Pre-crash Period Research Articles

Complex network analysis of cryptocurrency market during crashes

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (ρD), average path length (l̄), and average clustering coefficient (cc¯) are estimated from these networks. We find that both ρD and cc¯ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although ρD and cc¯ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get l̄ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of ρD, cc¯, and l̄ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

Effects of an integrated safety system for swivel seat arrangements in frontal crash.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle's occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations. Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from -45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20mph was applied to the sled. The occupant's kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20mph. Results: In a lateral movement, the excursions of the dummy head were 100mm and 70mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150mm and 180mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant's potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions. Conclusion: The pre-pretension seatbelt could reduce the occupant's forward movement in the rotating seat positions in a pre-crash period. The occupant's pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

Overconfidence bias in the Indian stock market in diverse market situations: an empirical study

Market situations affect investors’ sentiments. Sudden and sharp changes in the market may make investors adjust their beliefs and change their viewpoints. To quote, such sharp changes include the stock market crash of 2008 and the COVID-19 pandemic, which significantly impacted investors' emotions. In this direction, this study attempts to find investors’ overconfidence bias in different market situations, i.e., precrash period (2006–2008), crash period (2008–2010), and post-crash period (2010–2015, 2015–2020, 2020–2021). We have obtained secondary data for this study from the BSE 100 index (as a proxy of the Indian stock market). Econometric techniques used for data analysis include Vector Auto Regression (VAR), Granger causality VAR/Block Exogeneity Wald Test, and Impulse Response Function (IRF). The study finds that the investors were overconfident during pre-crash periods, i.e., before the global market crash of 2008 and the duration between 2015 and March 2020. In postcrash periods, i.e., during 2008–2010, 2010–2015, and 2020–2021, investors were not overconfident.

Identifying Pedal Misapplication Behavior Using Event Data Recorders

<div class="section abstract"><div class="htmlview paragraph">Pedal misapplication (PM) crashes, i.e., crashes caused by a driver pressing one pedal while intending to press another pedal, have historically been identified by searching unstructured crash narratives for keywords and verified via labor-intensive manual inspection. This study proposes an alternative method to identify PM crashes using event data recorders (EDRs). Since drivers in emergency braking situations are motivated to hit the brake hard, it follows that drivers in emergency braking situations that commit a PM would likewise hit the accelerator hard, likely harder than accelerator pedal application during normal driving. Thus, the time-series accelerator pedal position and the derived accelerator pedal application rate were used to isolate accelerator misapplications. Additional strategic filters were applied based on characteristics observed from previous PM analyses to reduce false positive PM identifications. These include a crash type filter, since PM crashes have been shown to manifest as majority road departure, end departure, rear-end, and forward impact crash types. After analyzing pre-crash EDR data from the National Automotive Sampling System Crashworthiness Data System (NASS/CDS) case years 1997 to 2015, evidence of PM was observed in 4.3% of weighted events. This result was substantially higher than the previously estimated 0.2% PM frequency [<span class="xref">1</span>,<span class="xref">2</span>]. The time-to-collision (TTC) at the point of PM was calculated for each case, and over 50% of cases had a TTC of less than 2.0 seconds. Over one-third of these drivers engaged the accelerator to 99% of pedal stroke or above and over one-eighth of drivers engaged both the brake and the accelerator pedals simultaneously during the recorded pre-crash period.</div></div>

Using transfer entropy to measure information flows between cryptocurrencies

In this paper, we use the transfer entropy to quantify information flows between three cryptocurrencies, namely Bitcoin, Ethereum and Ripple. We also employ the concept of Rényi transfer entropy that allows for capturing rare and frequent events separately as well as non-linear market dependencies, focusing on extreme (tail) observations of the return distributions. We find that Bitcoin and Ripple share a bidirectional information transmission, while there is only one directional information transmission from Ripple to Ethereum. There is no nonlinear information transmission according to the Rényi’s measure, which implies the linear dependency among the three cryptocurrencies. This information transmission between cryptocurrencies occurs mostly in the pre-crash period while they become independent after the 2017 cryptocurrency crash. We finally use the concept of volatility surprise to examine linkages among the volatility of our series, and find a highly significant information transmission flow in one direction from Bitcoin to Ripple. Our results should be useful to investors in helping them in developing investment strategies by considering these three cryptocurrencies.

