| Title: | Monitoring Systemic Risk |
|---|---|
| Description: | The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>. |
| Authors: | Jean-Baptiste Hasse [aut, cre] |
| Maintainer: | Jean-Baptiste Hasse <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2026-05-14 06:14:46 UTC |
| Source: | https://github.com/cran/SystemicR |
This dataset includes state variables data extracted from the FRED. Specifically, it includes data on credit spread, liquidity spread, yield spread, 3M Treasury bill and VIX.
data("data_state_variables")data("data_state_variables")
A data frame with 5030 observations on the following 7 variables.
Datea date vector
CRESPRa numeric vector
LIQSPRa numeric vector
YIESPRa numeric vector
TBR3Ma numeric vector
RESIa numeric vector
VIXa numeric vector
Federal Reserve Economic Data (FRED) St. Louis Fed
Hasse, Jean-Baptiste. "Systemic Risk: a Network Approach". AMSE Working Paper (2020) Hasse, Jean-Baptiste, and Quentin Lajaunie. "Does the Yield Curve Signal Recessions? New Evidence from an International Panel Data Analysis." AMSE Working Paper (2020).
data("data_state_variables") head(data_state_variables)data("data_state_variables") head(data_state_variables)
This dataset includes state variables data extracted from the FRED and Yahoo Finance. Specifically, it includes dates, MSCI STOXX Europe 600 Index returns and banks, insurers and asset managers stock returns.
data("data_stock_returns")data("data_stock_returns")
A data frame with 5030 observations on the following 74 variables.
ACKB.BB.Equitya numeric vector
AGN.NA.Equitya numeric vector
AGS.BB.Equitya numeric vector
AIBG.ID.Equitya numeric vector
ALV.GY.Equitya numeric vector
AV..LN.Equitya numeric vector
BALN.SE.Equitya numeric vector
BARC.LN.Equitya numeric vector
BBVA.SQ.Equitya numeric vector
BIRG.ID.Equitya numeric vector
BKT.SQ.Equitya numeric vector
BNP.FP.Equitya numeric vector
BPE.IM.Equitya numeric vector
CBG.LN.Equitya numeric vector
CBK.GY.Equitya numeric vector
CNP.FP.Equitya numeric vector
CS.FP.Equitya numeric vector
CSGN.SE.Equitya numeric vector
DANSKE.DC.Equitya numeric vector
DBK.GY.Equitya numeric vector
DNB.NO.Equitya numeric vector
Datea date vector
EBS.AV.Equitya numeric vector
EMG.LN.Equitya numeric vector
G.IM.Equitya numeric vector
GBLB.BB.Equitya numeric vector
GLE.FP.Equitya numeric vector
HELN.SE.Equitya numeric vector
HNR1.GY.Equitya numeric vector
HSBA.LN.Equitya numeric vector
HSX.LN.Equitya numeric vector
ICP.LN.Equitya numeric vector
III.LN.Equitya numeric vector
INDUA.SS.Equitya numeric vector
INGA.NA.Equitya numeric vector
INVEB.SS.Equitya numeric vector
ISP.IM.Equitya numeric vector
JYSK.DC.Equitya numeric vector
KBC.BB.Equitya numeric vector
KINVB.SS.Equitya numeric vector
KN.FP.Equitya numeric vector
KOMB.CK.Equitya numeric vector
LGEN.LN.Equitya numeric vector
LLOY.LN.Equitya numeric vector
LUNDB.SS.Equitya numeric vector
MAP.SQ.Equitya numeric vector
MB.IM.Equitya numeric vector
MF.FP.Equitya numeric vector
MUV2.GY.Equitya numeric vector
NDA.SS.Equitya numeric vector
NXG.LN.Equitya numeric vector
OML.LN.Equitya numeric vector
PARG.SE.Equitya numeric vector
PRU.LN.Equitya numeric vector
RBS.LN.Equitya numeric vector
RF.FP.Equitya numeric vector
RSA.LN.Equitya numeric vector
SAMPO.FH.Equitya numeric vector
SAN.SQ.Equitya numeric vector
SCR.FP.Equitya numeric vector
SDR.LN.Equitya numeric vector
SEBA.SS.Equitya numeric vector
SHBA.SS.Equitya numeric vector
SLHN.SE.Equitya numeric vector
SREN.SE.Equitya numeric vector
STAN.LN.Equitya numeric vector
STB.NO.Equitya numeric vector
STJ.LN.Equitya numeric vector
SWEDA.SS.Equitya numeric vector
SXXP.Indexa numeric vector
SYDB.DC.Equitya numeric vector
UBSG.SE.Equitya numeric vector
UCG.IM.Equitya numeric vector
ZURN.SE.Equitya numeric vector
Federal Reserve Economic Data (FRED) St. Louis Fed and Yahoo Finance
Hasse, Jean-Baptiste. "Systemic Risk: a Network Approach". AMSE Working Paper (2020)
data("data_stock_returns") head(data_stock_returns)data("data_stock_returns") head(data_stock_returns)
This function provides methods to compute dynamic systemic risk measures from correlation-based networks.
