nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒07‒30
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Factors that Fit the Time Series and Cross-Section of Stock Returns By Lettau, Martin; Pelger, Markus
  2. 'Information aggregation and learning in a dynamic asset pricing model' By Michele Berardi
  3. Emergence of frustration signals systemic risk By Chandrashekar Kuyyamudi; Anindya S. Chakrabarti; Sitabhra Sinha
  4. Should Central Banks Prick Asset Price Bubbles? An Analysis Based on a Financial Accelerator Model with an Agent-Based Financial Market By Alexey Vasilenko
  5. S&P 500 Index, an Option-Implied Risk Analysis By Giovanni Barone-Adesi; Chiara Legnazzi; Carlo Sala
  6. Analyzing credit risk transmission to the non-financial sector in Europe: a network approach By Gross, Christian; Siklos, Pierre
  7. Did China's anti-corruption campaign affect the risk premium on stocks of global luxury goods firms? By Thomas Nitschka
  8. Determinants of the Brazilian stock market development By Nyasha, Sheilla; Odhiambo, Nicholas M.

  1. By: Lettau, Martin; Pelger, Markus
    Abstract: We develop an estimator for latent asset pricing factors that fit the time-series and cross- section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly dominates PCA and finds weak factors with high Sharpe-ratios that PCA cannot detect. Studying a large number of characteristic sorted portfolios we find that five latent factors with economic meaning explain well the cross-section and time-series of returns. We show that out-of-sample the maximum Sharpe-ratio of our five factors is more than twice as large as with PCA with significantly smaller pricing errors. Our factors are based on only a subset of the stock characteristics implying that a significant amount of characteristic information is redundant.
    Keywords: Anomalies; Cross Section of Returns; expected returns; high-dimensional data; Latent Factors; PCA; Weak Factors
    JEL: C14 C52 C58 G12
    Date: 2018–07
  2. By: Michele Berardi
    Abstract: This paper analyses a dynamic framework where an unobservable fundamental can be learned over time through two signals: one exogenous and private and the other, prices, endogenous and public. As information cumulates over time through Bayesian learning, prices become fully revealing and agents disregard their private information, suggesting a possible route through which fundamental values and prices can become misaligned. The analysis is then extended to a setting where agents need to infer the statistical properties of the signals they receive, merging Bayesian with adaptive learning. By introducing uncertainty about the moments of the relevant distributions used for Bayesian learning, adaptive learning can improve the ability of prices to track changes in fundamentals and thus their efficiency.
    Date: 2018
  3. By: Chandrashekar Kuyyamudi; Anindya S. Chakrabarti; Sitabhra Sinha
    Abstract: We show that the emergence of systemic risk in complex systems can be understood from the evolution of functional networks representing interactions inferred from fluctuation correlations between macroscopic observables. Specifically, we analyze the long-term collective dynamics of the New York Stock Exchange between 1926-2016, showing that periods marked by systemic crisis, viz., around the Great Depression of 1929-33 and the Great Recession of 2007-09, are associated with emergence of frustration indicated by the loss of structural balance in the interaction networks. During these periods the dominant eigenmodes characterizing the collective behavior exhibit delocalization leading to increased coherence in the dynamics. The topological structure of the networks exhibits a slowly evolving trend marked by the emergence of a prominent core-periphery organization around both of the crisis periods.
    Date: 2018–07
  4. By: Alexey Vasilenko (Bank of Russia, Russian Federation;National Research University Higher School of Economics, Laboratory for Macroeconomic Analysis; University of Toronto, Joseph L Rotman School of Management.)
    Abstract: This paper studies whether and how the central bank should prick asset price bubbles, if the effect of interest rate policy on bubbles can significantly vary across periods. For this purpose, I first construct a financial accelerator model with an agent-based financial market that can endogenously generate bubbles and account for their impact on the real sector of the economy. Then, I calculate the effect of different nonlinear interest rate rules for pricking asset price bubbles on social welfare and financial stability. The results demonstrate that pricking asset price bubbles can enhance social welfare and reduce the volatility of output and inflation, especially if asset price bubbles are caused by credit expansion. Pricking bubbles is also desirable when the central bank can additionally implement an effective communication policy to prick bubbles, for example, effective verbal interventions aimed at the expectations of agents in the financial market.
