A simple model for asset price bubble formation and collapse (original) (raw)

We consider a simple stochastic differential equation for modeling bubbles in social context. A prime example is bubbles in asset pricing, but similar mechanisms may control a range of social phenomena driven by psychological factors (for example, popularity of rock groups, or a number of students pursuing a given major). Our goal is to study the simplest possible model in which every term has a clear meaning and which demonstrates several key behaviors. The main factors that enter are tendency of mean reversion to a stable value, speculative social response triggered by trend following and random fluctuations. The interplay of these three forces may lead to bubble formation and collapse. Numerical simulations show that the equation has distinct regimes depending on the values of the parameters. We perform rigorous analysis of the weakly random regime, and study the role of change in fundamentals in igniting the bubble.

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Bubbles and market crashes

Computational Economics, 1998

We present a dynamical theory of asset price bubbles that exhibits the appearance of bubbles and their subsequent crashes. We show that when speculative trends dominate over fundamental beliefs, bubbles form, leading to the growth of asset prices away from their fundamental value. This growth makes the system increasingly susceptible to any exogenous shock, thus eventually precipitating a crash. We also present computer experiments which in their aggregate behavior confirm the predictions of the theory.

Long scale evolution of a nonlinear stochastic dynamic system for modeling market price bubbles

Physics Letters A, 2000

This Letter investigates the stochastic dynamics of a simplified agent-based microscopic model describing stock market evolution. Our mathematical model includes a stochastic market and a sealed-bid double auction. The dynamics of the model Ž . are determined by the game of two types of traders: i 'intelligent' traders whose strategy is based on nonlinear technical 1 Ž . data analysis and ii 'random' traders that act without a consistent strategy. We demonstrate the effect of time-scale separations on the market dynamics. We study the characteristics of the market relaxation in response to perturbations caused by large cash flows generated between these two groups of traders. We also demonstrate that our model exhibits the formation of a price bubble 2 and the subsequent transition to a bear market 3 . q 2000 Published by Elsevier Science B.V. S.A. Kiselev .

A liquidity-based model for asset price bubbles

Quantitative Finance, 2012

We provide a new liquidity based model for financial asset price bubbles that explains bubble formation and bubble bursting. The martingale approach , Jarrow et al. ) to modeling price bubbles assumes that the asset's market price process is exogenous and the fundamental price, the expected future cash flows under a martingale measure, is endogenous. In contrast, we define the asset's fundamental price process exogenously and asset price bubbles are endogenously determined by market trading activity. This enables us to generate a model which explains both bubble formation and bubble bursting. In our model, the quantity impact of trading activity on the fundamental price process -liquidity risk -is what generates price bubbles. We study conditions under which asset price bubbles are consistent with no arbitrage opportunities and we relate our definition of the fundamental price process to the classical definition.

Asset price bubbles in financial networks

2018

Asset price bubbles are commonly defined as the difference between the market value of an asset and its fundamental value. In this work, we study the two main stages characterizing the evolution of an asset price bubble: a first phase when the bubble takes place and blows up, and a second period when the burst of the bubble can affect financial institutions, leading to a crisis. Networks play an essential role in our analysis: we study how an investors network influences the evolution of the bubble through trading effects via contagion between investors, and in which way a bubble alters the structure of a banking network due to preferential attachment mechanisms in investments between financial institutions. The first part of the thesis is devoted to the analysis of the first phase of the evolution of a bubble. In our approach, we follow the so called martingale theory of bubbles, recently developed starting from the assumption of absence of arbitrage. However, we slightly move the ...

Erratum to: Heterogeneous expectations leading to bubbles and crashes in asset markets: Tipping point, herding behavior and group effect in an agent-based model

2015

The traditional economic models are increasingly perceived as weak in explaining the bubbles and crashes in financial markets and the associated crisis. Thus, especially after the global financial crisis in 2008, agent-based model (ABM) is getting an attention as an alternative approach for a better understanding of complex dynamics of financial market. This paper develops an ABM to replicate financial instability, such as bubbles and crashes in asset markets, by introducing a simple idea of ‘heterogeneous expectation’ and ‘herding behavior’ by which agents in different groups have different expectations about a ‘tipping point’ where they expect the price to stop rising anymore but to begins to fall. It is shown that, when the agents have different expectations on the tipping point, the collapse of the price does not emerge automatically, and price fluctuations are often small and even some (seemingly) flat intervals appear. We also verify the impact of the herding behavior by dividing agents into several groups of varying sizes but with the same expectations. By changing the size of groups, we establish that the more agents share the same expectations about the tipping point, the higher volatility of the asset price emerges. We confirm that bubble and burst of prices are more like to emerge when heterogeneous expectations about prices are combined with herding behavior among agents, so that agents in the same group share the similar expectations about the price changes.

Bubble geometry and chaotic pricing behaviour

price gains can drive the market price away from long-run market-worth. Investing based on the outputs of past price-based valuation models appear to be more of a game-of-chance than a sound investment strategy.

Models for asset price bubbles

2017

Ponzi scheme draws its name from Charles Ponzi who became infamous for the technique of paying high returns to investors by funding of new customers in the 's. Tirole states that while unproductive assets may pay dividends they do not increase capital accumulation, and thus future dividends in the economy.

The Impact of Market Model on the Formation of Price Bubbles in Experimental Asset Markets

2009

For the past two decades a market model introduced by Smith, Suchanek, and Williams (1988, henceforth SSW) has dominated experimental research on financial markets. In SSW the fundamental value of the traded asset is determined by the expected value of a finite stream of dividend payments. This setup implies a deterministically falling fundamental value with a predetermined end of the life-span of the asset and extremely high dividend-payouts. We present a new market model in which we implement the fundamental value by adopting a random walk process. Compared to SSW-markets, prices in the new markets (SAVE) are more efficient and end-of-experiment imbalances common in SSW-markets are not observed. Our results demonstrate, that implicit features of the SSW market model contribute to bubble formation. JEL classification: C92, D83, D84, G12

An Economic Bubble Model and Its First Passage Time

2018

We introduce a new diffusion process Xt to describe asset prices within an economic bubble cycle. The main feature of the process, which differs from existing models, is the drift term where a mean-reversion is taken based on an exponential decay of the scaled price. Our study shows the scaling factor on Xt is crucial for modelling economic bubbles as it mitigates the dependence structure between the price and parameters in the model. We prove both the process and its first passage time are well-defined. An efficient calibration scheme, together with the probability density function for the process are given. Moreover, by employing the perturbation technique, we deduce the closed-form density for the downward first passage time, which therefore can be used in estimating the burst time of an economic bubble. The object of this study is to understand the asset price dynamics when a financial bubble is believed to form, and correspondingly provide estimates to the bubble crash time. Ca...

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