Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms.

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called `early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Sustainable biomass production for energy in selected Asian countries

This paper presents a synthesis of assessment of sustainable biomass production potential in six Asian countries-China, India, Malaysia, Philippines, Sri Lanka and Thailand, and is based on the detailed studies carried out in these countries under the Asian Regional Research Programme in Energy, Environment and Climate (ARRPEEC). National level studies were undertaken to estimate land availability for biomass production, identify and evaluate the biomass production options in terms of yield per hectare and financial viability, estimate sustainable biomass production for energy, and estimate the energy potential of biomass production in the six Asian countries. Sustainable biomass production from plantation is estimated to be in the range of 182.5-210.5, 62-310, 0.4-1.7, 3.7-20.4, 2.0-9.9 and 11.6-106.6 Mt yr(-1) for China, India, Malaysia, Philippines, Sri Lanka and Thailand, respectively. The maximum annual electricity generation potential, using advanced technologies, from the sustainable biomass production is estimated to be about 27, 114, 4.5, 79, 254 and 195 percentage of the total electricity generation in year 2000 in China, India, Malaysia, Philippines, Sri Lanka and Thailand, respectively. Investment cost for bioenergy production varies from US$381 to 1842 ha(-1) in the countries considered in this study; investment cost for production of biomass varies from US$5.1 to 23 t(-1). (C) 2003 Elsevier Ltd. All rights reserved.