Finite sample inference in multivariate instrumental regressions with an application to Catastrophe bonds*

References

• Adrian, T., Etula, E., Tyler, M. (2014). Financial intermediaries and the cross-section of asset returns. The Journal of Finance 69(6):2557–2596. doi:10.1111/jofi.12189
   Web of Science ®Google Scholar
• Anderson, T. W. (2006). Reduced rank regression for blocks of simultaneous equations. Journal of Econometrics 135(1–2):55–76. doi:10.1016/j.jeconom.2005.07.028
   Web of Science ®Google Scholar
• Anderson, T. W., Rubin, H. (1949). Estimation of the parameters of a single equation in a complete system of stochastic equations. The Annals of Mathematical Statistics 20(1):46–63. doi:10.1214/aoms/1177730090
   Google Scholar
• Andrews, I., Stock, J., Sun, L. (2019). Weak instruments in IV regression: theory and practice. Annual Review of Economics 11(1):727–753. doi:10.1146/annurev-economics-080218-025643
   Google Scholar
• Aon Benfield. (2017a). Insurance-linked securities. alternative capital breaks new boundaries. Report.
   Google Scholar
• Aon Benfield. (2017b). Reinsurance market outlook. Hurricane Harvey Highlights Protection Gap. Report.
   Google Scholar
• Artemis. (2017). Catastrophe bond & ILS Market Dashboard, http://www.artemis.bm/dashboard/. Last accessed 11 May 2017.
   Google Scholar
• Asgharian, H., Christiansen, C., Hou, A. J. (2018). Economic policy uncertainty and long-run stock market volatility and correlation. Center for Research in Econometric Analysis of Time Series. Research Paper 2018-12.
   Google Scholar
• Baker, S. R., Bloom, N., Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics 131(4):1593–1636. doi:10.1093/qje/qjw024
   Web of Science ®Google Scholar
• Barone-Adesi, G. (1985). Arbitrage equilibrium with skewed asset returns. The Journal of Financial and Quantitative Analysis 20(3):299–313. doi:10.2307/2331032
   Web of Science ®Google Scholar
• Barone-Adesi, G., Gagliardini, P., Urga, G. (2004). Testing asset pricing models with coskewness. Journal of Business & Economic Statistics 22(4):474–485. doi:10.1198/073500104000000244
   Web of Science ®Google Scholar
• Beaulieu, M.-C., Dufour, J.-M., Khalaf, L. (2022). Covariance nuisance parameters, invariance and HAR tests in multiple equation models. Technical report, Universite Laval, McGill University, Carleton University.
   Google Scholar
• Beaulieu, M.-C., Dufour, J.-M., Khalaf, L. (2007). Multivariate tests of mean-variance efficiency with possibly non-Gaussian errors: an exact simulation-based approach. Journal of Business & Economic Statistics 25(4):398–410. doi:10.1198/073500106000000468
   Web of Science ®Google Scholar
• Beaulieu, M.-C., Dufour, J.-M., Khalaf, L. (2013). Identification-robust estimation and testing of the Zero-Beta CAPM. The Review of Economic Studies 80(3):892–924. doi:10.1093/restud/rds044
   Web of Science ®Google Scholar
• Berndt, E. R., Savin, N. E. (1977). Conflict among criteria for testing hypotheses in the multivariate linear regression model. Econometrica 45(5):1263–1277. doi:10.2307/1914072
   Web of Science ®Google Scholar
• Bollen, N. P. B. (2013). Zero-R2 hedge funds and market neutrality. Journal of Financial and Quantitative Analysis 48(2):519–547. doi:10.1017/S0022109013000112
   Web of Science ®Google Scholar
• Braun, A. (2016). Pricing in the primary market for CAT bonds: New empirical evidence. Journal of Risk and Insurance 83(4):811–847. doi:10.1111/jori.12067
   Web of Science ®Google Scholar
• Braun, A., Ammar, S. B., Eling, M. (2019). Asset pricing and extreme event risk: Common factors in ILS fund returns. Journal of Banking & Finance 102:59–78. doi:10.1016/j.jbankfin.2019.02.012
   Web of Science ®Google Scholar
• Breeden, D. T. (1979). An intertemporal asset pricing model with stochastic consumption and investment opportunities. Journal of Financial Economics 7(3):265–296. doi:10.1016/0304-405X(79)90016-3
   Web of Science ®Google Scholar
• Breeden, D. T., Gibbons, M. R., Litzenberger, R. H. (1989). Empirical test of the consumption-oriented capm. The Journal of Finance 44(2):231–262.