How do investors behave in the context of a market crash? Evidence from India

PurposeThe main aim of this paper is to empirically test at market level, the investors' differential reaction to information, contribution of their confidence level and adaptive behaviour to excessive market volatility in Indian stock market.Design/methodology/approachThe Bivariate Vector Autoregression and Impulse Response Analysis are used to study whether investors over/under-react to private and public information. EGARCH models are used to study the contribution of investors' over/under-confidence and adaptive behaviour to excessive market volatility.FindingsThe investors over-react to private information and under-react to public information during pre-crash period, become overconfident and contribute to excessive volatility. They under-react to both private and public information during after-crash period, become under-confident and also conform to adaptive market hypothesis (AMH).Research limitations/implicationsThe empirical results of the study can help investors to minimize the negative impact of over/under-confidence on their expected utility.Practical implicationsThe investors shall perform a post-analysis of investment, become aware of their past behavioural mistakes and start adapting to changing market conditions. This shall move the markets towards a new equilibrium in long run thus conforming AMH. However, the investors sometimes display an apparently irrational behaviour during this process.Originality/valueTo the best of the author's knowledge, this is the first study at market level data examining investors' over/under-reaction, over/under-confidence and adaptive behaviour in the context of stock market crash.

Risk, uncertainty and stock returns predictability – a case of emerging equity markets

PurposeThis paper aims to investigate the predictability of stock returns under risk and uncertainty of a set of 11 emerging equity markets (EEMs) during the pre- and post-crash periods.Design/methodology/approachListed indices are considered to serve the proxy of stock markets with a structural break in data for the period: 2000-2014. As preliminary results highlight the significant autocorrelations in stock returns, Threshold-GARCH (1,1) model is used to estimate the conditional volatility, which is further decomposed into expected and unexpected volatility.FindingsResults highlight that the volatility has symmetric impact on stock returns during the pre-crash period and asymmetric impact during the post-crash period. While testing the relationship of stock returns, a significant positive (negative) relationship is found with expected volatility during the pre-crash (post-crash) periods. The stock returns are found positively related to unexpected volatility.Research limitations/implicationsBusiness, political and other market conditions of sample stock markets are fundamentally different. These economies were liberalized in different years, which may affect the degree of integration with international equity markets.Practical implicationsThe findings highlight that investors consider the impact of expected volatility in forecasting of stock returns during the growth period. They realize returns in commensurate to risk of their portfolios. However, they significantly reduce their investments in response to expected volatility during the recession period. The positive relationship between stock returns and unexpected volatility highlights the fact that investors realize extra returns for exposing their portfolios to unexpected volatility.Originality/valuePioneer efforts are made by using T-GARCH (1,1) procedure to analyse the problem. Given the emergence of emerging equity markets, new insight in dynamics of stock returns provide interesting findings for portfolio diversification under risk and uncertainty.

Investor Overconfidence, Margin Trade and Market Efficiency: Evidence Based on a Recent Financial Market Crash

We investigate the effect of investor overconfidence and margin trades on market efficiency around a market crash. We find that the price delay in a pre-crash period is about twice the price delay in a post-crash period. After a market crash, investors become more sensitive to market movements, resulting in small price delay and high price synchronicity. Margin traders not only trade on market trends but also put additional force on it, escalating the pyramiding and de-pyramiding effects caused by the shift in market sentiment. Finally, our results show that negative information travels slowly only when investors are overconfident.

Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?

ABSTRACTWe study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements.

