The Barra China Equity Model (CNE5) - Empirical Notes.pdf

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The Barra China Equity Model (CNE5) - Empirical Notes.pdf
MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes Appendix A: Descriptors by Style Factor 49 Size 49 Beta. Residual volatility. Non-linear Size B0Ok-1-PnCe.…...... Lpu|y.… Eanings yield. …51 Growth ……· 51 Leverage Appendix B: Decomposing RMS Returns......53 Appendix C: Review of Bias Statistics 54 C1. Single-Window Bias Statistics 54 C2. Rolling-Window Bias Statistics REFERENCES… 58 Client Service Information is Available 24 Hours a Day....59 ∧ otice and disclaimer∴ About MSCI MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document o59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes July 2012 1 Introduction 1.1. Model Highlights This document provides empirical results and analysis for the new barra china equity model (cnes) These notes include extensive information on the structure, the performance, and the explanatory forecasting accuracy of the cnes model and the che2 Model its predecessor, i power of the factors. Furthermore these notes also include a thorough side-by-side comparison of the The Cnes Model leverages the same methodologies used for the barra US equity model (USE4). These details may be found in the companion document: USE4 Methodology notes by menchero Orr and Wang(2011) Briefly the main advances are An innovative Optimization Bias Adjustment designed to improve the factor risk forecasts of optimized portfolios by reducing the effects of sampling error on the factor covariance matrix A Volatility regime Adjustment designed to calibrate factor volatilities and specific risk forecasts to current market levels The introduction of a country factor to separate the pure industry effect from the overall market, and provide timelier correlation forecasts a new specific risk model based on daily asset-level specific returns A Bayesian adjustment technique to reduce specific risk biases due to sampling error a uniform responsiveness for factor and specific components, providing greater stability in sources of portfolio risk An independent validation of production code through a double-blind development process to assure consistency and fidelity between research code and production code A daily update for all components of the model The CNE5 Model is offered in short-term(CNESS), long-term(CNE5L)and daily (cNEsD)versions. The three versions have identical factor exposures and factor returns, but differ in their factor covariance matrices and specific risk forecasts. the cness Model is designed to be more responsive and provide more accurate forecasts at a monthly prediction horizon the CNeSl model is designed for longer-term investors willing to trade some degree of accuracy for greater stability in risk forecasts. the CNeSd model provides investors of all horizons with a tactical, one- day risk forecast i The China Equity model has been renamed in line with the new generation of Single Country Models that incorporate iso country codes. Consequently, the successor to CHE2 has been designated as Cnes to avoid a naming conflict with previous generations of the Canada equity Model (also prefixed with "CNE") MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document 4of59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes July 2012 2. Methodology Highlights 2.1. Optimization Bias adjustment One significant bias of risk models is the tendency to underpredict the risk of optimized portfolios, as demonstrated empirically by Muller (1993). More recently Shepard (2009 ) derived an analytic result for the magnitude of the bias, showing that the underforecasting becomes increasingly severe as the number of factors grows relative to the number of time periods used to estimate the factor covariance matrix. the basic source of this bias is estimation error Namely spurious correlations may cause certain stocks to appear as good hedges in-sample, while these hedges fail to perform as effectively out-of- sample An important innovation is the identification of portfolios that capture these biases and to devise a procedure for correcting these biases directly within the factor covariance matrix As shown by Menchero, Wang, and Orr (2011), the eigenfactors of the sample covariance matrix are systematically biased. More specifically the sample covariance matrix tends to tends to underpredict the risk of low volatility eigenfactors, while overpredicting the risk of high-volatility eigenfactors Furthermore reducing the biases of the