nep-fmk New Economics Papers
on Financial Markets
Issue of 2016‒02‒04
nine papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Herding and Contrarian Behavior in Financial Markets : An Experimental Analysis By Park, Andreas; Sgroi, Daniel
  2. Market correlation structure changes around the Great Crash By Rui-Qi Han; Wen-Jie Xie; Xiong Xiong; Wei Zhang; Wei-Xing Zhou
  3. A comparison among some Hurst exponent approaches to predict nascent bubbles in $500$ company stocks By M. Fern\'andez-Mart\'inez; M. A S\'anchez-Granero; Mar\'ia Jos\'e Mu\~noz Torrecillas; Bill McKelvey
  4. Choosing Expected Shortfall over VaR in Basel III Using Stochastic Dominance By Chang, C-L.; Jiménez-Martín, J.A.; Maasoumi, E.; McAleer, M.J.
  5. Short-Selling Bans and Bank Stability By Alessandro Beber; Daniela Fabbri; Marco Pagano
  6. Long Forward Probabilities, Recovery and the Term Structure of Bond Risk Premiums By Likuan Qin; Vadim Linetsky; Yutian Nie
  7. Asia Bond Monitor - June 2015 By Asian Development Bank (ADB); Asian Development Bank (ADB); Asian Development Bank (ADB); Asian Development Bank (ADB)
  8. Financial connectedness among European volatility risk premia By Andrea Cipollini; Iolanda Lo Cascio; Silvia Muzzioli
  9. Stress events in the Hungarian stock market By Dömötör, Barbara; Váradi, Kata

  1. By: Park, Andreas (University of Toronto); Sgroi, Daniel (University of Warwick)
    Abstract: We analyze and confirm the existence and extent of rational informational herding and rational informational contrarianism in a financial market experiment, and compare and contrast these with equivalent irrational phenomena. In our study, subjects generally behave according to benchmark rationality. Traders who should herd or be contrarian in theory are the signicant sources of both within the data. Correcting for subjects who can be identified as less rational increases our ability to predict herding or contrarian behavior considerably.
    Keywords: Herding ; Contrarianism ; Informational Efficiency ; Experiments JEL classification numbers: C91 ; D82 ; G14
    Date: 2016
  2. By: Rui-Qi Han (ECUST); Wen-Jie Xie (ECUST); Xiong Xiong (TJU); Wei Zhang (TJU); Wei-Xing Zhou (ECUST)
    Abstract: We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 stocks listed on the Shanghai and Shenzhen stock exchanges. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks' capitalizations.
    Date: 2016–01
  3. By: M. Fern\'andez-Mart\'inez; M. A S\'anchez-Granero; Mar\'ia Jos\'e Mu\~noz Torrecillas; Bill McKelvey
    Abstract: In this paper, three approaches to calculate the self-similarity exponent of a time series are compared in order to determine which one performs best to identify the transition from random efficient market behavior (EM) to herding behavior (HB) and hence, to find out the beginning of a market bubble. In particular, classical Detrended Fluctuation Analysis (DFA), Generalized Hurst Exponent (GHE) and GM2 (one of Geometric Method-based algorithms) were applied for self-similarity exponent calculation purposes. Traditionally, researchers have been focused on identifying the beginning of a crash. Instead of this, we are pretty interested in identifying the beginning of the transition process from EM to a market bubble onset, what we consider could be more interesting. The relevance of self-similarity index in such a context lies on the fact that it becomes a suitable indicator which allows to identify the raising of HB in financial markets. Overall, we could state that the greater the self-similarity exponent in financial series, the more likely the transition process to HB could start. This fact is illustrated through actual S\&P500 stocks.
    Date: 2016–01
  4. By: Chang, C-L.; Jiménez-Martín, J.A.; Maasoumi, E.; McAleer, M.J.
    Abstract: Bank risk managers follow the Basel Committee on Banking Supervision (BCBS) recommendations that recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The Basel Committee on Banking Supervision (2013, p. 3) noted that: “a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk”. The proposed reform costs and impact on bank balances may be substantial, such that the size and distribution of daily capital charges under the new rules could be affected significantly. Regulators and bank risk managers agree that all else being equal, a “better” distribution of daily capital charges is to be preferred. The distribution of daily capital charges depends generally on two sets of factors: (1) the risk function that is adopted (ES versus VaR); and (2) their estimated counterparts. The latter is dependent on what models are used by bank risk managers to provide for forecasts of daily capital charges. That is to say, while ES is known to be a preferable “risk function” based on its fundamental properties and greater accounting for the tails of alternative distributions, that same sensitivity to tails can lead to greater daily capital charges, which is the relevant (that is, controlling) practical reference for risk management decisions and observations. In view of the generally agreed focus in this field on the tails of non-standard distributions and low probability outcomes, an assessment of relative merits of estimated ES and estimated VaR is ideally not limited to mean variance considerations. For this reason, robust comparisons between ES and VaR will be achieved in the paper by using a Stochastic Dominance (SD) approach to rank ES and VaR.
