https://www.nber.org/system/files/working_papers/w17490/w17490.pdf

On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms

Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

/pdf/estimation-of-clinical-trial-success-rates-and-related-3frvtz8qfz.pdf

Estimation of clinical trial success rates and related parameters

We introduce SRISK to measure the systemic risk contribution of a financial firm. SRISK measures the capital shortfall of a firm conditional on a severe market decline, and is a function of its size, leverage and risk. We use the measure to study top financial institutions in the recent financial crisis. SRISK delivers useful rankings of systemic institutions at various stages of the crisis and identifies Fannie Mae, Freddie Mac, Morgan Stanley, Bear Stearns, and Lehman Brothers as top contributors as early as 2005-Q1. Moreover, aggregate SRISK provides early warning signals of distress in indicators of real activity.Received June 7, 2011; accepted April 18, 2016 by Editor Geert Bekaert.

https://www.econstor.eu/bitstream/10419/193544/1/esrb-wp37.pdf

SRISK: A Conditional Capital Shortfall Measure of Systemic Risk

/pdf/srisk-a-conditional-capital-shortfall-index-for-systemic-1pl7zio822.pdf

SRISK: A Conditional Capital Shortfall Index for Systemic Risk Measurement

Recently proposed tests for unit root and other nonstationarity of Robinson (1994a) are applied to an extended version of the data set used by Nelson and Plosser (1982). Unusually, the tests are efficient (against appropriate parametric alternatives), the null can be any member of the I(d) class, and the null limit distribution is chi-squared. The conclusions vary substantially across fourteen series, and across different models of the disturbances (which, also unusually, include the Bloomfield spectral model). Overall, the consumer price index and money stock seem the most nonstationary, while industrial production and unemployment rate seem the closest to stationarity.

Testing of Unit Root and Other Nonstationary Hypotheses in Macroeconomic Time Series - (Now published in 'Journal of Econometrics', 80, 1997, pp.241-268.)

We propose the realized systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm’s Value-at-Risk (VaR) on the system’s VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential regulation.

/pdf/financial-network-systemic-risk-contributions-1ngj2ljfyq.pdf

Financial Network Systemic Risk Contributions

We propose the realized systemic risk beta as a measure of financial companies' contribution to systemic risk, given network interdependence between firms' tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm's Value-at-risk (VaR) on the system's VaR. Statistical inference reveals a multitude of relevant risk spillover channels and determines companies' systemic importance in the US financial system. Our approach can be used to monitor companies' systemic importance, enabling transparent macroprudential supervision.

/pdf/financial-network-systemic-risk-contributions-51xj41hoyy.pdf

We analyze the statistical properties of nonparametric regression estimators using covariates which are not directly observable, but have be estimated from data in a preliminary step. These so-called generated covariates appear in numerous applications, including two-stage nonparametric regression, estimation of simultaneous equation models or censored regression models. Yet so far there seems to be no general theory for their impact on the final estimator’s statistical properties. Our paper provides such results. We derive a stochastic expansion that characterizes the influence of the generation step on the final estimator, and use it to derive rates of consistency and asymptotic distributions accounting for the presence of generated covariates.

/pdf/nonparametric-regression-with-nonparametrically-generated-1gnunfmf9v.pdf

Nonparametric regression with nonparametrically generated covariates

https://statistik.econ.kit.edu/download/sovsysrisk_rev_final.pdf

Systemic risk spillovers in the European banking and sovereign network

https://www.econstor.eu/bitstream/10419/79594/1/72191148X.pdf

Melanie Schienle

Papers

Financial Network Systemic Risk Contributions

Financial Network Systemic Risk Contributions

Nonparametric regression with nonparametrically generated covariates

Systemic risk spillovers in the European banking and sovereign network

Nonparametric kernel density estimation near the boundary