eigenfactors helps improve factor risk forecasts of optimized portfolios In the context of the CNES Model, eigenfactors represent portfolios of the original pure factors the eigenfactor portfolios, however, are special in the sense that they are mutually uncorrelated. also note that the number of eigenfactors equals the number of pure factors within the model As described in the USE4 Methodology Notes, we estimate the biases of the eigenfactors by Monte Carlo simulation. We then adjust the predicted volatilities of the eigenfactors to correct for these biases. this procedure has the benefit of building the corrections directly into the factor covariance matrix, while fully preserving the meaning and intuition of the pure factors. 2. 2. Volatility Regime Adjustment Another major source of risk model bias is due to the fact that volatilities are not stable over time a characteristic known as non-stationarity. since risk models must look backward to make predictions about the future they exhibit a tendency to underpredict risk in times of rising volatility and to overpredict risk in times of falling volatility Another important innovation in the Cnes Model is the introduction of a volatility Regime Adjustment for estimating factor volatilities. as described in the Use4 Methodology notes, the volatility regime Adjustment relies on the notion of a cross-sectional bias statistic, which may be interpreted as an instantaneous measure of risk model bias for that particular day. by taking a weighted average of this quantity over a suitable interval, the non-stationarity bias can be significantly reduced Just as factor volatilities are not stable across time, the same holds for specific risk. In the cnes Model we apply the same volatility Regime Adjustment technique for specific risk. We estimate the adjustment by computing the cross-sectional bias statistic for the specific returns 2.3. Country Factor Traditionally, single country models (e. g, CHE2) have included industry and style factors, but no Country factor. an important improvement with the cnes Model is to explicitly include the country factor which MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document o59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes is analogous to the world factor in the barra global equity model (first introduced in GEM2),as described by Menchero, Morozov, and Shepard(2008, 2010) One significant benefit of the Country factor is the insight and intuition that it affords For instance, as discussed in the USE4 Methodology notes the country factor portfolio can be cleanly interpreted as the cap-weighted country portfolio. Furthermore, the Country factor disentangles the pure industry effect from the overall market effect thus providing a cleaner interpretation of the industry factors Without the Country factor industry factors represent portfolios that are 100 percent net long the particular industry, with zero net weight in every other industry With the Country factor by contrast, industry factors represent dollar-neutral portfolios that are 100 percent long the industry and 100 percent short the Country factor; that is industry performance is measured net of the market. Dollar-neutral industry factor portfolios are important from an attribution perspective. For instance suppose that a portfolio manager is overweight an industry that underperforms the market, but which nonetheless has a positive return. Clearly, overweighting an underperforming industry detracts from performance. If the industry factors are represented by net-long portfolios however, an attribution analysis would spuriously show that overweighting the underperforming industry contributed positively to performance. This non- intuitive result is resolved by introducing the country factor which makes the industry factor portfolios dollar-neutral and thereby produces the intuitive result that overweighting an underperforming industry detracts from performance Including the country factor also resolves other problematic issues in risk attribution, as described by davis and Menchero(2011) Another benefit of the country factor pertains to improvements in risk forecasting intuitively and empirically we know that industries tend to become more highly correlated in times of financial crisis As shown in the USE4 Methodology Notes, the Country factor is able to capture these changes in industry correlation in a timelier fashion the underlying mechanism for this effect is that net-long industry portfolios have common exposure to the country factor and when the volatility of the country factor rises during times of market stress, it explains the increased correlations for the industries 2. 