    Keywords: monetary policy, asset price bubble, New Keynesian macroeconomics, agent-based financial market.
    JEL: E44 E52 E58 G01 G02
    Date: 2018–06
  5. By: Giovanni Barone-Adesi (Swiss Finance Institute); Chiara Legnazzi (Swiss Finance Institute); Carlo Sala (ESADE Business School and University of Lugano)
    Abstract: The forward-looking nature of option market data allows one to derive economically-based and model-free conditional risk measures. The option-implied methodology is a tool for regulators and companies to perform external or internal risk analysis without posing assumptions on the distribution of returns. The article proposes the first comprehensive and extensive analysis of the performances of these measures compared with classical risk measures for the S&P500. Delivering good results both at short and long time horizons, the option-implied estimates emerge as a convenient alternative to the existing risk measures.
    Keywords: Option Prices, VaR and CVaR, Long and Short-term Risk Measures, S&P 500 Index
    JEL: G13 G32 D81
    Date: 2018–04
  6. By: Gross, Christian; Siklos, Pierre
    Abstract: Using variance decompositions in vector autoregressions (VARs) we model a highdimensional network of European CDS spreads to assess the transmission of credit risk to the non-financial corporate sector. Our findings suggest a sectoral clustering in the CDS network, where financial institutions are located in the center and non-financial as well as sovereign CDS are grouped around the financial center. The network has a geographical component reflected in differences in the magnitude and direction of real-sector risk transmission across European countries. While risk transmission to the non-financial sector increases during crisis events, risk transmission within the nonfinancial sector remains largely unchanged. JEL Classification: C01, C32, G01, G15
    Keywords: connectedness, contagion, credit risk, financial-real linkages, networks, systemic risk
    Date: 2018–07
  7. By: Thomas Nitschka
    Abstract: Media reports suggest that the recent Chinese anti-corruption campaign adversely influenced business prospects of globally operating luxury goods firms. This paper empirically tests this hypothesis. This paper finds that risk-adjusted returns on stock portfolios consisting of luxury goods firms with high exposure to China shifted persistently downward around the launch of the anti-corruption campaign. Risk-adjusted returns tend to co-vary with the intensity of the campaign. The evidence suggests that the Chinese anti-corruption campaign constituted negative cash-flow news about the affected global luxury goods firms. These findings neither pertain to luxury goods firms with low exposure to China nor to firms from other industries.
    Keywords: Asset pricing, financial markets, political risk
    JEL: G15 G18
    Date: 2018
  8. By: Nyasha, Sheilla; Odhiambo, Nicholas M.
    Abstract: In this paper, we examine the key determinants of stock market development in Brazil during the period from 1980 to 2016. The study was motivated by the growing important role of stock market development in economic development, on the one hand, and the conflicting findings on the determinants of stock market development, on the other hand. Unlike some previous studies that used cross-sectional data, the current study has used time-series techniques that take into consideration the Brazilian country-specific issues. Furthermore, the current study has also employed the ARDL bounds testing procedure to determine the determinants of stock market development in Brazil. This procedure is well known for its superior small sample properties; hence it is considered more suitable for this study. The results of the study reveal that the stock market development in Brazil is positively determined by trade openness, banking sector development and exchange rate, irrespective of whether the analysis is done in the long run or in the short run. Contrary to the results of some previous studies, investment and stock market liquidity are found to have a negative influence on the development of stock market in Brazil ??? both in the long run and in the short run. The study, therefore, recommends that policies that favour international trade, bank-based financial sector development and exchange rate stability should be pursued in Brazil, as this would translate into further stock market development.
    Keywords: Stock Market Development; Drivers; Determinants; Brazil; ARDL Approach
    Date: 2018–06–05

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