   Web of Science ®Google Scholar
• Carayannopoulos, P., Perez, M. F. (2015). Diversification through catastrophe bonds: Lessons from the subprime financial crisis. The Geneva Papers on Risk and Insurance—Issues and Practice 40(1):1–28. doi:10.1057/gpp.2014.14
   Web of Science ®Google Scholar
• Cummins, J. D., Weiss, M. A. (2009). Convergence of insurance and financial markets: Hybrid and securitized risk-transfer solutions. Journal of Risk and Insurance 76(3):493–545. doi:10.1111/j.1539-6975.2009.01311.x
   Web of Science ®Google Scholar
• Doko Tchatoka, F., Dufour, J.-M. (2008). Instrument endogeneity and identification-robust tests: Some analytical results. Journal of Statistical Planning and Inference 138(9):2649– 2661. doi:10.1016/j.jspi.2008.03.006
   Web of Science ®Google Scholar
• Doko Tchatoka, F., Dufour, J.-M. (2014). Identification-robust inference for endogeneity parameters in linear structural models. The Econometrics Journal 17(1):165–187. doi:10.1111/ectj.12021
   Web of Science ®Google Scholar
• Doko Tchatoka, F., Dufour, J.-M. (2020). Exogeneity tests, incomplete models, weak identification and non-gaussian distributions: Invariance and finite-sample distributional theory. Journal of Econometrics 218(2):390–418. doi:10.1016/j.jeconom.2020.04.022
   Web of Science ®Google Scholar
• Dufour, J.-M. (1997). Some impossibility theorems in econometrics, with applications to structural and dynamic models. Econometrica 65(6):1365–1389. doi:10.2307/2171740
   Web of Science ®Google Scholar
• Dufour, J.-M. (2006). Monte carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics. Journal of Econometrics 133(2):443–477. doi:10.1016/j.jeconom.2005.06.007
   Web of Science ®Google Scholar
• Dufour, J.-M., Khalaf, L. (2002). Simulation based finite and large sample tests in multivariate regressions. Journal of Econometrics 111(2):303–322. doi:10.1016/S0304-4076(02)00108-2
   Web of Science ®Google Scholar
• Dufour, J.-M., Khalaf, L., Kichian, M. (2013). Identification-robust analysis of DSGE and structural macroeconomic models. Journal of Monetary Economics 60(3):340–350. doi:10.1016/j.jmoneco.2013.02.001
   Web of Science ®Google Scholar
• Dufour, J.-M., Taamouti, M. (2005). Projection-based statistical inference in linear structural models with possibly weak instruments. Econometrica 73(4):1351–1365. doi:10.1111/j.1468-0262.2005.00618.x
   Web of Science ®Google Scholar
• Durbin, J. (1954). Errors in variables. Revue de L’Institut International de Statistique / Review of the International Statistical Institute 22(1/3):23–32. doi:10.2307/1401917
   Google Scholar
• Fang, L., Chen, B., Yu, H., Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: a garch-midas approach. Journal of Futures Markets 38(3):413–422. doi:10.1002/fut.21897
   Web of Science ®Google Scholar
• Gomez, L., Carcamo, U. (2014). A multifactor pricing model for CAR bonds in the secondary market. Journal of Business Economics and Finance 3(2):247–258.