    Keywords: Stochastic dominance, Value-at-Risk, Expected Shortfall, Optimizing strategy, Basel III Accord
    JEL: G32 G11 G17 C53 C22
    Date: 2015–12–01
  5. By: Alessandro Beber (Cass Business School and CEPR); Daniela Fabbri (Cass Business School); Marco Pagano (University of Naples Federico II, CSEF, EIEF and CEPR)
    Abstract: In both the 2008-09 subprime crisis and the 2011-12 euro debt crisis, security regulators imposed bans on short sales, designed mainly with financial institutions in mind. The motivation was that a collapse in a bank’s stock price could lead to funding problems, triggering further price drops: the ban on shorting bank stocks was supposed to break this loop, stabilizing banks and bolstering their solvency. We test this hypothesis against evidence from both crises, estimating panel data regressions for 13,473 stocks in 2008 and 16,424 stocks in 2011 in 25 countries, taking the endogeneity of short-selling bans into account. Contrary to the regulators’ intentions, in neither crisis were the bans associated with increased bank stability. Instead, when financial institutions were subjected to a short-selling ban, they displayed larger share price drops, greater return volatility and higher probability of default. And the effects were more pronounced for the more vulnerable banks. Nor did the ban in 2011 do anything to mitigate the “diabolic loop” between bank and sovereign insolvency risk during the euro-area sovereign debt crisis.
    Date: 2016
  6. By: Likuan Qin; Vadim Linetsky; Yutian Nie
    Abstract: We show that the martingale component in the long-term factorization of the stochastic discount factor due to Alvarez and Jermann (2005) and Hansen and Scheinkman (2009) is highly volatile, produces a downward-sloping term structure of bond Sharpe ratios, and implies that the long bond is far from growth optimality. In contrast, the long forward probabilities forecast an upward sloping term structure of bond Sharpe ratios that starts from zero for short-term bonds and implies that the long bond is growth optimal. Thus, transition independence and degeneracy of the martingale component are implausible assumptions in the bond market.
    Date: 2016–01
  7. By: Asian Development Bank (ADB); Asian Development Bank (ADB) (Economic Research and Regional Cooperation Department, ADB); Asian Development Bank (ADB) (Economic Research and Regional Cooperation Department, ADB); Asian Development Bank (ADB)
    Abstract: This publication reviews recent developments in East Asian local currency bond markets along with the outlook, risks, and policy options. It covers the 10 members of the Association of Southeast Asian Nations plus the People’s Republic of China; Hong Kong, China; and the Republic of Korea.
    Keywords: bonds, local currency, foreign currency, bond yields, emerging East Asia, bonds outstanding, bond issuance, bond market, foreign investor holdings, People’s Republic of China, Hong Kong, China, Indonesia, Republic of Korea, Malaysia, Philippines, Singapore, Thailand, Viet Nam, credit spreads, government bonds, corporate bonds, bond financing, renewable energy, green bonds, policy, regulatory developments, project bonds, financing
    Date: 2015–06
  8. By: Andrea Cipollini; Iolanda Lo Cascio; Silvia Muzzioli
    Abstract: In this paper we use the Diebold Yilmaz (2009 and 2012) methodology to estimate the contribution and the vulnerability to systemic risk of volatility risk premia for five European stock markets: France, Germany, UK, Switzerland and the Netherlands. The volatility risk premium, which is a proxy of risk aversion, is measured by the difference between the implied volatility and expected realized volatility of the stock market for next month. While Diebold and Yilmaz focus is on the forecast error variance decomposition of stock returns or range based volatilities employing a stationary VAR in levels, we account for the (locally) long memory stationary properties of the levels of volatility risk premia series. Therefore, we estimate and invert a Fractionally Integrated VAR model to compute the cross forecast error variance shares necessary to obtain the index of total and directional connectedness.
    Keywords: volatility risk premium, long memory, FIVAR, financial connectedness
    JEL: C32 C38 C58 G13
    Date: 2015–12
  9. By: Dömötör, Barbara; Váradi, Kata
    Abstract: Central clearing and the role of central counterparties (CCP) has gained on importance in the financial sector, since counterparty risk of the trading is to be managed by them. The regulation has turned towards them lately, by defining several processes, how CCPs should measure and manage their risk. Stress situation is an important term of the regulation, however it is not specified clearly, how stress should be identified. This paper provides a possible definition of stress event based on the existing risk management methodology: the usage of risk measure oversteps, and investigates the potential stress periods of the last years on the Hungarian stock market. According to the results the definition needs further calibration based on the magnitude of the cross-sectional data. The paper examines furthermore whether stress is to be predicted from market liquidity. The connection of liquidity and market turmoil proved to be contrary to the expectations; liquidity shortage was rather a consequence, than a forecaster phenomenon in the tested period.
    Keywords: EMIR regulation, Value at Risk models, market liquidity measurement, stress definition
    JEL: G18 G28 G32
    Date: 2016–01–20

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