Efficient portfolios returns

Description

This dataset includes efficient real estate portfolios returns from 1999 to 2018 (annual frequency). Overall, country- and -sector level portfolios are computed in both Markowitz and Black-Litterman frameworks.

Usage

data("data_efficient_portfolios_returns")

Format

The format is: num [1:19, 1:6] 7.87 6.93 6.32 6.92 7.1 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:6] "v_Overall_M" "v_Countries_M" "v_Sectors_M" "v_Overall_BL" ...

Source

Author's own calculations based on MSCI dataset.

References

Candelon, Bertrand, Franz Fuerst, and Jean-Baptiste Hasse. "Diversification Potential in Real Estate Portfolios." (2020) Cambridge Working Paper.

Examples

data(data_efficient_portfolios_returns)
head(data_efficient_portfolios_returns)

Function computing the RSRL or the RSRL

Description

This function computes the relative Sharpe ratio loss (RSRL) or its modified version (mRSRL) from two vectors of financial returns (a given portfolio and its benchmark). RSRL and mRSRL are both (under)diversification measures. Compared to RSRL, mRSRL is robust to the non-normality of returns.

Usage

f_RSRL(v_input_data_portfolio, v_input_data_benchmark, b_input_RSRL_modified, input_prob)

Arguments

Value

Author(s)

Jean-Baptiste Hasse

References

Calvet, Laurent E., John Y. Campbell, and Paolo Sodini. "Down or out: Assessing the welfare costs of household investment mistakes." Journal of Political Economy 115.5 (2007): 707-747.

Candelon, Bertrand, Franz Fuerst, and Jean-Baptiste Hasse. "Diversification Potential in Real Estate Portfolios." (2020) Cambridge Working Paper.

Examples

# NOT RUN {

  # Load data
  data("data_efficient_portfolios_returns")

  # Prepare variables
  v_port <- data_efficient_portfolios_returns[,2]
  v_bench <- data_efficient_portfolios_returns[,1]

  # Compute RSRL
  f_RSRL(v_port, v_bench, FALSE, 0.95)

  # Compute mRSRL
  f_RSRL(v_port, v_bench, TRUE, 0.95)

# }

Function computing the Sharpe ratio or one of its modified version

Description

This function computes the Sharpe ratio (SR) or one of its modified version (mSR) from two vectors of financial returns (a given portfolios and its benchmark).

Usage

f_SR(v_input_data_portfolio, v_input_data_benchmark, c_input_method, input_prob)

Arguments

Value

Author(s)

Jean-Baptiste Hasse

References

Bali, Turan G., Stephen J. Brown, and K. Ozgur Demirtas. "Do hedge funds outperform stocks and bonds?." Management Science 59.8 (2013): 1887-1903.

Favre, Laurent, and José-Antonio Galeano. "Mean-modified value-at-risk optimization with hedge funds." The journal of alternative investments 5.2 (2002): 21-25.

Gregoriou, Greg N., and Jean-Pierre Gueyie. "Risk-adjusted performance of funds of hedge funds using a modified Sharpe ratio." The Journal of wealth management 6.3 (2003): 77-83.

Sharpe, William F. "The sharpe ratio." Journal of Portfolio Management 21.1 (1994): 49-58.

Sharpe, William F. "Mutual fund performance." The Journal of business 39.1 (1966): 119-138.

Examples

# NOT RUN {

  # Load data
  data("data_efficient_portfolios_returns")

  # Prepare data
  v_port <- data_efficient_portfolios_returns[,2]
  v_bench <- data_efficient_portfolios_returns[,1]
  v_rf <- v_bench


  # Compute the Reward-to-Variablity Ratio as in Sharpe (1966)
  f_SR(v_port, v_rf, "", 0.95)

  # Compute the Sharpe ratio as in Sharpe (1994)
  f_SR(v_port, v_bench, "S", 0.95)

  # Compute the modified Sharpe ratio as in Favre and Galeano (2002) and Gregoriou and Gueyie (2003)
  f_SR(v_port, v_bench, "FG-GG", 0.95)

  # Compute the modified Sharpe ratio as in Bali et al. (2013)
  f_SR(v_port, v_bench, "BBD", 0.95)

# }

Function computing Value-at-Risk and modified Value-at-Risk

Description

This function computes the Value-at-Risk (VaR) or the modified Value-at-Risk (mVaR) from a vector of financial returns. mVaR is also called the Cornish-Fisher expansion of Value-at-Risk. Compared to classic VaR, mVaR adequately accounts for the non-normality of returns.