On the return-volatility relationship in the Bitcoin market around the price crash of 2013

Abstract The authors examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in US dollar. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. The authors test if there is a difference in the return-volatility relation before and after the price crash of 2013 and show a significant inverse relation between past shocks and volatility before the crash and no significant relation after. This finding shows that, prior to the price crash of December 2013, positive shocks increased the conditional volatility more than negative shocks. This inverted asymmetric reaction of Bitcoin to positive and negative shocks is contrary to what one observes in equities. As leverage effect and volatility feedback do not adequately explain this reaction, the authors propose the safe-haven effect (Baur, Asymmetric volatility in the gold market, 2012). They highlight the benefits of adding Bitcoin to a US equity portfolio, especially in the pre-crash period. Robustness analyses show, among others, a negative relation between the US implied volatility index (VIX) and Bitcoin volatility. Those additional analyses further support the findings and provide useful information for economic actors who are interested in adding Bitcoin to their equity portfolios or are curious about the capabilities of Bitcoin as a financial asset.

On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013

We examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in various currencies. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. We test if there is a difference in the return-volatility relation before and after the price crash of 2013 and show a significant inverse relation between past shocks and volatility before the crash and no significant relation after. This finding shows that, prior to the price crash of December 2013, positive shocks increased the conditional volatility more than negative shocks. This inverted asymmetric reaction of Bitcoin to positive and negative shocks is contrary to what we observe in equities. As leverage effect and volatility feedback don’t adequately explain this reaction, we propose the safe-haven effect (Baur, 2012). We highlight the benefits of adding Bitcoin to a US equity portfolio, especially in the pre-crash period. Robustness analyses show, among others, a negative relation between the US implied volatility index (VIX) and Bitcoin volatility. Those additional analyses further support our findings and provide useful information for economic actors who are interested in adding Bitcoin to their equity portfolios or are curious about the capabilities of Bitcoin as a financial asset.

The contagion channels of July–August-2011 stock market crash: A DAG-copula based approach

The objective of this paper is to empirically investigate whether there is a contagion phenomenon between the stock markets during the July–August-2011 stock market crash. When there is a market contagion, we will identify the propagation channel through which the crash is transmitted. Hence, after checking if there is financial contagion between the stock markets, we will see if the transmission mechanism “constraints of wealth” outweighs that of the “portfolio rebalancing”. An additional test covering the interdependence between the stock and bond markets during the crash helps us verify whether the transmission is due either to the “cross-market rebalancing” channel or to the “flight to quality” phenomenon.On the basis of the combination of the copula theory and the directed acyclic graph to study the structure of causal dependence between the stock market during the period that lies between 01/02/2010 and 28/11/2012, we show that the links between countries are different between the pre-crash period and that of the crash. More specifically, the links that do not exist during normal times seem to have a major role during the crash period. We interpret this result as an evidence of the existence of pure contagion. On the one hand, the tests show that the channel of the “portfolio rebalancing” was the major mechanism for the spread of the crash. On the other hand, the phenomenon of the cross-market rebalancing existed only in Germany; whereas, that of the flight to quality was in all the other studied stock markets.

The evolution of financial analysts’ forecasts on Asian emerging markets

This study investigates the financial analysts’ performance on eight Pacific-Basin markets from 1990 to 2000. We focus on the analysis of the crisis in 1997 on the quality of earnings forecasts. The Asian crisis should stand as a serious but positive breakdown in the performance of analysts. We show that before as after the crash, analysts issued forecasts systematically upward biased. The magnitude of errors, in 2000, was still superior to that observed in the pre-crash period. We conclude that analysts have failed to anticipate the latent financial problems before the crisis and to draw the lesson from it.