4. Specific Risk Model with Bayesian Shrinkage The Cne5 specific risk model builds upon methodological advances introduced with the european equity Model(EUE3), as described by Briner, Smith, and Ward(2009). the EUE3 model utilizes daily observations to provide timely estimates of specific risk directly from the time series of specific returns. a significant benefit of this approach is that specific risk is estimated individually for every stock, thus reflecting the idiosyncratic nature of this risk source A potential shortcoming of the pure time-series approach is that specific volatilities may not fully persist out-of-sample. In fact as shown in the USE4 Methodology notes there is a tendency for time-series volatility forecasts to overpredict the specific risk of high-volatility stocks, and underpredict the risk of low-volatility stocks. To reduce these biases we apply a bayesian shrinkage technique. We segment stocks into deciles based on their market capitalization within each size bucket we compute the mean and standard deviation of the specific risk forecasts. We then pull or"shrink"the volatility forecast to the mean within the size decile, with the shrinkage intensity increasing with the number of standard deviations away from the mean MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document o59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes 3. Factor Structure Overview 3.1. Estimation Universe Like the legacy model, CHE2, CNES utilizes a broad all a-shares universe to form the estimation universe the set of securities used to estimate the model. The china equity market is substantially different from other more mature markets in that the largest, most liquid securities that would normally form an equity index within the country cannot adequately capture the richness of the industry structure available within the market For this reason, an expanded set of stocks is used to capture the underlying structure in the market Failing to recognize this diversity would lead to an overly-aggregated view of the industries and result in grouping stocks with disparate business risk as well as behavior. Moreover, such coarseness in the classification of securities would not adequately reflect the choices available to market participants in the making of investment decisions 3. 2. Industry Factors Industries are important variables for explaining the sources of equity return co-movement one of the strengths of the Cnes Model is that it uses the global Industry Classification Standard(glCs )for the industry factor structure. the glcs scheme is hierarchical, with 10 sectors at the top level, 24 industry groups at the next level, followed with increasing granularity at the industry and sub-industry levels GICS applies a consistent global methodology to classify stocks based on careful evaluation of the firm's business model and economic operating environment It is important that the industry factor structure for each country reflects the unique characteristics of the local market. For instance, some countries may require fine industry detail in some sectors, while a coarser structure may be appropriate for other sectors When building barra risk models, special care is taken in customizing the industry factor structure to the local market Within each sector we analyze which combinations of industries and sub-industries best reflect the market structure, while also considering the economic intuition and explanatory power of such groupings MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document 7of59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes The result of this investigative process is the set of cnes industry factors presented in Table 3.1 Industries that qualify as factors tend to exhi bit high volatility and have significant weight. also reported in Table 3. 