Analyzing the influence of Value at Risk on financial markets through agent-based modeling

2013 ◽  
Vol 17 (4) ◽  
pp. 257-266 ◽  
Author(s):  
Hiroshi Takahashi
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Agent Based Modeling ◽  
Agent Based

Agent-based economic models and econometrics

2012 ◽  
Vol 27 (2) ◽  
pp. 187-219 ◽  
Author(s):  
Shu-Heng Chen ◽  
Chia-Ling Chang ◽  
Ye-Rong Du
Keyword(s):  
Financial Markets ◽  
Agent Based Modeling ◽  
Computational Finance ◽  
Agent Based ◽  
Second Stage ◽  
The Past ◽  
Stage Development ◽  
Three Stages ◽  
The One ◽  
Two Stages

AbstractThis paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets.

Analyzing the Influences of Passive Investment Strategies on Financial Markets via Agent-Based Modeling

2008 ◽  
pp. 224-238 ◽  
Author(s):  
Hiroshi Takahashi ◽  
Satoru Takahashi ◽  
Takao Terano
Keyword(s):  
Asset Pricing ◽  
Financial Markets ◽  
Financial Market ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Investment Strategies ◽  
Agent Based Model ◽  
Efficient Market ◽  
Agent Based ◽  
Price Fluctuations

This chapter develops an agent-based model to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. This analysis focuses on the effects of Passive Investment Strategy in a financial market. From the extensive analyses, we have found that (1) Passive Investment Strategy is valid in a realistic efficient market, however, it could have bad influences such as instability of market and inadequate asset pricing deviations, and (2) under certain assumptions, Passive Investment Strategy and Active Investment Strategy could coexist in a Financial Market.

TRADER SPECIES WITH DIFFERENT DECISION STRATEGIES AND PRICE DYNAMICS IN FINANCIAL MARKETS: AN AGENT-BASED MODELING PERSPECTIVE

2010 ◽  
Vol 09 (02) ◽  
pp. 327-344 ◽  
Author(s):  
WEI ZHANG ◽  
GEN LI ◽  
XIONG XIONG ◽  
YONG JIE ZHANG
Keyword(s):  
Financial Markets ◽  
Asset Price ◽  
Agent Based Modeling ◽  
Price Dynamics ◽  
Trading Behavior ◽  
Agent Based ◽  
Price Process ◽  
Trading Decisions ◽  
The Individual ◽  
Analytical Approaches

Investors with different trading strategies can be viewed as different "species" in financial markets. Since the asset price is ultimately determined by the individual trading decisions, the combination and evolution of different trader species in financial market ecology will have great impact to the price dynamics. Considering the limitations and shortcomings of traditional analytical approaches in financial economics in dealing with this issue, an agent-based computational model is introduced in this paper. With the co-existence of 3-type trader species that make different decisions based on their own beliefs and constrains, it is found that although rational speculation destabilizes the price process with the presence of positive feedback strategy, as suggested in the literature, introducing extra noise trading behavior to the market will make the price process back to a more stationary situation, meaning that the market will be healthier if more diversified trader species co-exist in the markets.

scholarly journals Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

2021 ◽  
Author(s):  
Massimiliano Frezza ◽  
Sergio Bianchi ◽  
Augusto Pianese
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Average Method ◽  
Moving Average ◽  
Computational Approach ◽  
Local Regularity ◽  
Price Process ◽  
Weighted Moving Average ◽  
Pointwise Regularity

AbstractA new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

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