for some small or emerging financial markets. It is particularly interesting to test not only for calendar effects in the conventional Gregorian calendar but also in other calendars like the Hijri calendar. Thus, the aim of this study is to provide a comprehensive analysis of calendar anomalies on Islamic stock markets. Firstly, we deliver a complete literature review of previous studies dealing with calendar effects on Islamic stock markets showing that there is still a lack of consensus about the effects. Secondly, we analyse whether there are any seasonal patterns in stock markets’ returns by conventional estimation techniques. Thirdly, we study whether those calendar effects are still apparent when we control for volatility clustering. In fact, there is evidence for calendar anomalies on all stock markets. However, those effects are prone to changes when different models or distributions are used. One should, therefore, be careful when interpreting calendar effects on Islamic stock markets. The evidence for theories put forward when analysing Western stock markets is – at best – mild.

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