Usage

f_VaR(v_input_data, b_input_var_modified, input_prob)

Arguments

Value

Author(s)

Jean-Baptiste Hasse

References

Cornish, Edmund A., and Ronald A. Fisher. "Moments and cumulants in the specification of distributions." Revue de l'Institut international de Statistique (1938): 307-320.

Jorion, Philippe. "Risk2: Measuring the risk in value at risk." Financial analysts journal 52.6 (1996): 47-56.

Examples

# NOT RUN {

  # Load data
  data("data_efficient_portfolios_returns")

  # Prepare variables
  v_port <- data_efficient_portfolios_returns[,1]

  # Compute VaR
  f_VaR(v_port, FALSE, 0.95)

  # Compute modified VaR
  f_VaR(v_port, TRUE, 0.95)

# }

Function computing a circular block bootstrap

Description

This function computes a circular block-resampling bootstrap of a matrix of returns.

Usage

f_circular_bloc_bootstrap(m_input_data_series, input_c, input_b, input_prob)

Arguments

Value

Author(s)

Jean-Baptiste Hasse

References

Efron, B. "Bootstrap methods: another look at the jackknife." The Annals of Statistics 7 (1979): 1-26.

Hall, Peter, Joel L. Horowitz, and Bing-Yi Jing. "On blocking rules for the bootstrap with dependent data." Biometrika 82.3 (1995): 561-574.

Politis, Dimitris N., and Joseph P. Romano. "A circular block-resampling procedure for stationary data." Exploring the limits of bootstrap 2635270 (1992).

Examples

# NOT RUN {

  # Load data
  data("data_efficient_portfolios_returns")
  m_example_returns <- data_efficient_portfolios_returns[,1:2]

  # Compute Circular bootstap
  f_circular_bloc_bootstrap(m_example_returns, 10, 2, 0.95)

# }

Function computing portfolio diversification measures

Description

This function computes several portfolio diversification measures: Portfolio Diversification Index (PDI), Diversification Ratio (DR), Diversification Delta (DD) and Diversification Delta Star (DD*).

Usage

f_diversification_measurement(v_input_weights, m_input_returns, c_input_method)

Arguments

Value

Author(s)

Jean-Baptiste Hasse

References

Rudin, Alexander M. "A portfolio diversification index." The Journal of Portfolio Management 32.2 (2006): 81-89.

Choueifaty, Yves, and Yves Coignard. "Toward maximum diversification." The Journal of Portfolio Management 35.1 (2008): 40-51.

Vermorken, Maximilian A., Francesca R. Medda, and Thomas Schroder. "The diversification delta: A higher-moment measure for portfolio diversification." The Journal of Portfolio Management 39.1 (2012): 67-74.

Flores, Yuri Salazar, et al. "The diversification delta: A different perspective." The Journal of Portfolio Management 43.4 (2017): 112-124.

Examples

# NOT RUN {

  # Load data
  data("data_efficient_portfolios_returns")
  m_assets_returns <- data_efficient_portfolios_returns
  number_assets <- length(m_assets_returns[1,])
  v_weights <- rep(1/number_assets, number_assets)

  # Portfolio Diversification Index (PDI)
  f_diversification_measurement(v_weights, m_assets_returns, "Portfolio_Diversification_Index")

  # Diversification Ratio (DR)
  f_diversification_measurement(v_weights, m_assets_returns, "Diversification_Ratio")

  # Diversification Delta (DD)
  f_diversification_measurement(v_weights, m_assets_returns, "Diversification_Delta")

  # Diversification Delta Star (DD*)
  f_diversification_measurement(v_weights, m_assets_returns, "Diversification_Delta_Star")

# }

Function computing coefficients and significance levels of the RSRL and mRSRL

Description

This function computes coefficients and significance levels of the RSRL and mRSRL. It performs the (under)diversification test of a given portfolio compared to its benchmark.

Usage

f_test_RSRL(v_input_p_r, v_input_b_r, input_c, input_b, input_sim, b_input_s, input_prob)

Arguments

Value

Author(s)

Jean-Baptiste Hasse

References

Candelon, Bertrand, Franz Fuerst, and Jean-Baptiste Hasse. "Diversification Potential in Real Estate Portfolios." (2020) Cambridge Working Paper.

Examples

# NOT RUN {

  # Load data
  data("data_efficient_portfolios_returns")

  # Prepare data
  v_port <- data_efficient_portfolios_returns[,2]
  v_bench <- data_efficient_portfolios_returns[,1]

  # Test RSRL and mRSRL
  f_test_RSRL(v_port, v_bench, 10, 2, 1000, TRUE, 0.95)

# }