   Google Scholar
• Gouriéroux, C., Monfort, A., Renault, E. (1995). Inference in factor models. In Maddala, G. S., Phillips, P. C. B., and Srinivasan, T. N., eds., Advances in Econometrics and Quantitative Economics., chapter 13. Oxford, UK: Blackwell, pp. 311–353.
   Google Scholar
• Guggenberger, P. (2010a). The impact of a hausman pretest on the asymptotic size of a hypothesis test. Econometric Theory 26(2):369–382. doi:10.1017/S0266466609100026
   Web of Science ®Google Scholar
• Guggenberger, P. (2010b). The impact of a hausman pretest on the size of a hypothesis test: the panel data case. Journal of Econometrics 156(2):337–343. doi:10.1016/j.jeconom.2009.11.003
   Web of Science ®Google Scholar
• Guggenberger, P., Kleibergen, F., Mavroeidis, S. (2019). A more powerful subvector anderson rubin test in linear instrumental variables regression. Quantitative Economics 10(2):487–526. doi:10.3982/QE1116
   Web of Science ®Google Scholar
• Guggenberger, P., Kleibergen, F., Mavroeidis, S. (2021). A powerful subvector Anderson Rubin test in linear instrumental variables regression with conditional heteroscedasticity. Technical report, Pennsylvania State University, University of Amsterdam and University of Oxford.
   Google Scholar
• Guggenberger, P., Kleibergen, F., Mavroeidis, S., Chen, L. (2012). On the asymptotic sizes of subset Anderson-Rubin and lagrange multiplier tests in linear instrumental variables regression. Econometrica 80:2649–2666.
   Web of Science ®Google Scholar
• Guggenberger, P., Kumar, G. (2012). On the size distortion of tests after an overidentifying restrictions pretest. Journal of Applied Econometrics 27(7):1138–1160. doi:10.1002/jae.1251
   Web of Science ®Google Scholar
• Gürtler, M., Hibbeln, M., Winkelvos, C. (2016). The impact of the financial crisis and natural catastrophes on CAT bonds. Journal of Risk and Insurance 83(3):579–612. doi:10.1111/jori.12057
   Web of Science ®Google Scholar
• Harvey, C. R., Siddique, A. (2000). Conditional skewness in asset pricing tests. The Journal of Finance 55(3):1263–1295. doi:10.1111/0022-1082.00247
   Web of Science ®Google Scholar
• Hausman, J. A. (1978). Specification tests in econometrics. Econometrica 46(6):1251–1271. doi:10.2307/1913827
   Web of Science ®Google Scholar
• Huberman, G., Kandel, S., Stambaugh, R. F. (1987). Mimicking portfolios and exact arbitrage pricing. The Journal of Finance 42(1):1–9. doi:10.1111/j.1540-6261.1987.tb02546.x
   Web of Science ®Google Scholar
• Karolyi, G. A., Wu, Y. (2021). Is currency risk priced in global equity markets? Review of Finance 25(3):863–902. doi:10.1093/rof/rfaa026
   Web of Science ®Google Scholar
• Kim, T.-H., Muller, C. (2017). A robust test of exogeneity based on quantile regressions. Journal of Statistical Computation and Simulation 87(11):2161–2174. doi:10.1080/00949655.2017.1319947
   Web of Science ®Google Scholar
• Kiviet, J. (2013). Identification and inference in a simultaneous equation under alternative information sets and sampling schemes. The Econometrics Journal 16(1):S24–S59. doi:10.1111/j.1368-423X.2012.00386.x
   Web of Science ®Google Scholar
• Kiviet, J. (2016). When is it really justifiable to ignore explanatory variable endogeneity in a regression model. Economics Letters 145:192–195. doi:10.1016/j.econlet.2016.06.021
   Web of Science ®Google Scholar
• Kiviet, J. (2020). Testing the impossible: Identifying exclusion restrictions. Journal of Econometrics 218(2):294–316. doi:10.1016/j.jeconom.2020.04.018
   Web of Science ®Google Scholar
• Kleibergen, F. (2021). Efficient size correct subset inference in homoskedastic linear instrumental variables regression. Journal of Econometrics 221(1):78–96. doi:10.1016/j.jeconom.2019.10.013
   Web of Science ®Google Scholar
• Kleibergen, F., Lingwei, K., Zhan, Z. (2019). Identification robust testing of risk premia in finite samples. The Journal of Financial Econometrics 2022, 1–35. doi:10.1093/jjfinec/nbac010
   Web of Science ®Google Scholar
• Kleibergen, F., Zhan, Z. (2018). Identification-robust inference on risk premia of mimicking portfolios of non-traded factors. Journal of Financial Econometrics 16(2):155–190. doi:10.1093/jjfinec/nby005
   Web of Science ®Google Scholar
• Kraus, A., Litzenberger, R. H. (1976). Skewness preference and the valuation of risk assets. Journal of Finance 31(4):1085–1100.
