On the performance of the minimum VaR portfolio

Alexander and Baptista [2002. Economic implications of using a mean-value-at-risk (VaR) model for portfolio selection: A comparison with mean–variance analysis. Journal of Economic Dynamics and Control 26: 1159–93] develop the concept of mean-VaR efficiency for portfolios and demonstrate its very close connection with mean–variance efficiency. In particular, they identify the minimum VaR portfolio as a special type of mean–variance efficient portfolio. Our empirical analysis finds that, for commonly used VaR breach probabilities, minimum VaR portfolios yield ex post returns that conform well with the specified VaR breach probabilities and with return/risk expectations. These results provide a considerable extension of evidence supporting the empirical validity and tractability of the mean-VaR efficiency concept.     

On the source of contrarian and momentum strategies in the Italian equity market

This paper investigates the source of momentum profits, while inferring the validity of the assumptions underlying rational and behavioural theories. Using a unique sample of securities listed in the Italian Stock Exchange from 1950 to 1995, we observe that buying better performing stocks in the previous 3-12 months and selling worse performing stocks over the same period yields significant profits in the short term (less than 1 year). Results also hold when conditioned upon different risk specifications. On the other hand, the continuation effect seems to significantly revert over a longer period. More importantly, in contrast with Conrad and Kaul [Rev. Financ. Stud. 11 (1998) 489], bootstrap and Monte Carlo simulations show that momentum profits are more likely to be generated by stock returns time series properties rather than by their cross-sectional differences. While the overall findings cannot reject the market efficiency hypothesis, we argue that behavioural theory may be a possible “story” to interpret the continuation effect.     

Information stages in efficient markets

Market efficiency, in its strong form, asserts that asset prices fully reflect all available information. The classical event study methodology attempts to make explicit this link by assuming rigid and universal pre-event, event, and post-event periods. As an alternative, our framework captures the progressive diffusion of information around events as well as the overlapping impacts of separate events. We also illustrate that our approach captures mean-reversion of expected returns and increased volatility around announcement dates. These features reflect latent regime switches and are associated with semi-strong market efficiency.     

When does the Japan Empowering Women Index outperform its parent and the ESG Select Leaders Indexes?

This paper examines and compares the performance of the Japan Empowering Women Index (WIN), Japan ESG Select Leaders Index (SLI), and their parent, the Japanese Investable Market Index (IMI) using data from April 2013 to October 2020. Without regime switching, our benchmark analysis suggests that none of the indexes outperforms the market on average. We also investigate the possible regime-dependent performance of each index to identify the periods when WIN outperforms the market, the IMI, and the SLI, if ever. Our results indicate regime-dependent performance of the WIN and IMI and regime-independent performance of the SLI. For example, when the market performance of the previous month is relatively poor, the WIN tends to outperform the market, while the IMI tends to underperform. Our results also show that, when the market volatility of the previous month is relatively small, the WIN outperforms the market. However, the WIN and IMI tend to underperform the market under the high market volatility regime.     

Simulation-based Value-at-Risk for nonlinear portfolios

Value-at-risk (VaR) has been playing the role of a standard risk measure since its introduction. In practice, the delta-normal approach is usually adopted to approximate the VaR of portfolios with option positions. Its effectiveness, however, substantially diminishes when the portfolios concerned involve a high dimension of derivative positions with nonlinear payoffs; lack of closed form pricing solution for these potentially highly correlated, American-style derivatives further complicate the problem. This paper proposes a generic simulation-based algorithm for VaR estimation that can be easily applied to any existing procedures. Our proposal leverages cross-sectional information and applies variable selection techniques to simplify the existing simulation framework. Asymptotic properties of the new approach demonstrate faster convergence due to the additional model selection component introduced. We have also performed sets of numerical results that verify the effectiveness of our approach in comparison with some existing strategies.     

Detecting ARCH Effects in Non-Gaussian Time Series

Engles ARCH test has become the standard test for ARCH effectsin applied work. Under non-normality the true rejection probabilityof this test can differ substantially from the nominal level,however. Bootstrap and Monte Carlo versions of the test maythen be used instead. This paper proposes an alternative testprocedure. The new test exploits the empirical distributionof the data and an extended probability integral transformation.The test is compared with the former tests in Monte Carlo experiments.Under normality, the new test works as well as the conventionalMonte Carlo test and the bootstrap. Under non-normality, thetest tends to be more accurate and more powerful than the bootstrappedARCH test. The procedure is then used to test for ARCH effectsin S&P 500 returns sampled at different frequencies. Incontrast to the standard and the bootstrapped ARCH tests, thenew test detects ARCH effects in the transformed low-frequencyreturns.     