1 are the average weights from the sample period and end-of-period industry weights Table 3.1: CNE5 Industry Factors. Weights were determined within the CNE5 estimation universe using total market capitalization. Averages were computed over the sample period Sample period 29-Jan-1999 to 30-Dec-2011 CNES Average 30-Dec-2011 Sector Code CNE5 Industry Factor Name Weight Weight Ener Energy 11.05 1538 Materials 2 Chemicals 6.13 3 Construction materials 1.17 114 4 Diversified metals 8.84 5.96 5 Materials 0.97 Industrials 6 Aerospace and Defense 0.38 7 Building Products 0.44 033 8 Construction and engineering 1.82 249 9 Electrical equipment 2.32 316 10 Industrial Conglomerates 1.33 028 11 Industrial Machinery 3.86 5.12 12 Trading Companies and Distributors 1.50 080 13 Commercial and Professional services 0.23 14 Airlines 0.96 0.73 15 Marine 0.78 0.4 16 Road Rail and Trans portation Infrastructure 4.55 232 Consumer Discretionary 17 Automobiles and Components 256 18 Household Durables(non-Homebuilding 2.16 1571 19 Leisure Products Textiles Apparel and Luxury 2.35 177 20 Hotels Restaurants and leisure 0.99 085 21 Media 0.73 080 22 Retail 2.7 1.79 Consumer Staples 23 Food Staples Retail Household Personal Prod 0.6 0.65 24 Beverages 3 3.37 25 Food Products 2.58 222 6 Health 4. Financials 27 Banks 9.45 28 Diversified Financial Services 29 Real Estate 5.95 338 Information Technology 30 Software 1.33 and Telecommunication Services 31 Hardware and semiconductors 456 Utilities 32 Utilities 5.99 308 MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document 8o59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes In Table 3.2, we report the underlying giCS codes that map to each of the cnes industry factors. In each case, the industry structure is guided by a combination of financial intuition and empirical analysis Table 3.2: Mapping of CNE5 industry factors to GICS codes. Code CNes Industry Factor Name GICS Codes Energy 10 2 Chemicals 151010 3 Construction materials 151020 4 Diversified Metals 151040 5 Materials 151030,151050 6 Aerospace and defense 201010 7 Building Products 201020 8 Construction and engineering 201030 9 Electrical equipment 201040 10 Industrial Conglomerates 201050 11 Industrial Machinery 201060 12 Trading Companies and Distributors 201070 13 Commercial and professional services 2020 14 Airlines 203010.203020 15 Marine 203030 16 Road rail and transportation Infrastructure 203040, 203050 17 Automobiles and components 2510 18 Household Durables(non-Homebuilding) 252010 19 Leisure Products Textiles apparel and Luxury 252020, 252030 20 Hotels Restaurants and leisure 2530 21 Med 2540 22 Retail 2550 23 Food Staples retail Household Personal Prod 3010, 3030 24 Beverages 302010 25 Food Products 302020 26 Health 35 27 Banks 4010 28 Diversified Financial Services 4020,4030 29 Real estate 4040 30 Software 4510 31 Hardware and semiconductors 4520.4530.50 32 utilitⅰes MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. All rights reserved Please refer to the disclaimer at the end of this document 9of59 MSCI Model Insight Barra China equity Model (CNEs)Empirical Notes In Table 3. 3 we report the largest firm within each industry as well as the total market capitalization at the end of the sample period Table 3.3: Largest stock within each industry at the end of the sample period. Market capitalizations are reported in billions of us dollars Code CNes Industry Factor Name Largest Stock(30-Dec 2011) 1 Energy PETROCHINA COMPANY LIM (250.58) 2 Chemicals QINGHAI SALT LAKE POTA-A(8.08) 3 Construction materials ANHUI CONCH CEMENT CO.-A (9.95) 4 Diversified Metals BAOSHAN IRON STEEL-A(13.49) 5 Materials SHAN DONG SUN PAPER-A(1.29) 6 Aerospace and Defense XI'AN AIRCRAFT INTL-A (2.86 7 Building Products ZHEJIANG DUN'AN ARTIF-A(1.17) 8 Construction and Engineering CHN STATE CONSTRUCTION EN(13.87) 9 Electrical Equipment SHANGHAI ELEC GRP-A(7.98 10 Industrial Conglomerates CHINA BAOAN GROUP-A(1.89) 11 Industrial Machinery SANY HEAVY INDUSTRY-A(15.13) 12 Trading Companies and Distributors SHANXI COAL INTERNATIO A(3.82) 13 Commercial and professional services BJ ORIGINWATER TECH-A(2. 13) 14 Airlines AIR CHINA LIMITED (8.59 15 Marine CHINA COSCO HOLDINGS C (5.68 16 Road Rail and Transportation Infrastructure DAQIN RAILWAY CO LTD (17.62) 17 Automobiles and Components SAIC MOTOR CORPORATION (20.76 18 Household Durables(non-Homebuilding) GREE ELECTRIC APPLIANC -A(7.74) 19 Leisure Products Textiles Apparel and Luxury SHANGHAI METERSBONWE F(4.15) 20 Hotels Restaurants and leisure SHENZHEN OVERSEAS CHIN (6.35) 21 Media JIANGSU PHOENIX PUBLISHING MEDIA (3.38) 22 Retail SUNING APPLIANCE COL-A(9.38) 23 Food Staples Retail Household Personal Prod YONGHUI SUPERSTORES ORD SHS A (3.68 24 Beverages KWEICHOW MOUTAl-A(31.88) 5 Food Products HENAN SHUANGHUI INV&-A(6.73) 26 Health YUNNAN BAIYAO-A(5.85) 27 Bank CBC-A(17665) 28 Diversified Financial Services CHINA LIFE INSURANCE-A(58.36) 29 Real Estate CHINA VANKE-A(11.49) 30 Software AEROSPACE INFORMATION-A (2.91 31 Hardware and semiconductors CHINA UNITED NETWORK-A(17.65 32 Utilities CHINA YANGTZE POWER-A(16.67) MSCI Portfolio Management analytics msci. com 2012 MSCI Inc. 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