   Web of Science ®Google Scholar
• LGT Capital Partners. (2015). Monthly report on LGT (CH) CAT Bond Fund (USD) A.
   Google Scholar
• Lintner, J. (1965a). Security prices, risk, and maximal gains from diversification. The Journal of Finance 20(4):587–615. doi:10.2307/2977249
   Web of Science ®Google Scholar
• Lintner, J. (1965b). The valuation of risk assets and the selection of risky investments in stock portfolios and Capital budgets. The Review of Economics and Statistics 47(1):13–37. doi:10.2307/1924119
   Web of Science ®Google Scholar
• Litzenberger, R. H., Beaglehole, D. R., Reynolds, C. E. (1997). Assessing catastrophe reinsurance-linked securities as a new asset class. The Journal of Portfolio Management 23(5):76–86. doi:10.3905/jpm.1996.076
   Google Scholar
• MacKinlay, A. C., Richardson, M. P. (1991). Using generalized method of moments to test mean-variance efficiency. The Journal of Finance 46(2):511–527. doi:10.1111/j.1540-6261.1991.tb02672.x
   Web of Science ®Google Scholar
• Moreira, H., Moreira, M. J. (2019). Optimal two-sided tests for instrumental variables regression with heteroskedastic and autocorrelated errors. Journal of Econometrics 213(2):398–433. doi:10.1016/j.jeconom.2019.04.038
   Web of Science ®Google Scholar
• Poskitt, D., Skeels, C. (2012). Inference in the presence of weak instruments: a selected survey. Foundations and Trends in Econometrics 6:26–44.
   Google Scholar
• Rahman, N. A. (1968). A Course in Theoretical Statistics. London, UK: Charles Griffin and Company.
   Google Scholar
• Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. The Journal of Finance 19(3):425–442.
   Web of Science ®Google Scholar
• Staiger, D., Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica 65(3):557–586. doi:10.2307/2171753
   Web of Science ®Google Scholar
• Stock, J. H., Wright, J. H. (2000). GMM with weak identification. Econometrica 68(5):1055–1096. doi:10.1111/1468-0262.00151
   Web of Science ®Google Scholar
• Stock, J. H., Wright, J. H., Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business & Economic Statistics 20(4):518–529. doi:10.1198/073500102288618658
   Web of Science ®Google Scholar
• Sum, V. (2012). Economic policy uncertainty and stock market performance: Evidence from the european union, croatia, norway, russia, switzerland, turkey and ukraine. Journal of Money, Investment and Banking 25:99–104.
   Google Scholar
• Wu, D.-M. (1973). Alternative tests of independence between stochastic regressors and disturbances. Econometrica 41(4):733–750. doi:10.2307/1914093
   Web of Science ®Google Scholar
• Young, A. (2022). Leverage, Heteroskedasticity and Instrumental Variables in Practical Application. Technical Report. London School of Economics and Political Science.
   Google Scholar