The long-horizon returns behaviour of the Portuguese stock market1

In the last few years several research studies have challenged the traditional weak-form efficiency tests of the stock market. These studies suggested an alternative to the random walk model, containing temporary and permanent components. If stocks follow such a model then the traditional tests, using returns computed for short intervals would be unable to detect them. To investigate the evidence for such models in the Portuguese stock market ten stock indexes were created. This is a pioneer study of the Portuguese stock market, and uses nominal, real and excess returns, computed for longer horizons. Three methodologies were used: variance ratios, ordinary least squares regressions and weighted least squares regressions. The statistical significance of the results was studied using traditional parametric tests as well as non-parametric tests. The evidence is mixed, as the presence of tendencies towards mean aversion and mean reversion were detected. Results also show that the evidence is very sensitive to the methodology used and the signifcance tests performed. These results, however, do not necessarily reject the weak-form market efficiency hypothesis.     

The augmented Black–Litterman model: a ranking-free approach to factor-based portfolio construction and beyond

The Fama and French factor-ranking approach (1992, 1993, etc.) has been extensively applied in quantitative fund management. However, this approach suffers from hidden factor view, information inefficiency, etc. issues. Based on the Black–Litterman model (1992; as explained in Cheung 2010b Cheung, W. 2010b. The Black–Litterman model explained. J. Asset Mgmt., 11: 229–243. [Crossref] , [Google Scholar]), we develop a technique that endogenizes the ranking process and elegantly resolves these issues. This model explicitly seeks forward-looking factor views and smoothly blends them to deliver robust allocation to securities. Our numerical experiments show this is an intuitive and practical framework for factor-based portfolio construction, and beyond. This article features: (1) a new and unified framework for strategy combination, factor mimicking and security-specific bets; (2) an elegant and ranking-free approach to factor style construction; (3) worked examples based on the FTSE EUROTOP 100 universe; (4) insight into the classic issue of confidence parameter setting; and (5) implementation guidance in an appendix.     

Constructing Fama–French Factors from Style Indices: Evidence from the Islamic Equity Market

This study has contributed to the analysis of the Fama–French three-factor model by proving the validity of model using the newly constructed Fama–French factors from Malaysian Islamic stock market. With generalized method of moments and robustness tests, our results compliment earlier studies by comparing the results over two sub-periods, before and after the financial crises and the fall of Lehman Bros. The results of the analysis suggest that the reversal of size effects exists after periods of financial crisis. This is the first attempt to create FF factors and test the model from Islamic equity style indices.     

Automatic variance ratio test under conditional heteroskedasticity

An extensive Monte Carlo experiment is conducted to evaluate small sample properties of the automatic variance ratio test under conditional heteroskedasticity. It is found that the test shows serious size distortion in small samples. For improved small sample performance, this paper proposes the use of wild bootstrap. When wild bootstrapped, the automatic variance ratio test shows no size distortion, and it has power substantially higher than its competitors such as the Chen–Deo test and wild bootstrap Chow–Denning test.     

外资入股对商业银行效率的影响：基于面板数据的分析

本文运用基于面板数据的回归模型,对引入外资两年以上的中资商业银行进行了实证分析,结果表明外资战略投资者的入股对中资商业银行效率具有明显的改善作用。     

Implications of Stratified Sampling for Fair Lending Binary Logit Models

We examine the effect of sample design on estimation and inference for disparate treatment in binary logistic models used to assess for fair lending. Our Monte Carlo experiments provide information on how sample design affects efficiency (in terms of mean squared error) of estimation of the disparate treatment parameter and power of a test for statistical insignificance of this parameter. The sample design requires two decision levels: first, the degree of stratification of the loan applicants (Level I Decision) and secondly, given a Level I Decision, how to allocate the sample across strata (Level II Decision). We examine four Level I stratification strategies: no stratification (simple random sampling), exogenously stratifying loan cases by race, endogenously stratifying cases by loan outcome (denied or approved), and stratifying exogenously by race and endogenously by outcome. Then, we consider five Level II methods: proportional, balanced, and three designs based on applied studies. Our results strongly support the use of stratifying by both race and loan outcome coupled with a balanced sample design when interest is in estimation of, or testing for statistical significance of, the disparate treatment parameter.     

Statistical arbitrage in the Black–Scholes framework

In this study, we prove the existence of statistical arbitrage opportunities in the Black–Scholes framework by considering trading strategies that consist of borrowing at the risk-free rate and taking a long position in the stock until it hits a deterministic barrier level. We derive analytical formulas for the expected value, variance and probability of loss for the discounted cumulative trading profits. The statistical arbitrage condition is derived in the Black–Scholes framework, which imposes a constraint on the Sharpe ratio of the stock. Furthermore, we verify our theoretical results via extensive Monte Carlo simulations.