f_correlation_network_measures(df_data_returns)f_correlation_network_measures(df_data_returns)
df_data_returns |
A dataframe including dates and stock returns |
Degree |
xts vector |
Closeness_Centrality |
xts vector |
Eigenvector_Centrality |
xts vector |
SR |
xts vector |
Volatility |
xts vector |
Jean-Baptiste Hasse
Hasse, Jean-Baptiste. "Systemic Risk: a Network Approach". AMSE Working Paper (2020)
# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Load data data("data_stock_returns") # Compute topological risk measures from correlation-based financial networks l_result <- f_correlation_network_measures(data_stock_returns) # Plot SR_t f_plot(l_result$SR) # }# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Load data data("data_stock_returns") # Compute topological risk measures from correlation-based financial networks l_result <- f_correlation_network_measures(data_stock_returns) # Plot SR_t f_plot(l_result$SR) # }
This function computes the CoVaR and the Delta CoVaR of a given financial institution i for a given quantile q.
f_CoVaR_Delta_CoVaR_i_q(df_data_returns)f_CoVaR_Delta_CoVaR_i_q(df_data_returns)
df_data_returns |
A dataframe including data: dates and stock returns |
CoVaR_i_q |
A numeric matrix |
Delta_CoVaR_i_q |
A numeric vector |
Jean-Baptiste Hasse
Adrian, Tobias, and Markus K. Brunnermeier. "CoVaR". American Economic Review 106.7 (2016): , 106, 7, 1705-1741.
# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Load data data("data_stock_returns") # Compute CoVaR_i_q and Delta_CoVaR_i_q f_CoVaR_Delta_CoVaR_i_q(data_stock_returns) # }# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Load data data("data_stock_returns") # Compute CoVaR_i_q and Delta_CoVaR_i_q f_CoVaR_Delta_CoVaR_i_q(data_stock_returns) # }
This function computes the dynamic CoVaR and the Delta CoVaR of a given financial institution i for a given quantile q at time t. The dynamic and aggregate Delta CoVaR is also computed.
f_CoVaR_Delta_CoVaR_i_q_t(df_data_returns, df_data_state_variables)f_CoVaR_Delta_CoVaR_i_q_t(df_data_returns, df_data_state_variables)
df_data_returns |
A dataframe including data: dates and stock returns |
df_data_state_variables |
A dataframe including data: dates and macroeconomic variables |
CoVaR_i_q_t |
A xts matrix |
Delta_CoVaR_i_q_t |
A xts matrix |
Delta_CoVaR_t |
A xts vector |
Jean-Baptiste Hasse
Adrian, Tobias, and Markus K. Brunnermeier. "CoVaR". American Economic Review 106.7 (2016): , 106, 7, 1705-1741.
# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Load data data("data_stock_returns") data("data_state_variables") # Compute CoVaR_i_q_t , Delta_CoVaR_i_q_t and Delta_CoVaR_t l_result <- f_CoVaR_Delta_CoVaR_i_q_t(data_stock_returns, data_state_variables) # Plot Delta_CoVaR_t f_plot(l_result$Delta_CoVaR_t) # }# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Load data data("data_stock_returns") data("data_state_variables") # Compute CoVaR_i_q_t , Delta_CoVaR_i_q_t and Delta_CoVaR_t l_result <- f_CoVaR_Delta_CoVaR_i_q_t(data_stock_returns, data_state_variables) # Plot Delta_CoVaR_t f_plot(l_result$Delta_CoVaR_t) # }
This function provides a framework to plot xts time series.
f_plot(xts_index_returns)f_plot(xts_index_returns)
xts_index_returns |
A xts vector |
No return value, called for side effects
Jean-Baptiste Hasse
# Plot a xts vector # NOT RUN { # Generate data returns v_returns <- numeric(10) v_returns <- rnorm(10, 0, 0.01) v_date <- seq(from = as.Date("2019-01-01"), to = as.Date("2019-10-01"), by = "month") xts_returns <- xts(v_returns, order.by = v_date) # Plot the xts vector of simulated returns f_plot(xts_returns) # }# Plot a xts vector # NOT RUN { # Generate data returns v_returns <- numeric(10) v_returns <- rnorm(10, 0, 0.01) v_date <- seq(from = as.Date("2019-01-01"), to = as.Date("2019-10-01"), by = "month") xts_returns <- xts(v_returns, order.by = v_date) # Plot the xts vector of simulated returns f_plot(xts_returns) # }
This function normalizes data to 0-1 range. Specifically, this function computes linearly rescaled values from a vector of numeric values.
f_scale(v_time_series)f_scale(v_time_series)
v_time_series |
Vector of numeric values |
A vector of numeric normalized values
Jean-Baptiste Hasse
Hasse, Jean-Baptiste. "Systemic Risk: a Network Approach". AMSE Working Paper (2020)
# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Generate data v_data <- numeric(10) v_data <- c(1, 5, 3, 2, 15, 12, 9, 11, 7, 13) # Rescale data v_rescaled_data <- numeric(10) v_rescaled_data <- f_scale(v_data) # print rescaled data print(v_rescaled_data) # }# Scale the entries of a vector to the interval [0,1] # NOT RUN { # Generate data v_data <- numeric(10) v_data <- c(1, 5, 3, 2, 15, 12, 9, 11, 7, 13) # Rescale data v_rescaled_data <- numeric(10) v_rescaled_data <- f_scale(v_data) # print rescaled data print(v_rescaled_data) # }