Cross‐market correlations and transmission of information

AbstractWe investigate characteristics of cross‐market correlations using daily data from U.S. stock, bond, money, and currency futures markets using a new multivariate GARCH model that permits direct hypothesis testing on conditional correlations. We find evidence that arrival of information in a market affects subsequent cross‐market conditional correlations in the sample period following the stock market crash of 1987, but there is little evidence of such a relationship in the precrash period. In the postcrash period, we also find evidence that the prime rate of interest affects daily correlations between futures returns. Furthermore, we find that conditional correlations between currency futures and other markets decline steeply a few months before the crash and revert to normal dynamics after the crash. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:1059–1082, 2002

The behavior of financial analysts during the Asian financial crisis in Indonesia, Korea, Malaysia, and Thailand

This study investigates the behavior of a potentially influential class of market participants, the stock analysts, around the period of market crashes in four Asian countries. We find that analysts not only failed to anticipate the weaknesses in the firms they covered before the crash, they also failed to sufficiently adjust their forecasts after these markets crashed. Throughout the entire period of the study, the magnitudes of forecast errors were several times to that observed in the pre-crash period. However, we also do not find evidence of panic or herding on a large scale. Other issues investigated in this paper include the changes in coverage, changes in forecasts accuracy, the extent analysts agree and the patterns of their convergence for the sample. We also compare analyst forecasts for large versus small firms, and for high- versus low-growth/quality firms. A contribution to the literature is the cataloguing of models that may provide explanations for investors' expectations during a market crash.

Pre- and post-1987 crash frequency domain analysis among Pacific Rim equity markets

This paper uses the method of cross spectral analysis to examine pre- and post-1987 crash comovements among several Pacific Rim markets. The results support increased post-crash comovements among the markets. Each of the pairwise median coherences was greater in the post-crash period. The same is true for the post-crash mean coherences, with one exception. The nonparametric Wilcoxon Z statistics indicate that the pre- and post-crash coherences were drawn from different populations in seven of the eight pairwise market comparisons. Phase lead results indicate that the post-crash phase leads in four of the eight pairwise comparisons were smaller than in the pre-crash period, while post-crash phase leads did not fall in any of the market comparisons.

A Cointegration Analysis of Latin American Stock Markets and the U.S.

This paper investigates the long run relationship between four major Latin American stock markets (Argentina, Brazil, Chile and Mexico) and the United States for the period 1976-1998. Using Johansen's multivariate cointegration analysis, we find a stationary long run relationship between the stock indices during the 1976-1998 period, and also during the post-October 1987 stock market crash period (1987-1998). No stationary relationship was found during the pre-crash period (1976-1987). Finally, an important role is played by the markets' degree of development and cooperation among themselves.

The causal relationship between stock index futures and cash index prices in Hong Kong

The interrelationship between the Hang Sang index futures contracts traded in Hong Kong and the underlying Hang Sang index is investigated. Causal relationships between the variables are studied using the full-information estimation technique, Granger's definition of causality (Granger, C. W. J. (1969) Econometrica, 37, 424–38) and Hsiao's operational test (Hsiao, C. (1981) Journal of Monetary Economics, 7, 85–106). The results indicate that futures prices cause cash index prices to change in the pre-crash period but not vice versa. In the post-crash period, it is found that a bi-directional causality exists between the two variables.1

Behaviour of intra-daily stock return on an Asian emerging market - Hong Kong

This study examines the intra-daily return behaviour of one of the most open Asian emerging markets - Hong Kong. It is found that there is a general increase in the positive skewness and kurtosis of all the intra-daily returns after the 1987 October crash and the distributions of all the returns have become non-normal after the crash. There seems to be more day-of-the-week and time-of-the-day variations in the post-crash period than in the pre-crash period. There also exists some day-end effect in both of the periods and such a day-end effect seems to be related to the day of the week.

Australian All Ordinaries Share Price Index Futures and Random Walks

The study covers the period 16/3/83 to 31/12/87, using daily futures prices to provide descriptive statistics and statistical tests for a random walk. The study period falls naturally into two periods, the pre-crash and period including the 1987 crash. The random walk hypothesis provides a “rough” description of futures price changes in the pre-crash period but is rejected for the full period. Some indication of the effect of the 1987 crash period on share price index futures prices is demonstrated via descriptive statistics. Day in the week effects are isolated both in terms of variance and returns. The existence of these effects suggest that random walk is at best an imperfect description of price changes over time.