How common are common return factors across the NYSE and Nasdaq?

We entertain the possibility of pervasive factors that are not common across two (or more) groups of securities. We propose and implement a general procedure to estimate the space spanned by common and group-specific pervasive factors. In our empirical analysis, we study the factor structure of excess returns on stocks traded on the NYSE and Nasdaq using our methodology. We find that there are only two common pervasive factors that govern the returns for both NYSE and Nasdaq. At the same time, the NYSE and Nasdaq each have one more group-specific factor that is not the same across the two exchanges. Our results point to the absence of complete similarity between the factors driving the returns on these exchanges.     

Assessing financial instability: The case of Brazil

In this paper we use the arbitrage pricing theory to infer the probability of financial institution failure for banks in Brazil. We build an index of financial stability for Brazilian banks. Empirical results seem to provide evidence that after the Russian crisis in 1998, systemic risk has increased in the country but this risk has decreased over time through 2002. Furthermore, for individual major banks the probability of failure has decreased monotonically after the Russian crisis with the adoption of a floating exchange rate regime, an inflation-targeting framework and the introduction of the new payment system.     

Semiparametric estimation of a characteristic-based factor model of common stock returns

We introduce an alternative version of the Fama–French three-factor model of stock returns together with a new estimation methodology. We assume that the factor betas in the model are smooth nonlinear functions of observed security characteristics. We develop an estimation procedure that combines nonparametric kernel methods for constructing mimicking portfolios with parametric nonlinear regression to estimate factor returns and factor betas simultaneously. The methodology is applied to US common stocks and the empirical findings compared to those of Fama and French.     

Leveraged Lévy processes as models for stock prices

Adopting a constant elasticity of variance formulation in the context of a general Lévy process as the driving uncertainty we show that the presence of the leverage effect? ?One explanation of the documented negative relation between market volatilities and the level of asset prices (the ‘smile’ or ‘skew’), we term the ‘leverage effect’, argues that this negative relation reflects greater risk taking by the management, induced by a fall in the asset price, with a view of maximizing the option value of equity shareholders. in this form has the implication that asset price processes satisfy a scaling hypothesis. We develop forward partial integro-differential equations under a general Markovian setup, and show in two examples (both continuous and pure-jump Lévy) how to use them for option pricing when stock prices follow our leveraged Lévy processes. Using calibrated models we then show an example of simulation-based pricing and report on the adequacy of using leveraged Lévy models to value equity structured products.     

A contingent claim analysis of closed-end fund premia

We estimate contingent claims that replicate monthly net asset value (NAV) payoffs from closed-end funds. A claim's theoretical value is obtained by martingale pricing methods. The resulting net present value (NPVS) sequence is the theoretical premia sequence that is compared to the actual market premia sequence. The theoretical premia, like actual premia, are uncorrelated with NAV returns and are positively autocorrelated due to autocorrelation in the pricing information. However, there is poor correspondence between the theoretical and actual premia that seems due to the market's systematic errors in estimating a fund's management value. Risky arbitrage may be available to insiders.     

高频数据下基于组合预测思想的统计套利策略创新设计与实证研究

采用两只股票的日数据和5种高频数据,借鉴组合预测思想,综合利用协整模型和新卡尔曼滤波模型,与统计套利策略具体目标相结合,设计出新统计套利组合策略,实证分析数据频率、策略选择对统计套利效果的影响.结果表明:运用高频数据及引入卡尔曼滤波模型均有效,但卡尔曼滤波模型与协整模型不存在明显优劣之分,选择组合策略是必要的;组合策略收益性显著优于采取单一模型的套利策略;组合策略下的套利组合随数据频率提高,收益率波动性更小、更稳定;组合策略接近市场中性,能很好地免疫市场风险.