SSRN Author: John SouthallJohn Southall SSRN Content
https://privwww.ssrn.com/author=4110938
https://privwww.ssrn.com/rss/en-usFri, 07 Aug 2020 01:04:58 GMTeditor@ssrn.com (Editor)Fri, 07 Aug 2020 01:04:58 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Rules-of-thumb for the Impact of Parameter Uncertainty on Investor OutcomesUnder a simple setup we investigate the impact of parameter uncertainty on risk-adjusted returns that correspond to expected exponential utilities. In particular, we present rules-of- thumb for the impact of parameter uncertainty on the risk-adjusted returns of non-learning and learning investment strategies. These enable investors to easily approximate how much additional wealth they would need now to compensate for parameter uncertainty
https://privwww.ssrn.com/abstract=3610358
https://privwww.ssrn.com/1929132.htmlThu, 06 Aug 2020 09:32:40 GMTREVISION: Rules-of-thumb for the Impact of Parameter Uncertainty on Investor OutcomesUnder a simple setup we investigate the impact of parameter uncertainty on risk-adjusted returns that correspond to expected exponential utilities. In particular, we present rules-of- thumb for the impact of parameter uncertainty on the risk-adjusted returns of non-learning and learning investment strategies. These enable investors to easily approximate how much additional wealth they would need now to compensate for parameter uncertainty
https://privwww.ssrn.com/abstract=3610358
https://privwww.ssrn.com/1928153.htmlTue, 04 Aug 2020 09:25:16 GMTREVISION: Rules-of-thumb for the Impact of Parameter Uncertainty on Investor OutcomesUnder a simple setup we investigate the impact of parameter uncertainty on risk-adjusted returns that correspond to expected exponential utilities. In particular, we present rules-of- thumb for the impact of parameter uncertainty on the risk-adjusted returns of non-learning and learning investment strategies. These enable investors to easily approximate how much additional wealth they would need now to compensate for parameter uncertainty
https://privwww.ssrn.com/abstract=3610358
https://privwww.ssrn.com/1927482.htmlMon, 03 Aug 2020 09:32:03 GMTREVISION: A real-world stochastic model for projecting nominal interest rates in a Monte Carlo settingWe present a real-world stochastic projection of nominal interest rate curves intended to integrate with Monte Carlo economic scenario generators. The model projects three time-varying parameters arising from a singular value decomposition of transformed historic interest rates, rather than modelling tenor points directly. A key feature is that we devise an empirically-motivated, rather than an assumed convenient form, transform function to apply to interest rates before performing the decomposition. This choice reflects an observed relationship between the volatility and level of interest rates. The resulting time-varying parameters, which we call time components, are projected as mean-reverting arithmetic random walks with mean reversion strength calibrated to historic behaviour. The drift of the model is adjusted so that the mean average of discount factors across simulations is in line with initial market-implied expectations. This adjustment preserves the required relationship ...
https://privwww.ssrn.com/abstract=3577777
https://privwww.ssrn.com/1927429.htmlMon, 03 Aug 2020 08:27:00 GMTREVISION: Rules-of-thumb for the Impact of Parameter Uncertainty on Investor OutcomesUnder a simple setup we investigate the impact of parameter uncertainty on risk-adjusted returns that correspond to expected exponential utilities. In particular, we present rules-of- thumb for the impact of parameter uncertainty on the risk-adjusted returns of non-learning and learning investment strategies. These enable investors to easily approximate how much additional wealth they would need now to compensate for parameter uncertainty
https://privwww.ssrn.com/abstract=3610358
https://privwww.ssrn.com/1927012.htmlFri, 31 Jul 2020 09:16:05 GMTREVISION: A real-world stochastic model for projecting nominal interest rates in a Monte Carlo settingWe present a real-world stochastic projection of nominal interest rate curves intended to integrate with Monte Carlo economic scenario generators. The model projects three time-varying parameters arising from a singular value decomposition of transformed historic interest rates, rather than modelling tenor points directly. A key feature is that we devise an empirically-motivated, rather than an assumed convenient form, transform function to apply to interest rates before performing the decomposition. This choice reflects an observed relationship between the volatility and level of interest rates. The resulting time-varying parameters, which we call time components, are projected as mean-reverting arithmetic random walks with mean reversion strength calibrated to historic behaviour. The drift of the model is adjusted so that the mean average of discount factors across simulations is in line with initial market-implied expectations. This adjustment preserves the required relationship ...
https://privwww.ssrn.com/abstract=3577777
https://privwww.ssrn.com/1926984.htmlFri, 31 Jul 2020 09:03:39 GMTREVISION: Rules-of-thumb for the Impact of Parameter Uncertainty on Investor OutcomesUnder a simple setup we investigate the impact of parameter uncertainty on risk-adjusted returns that correspond to expected exponential utilities. In particular, we present rules-of- thumb for the impact of parameter uncertainty on the risk-adjusted returns of non-learning and learning investment strategies. These enable investors to easily approximate how much additional wealth they would need now to compensate for parameter uncertainty
https://privwww.ssrn.com/abstract=3610358
https://privwww.ssrn.com/1926716.htmlThu, 30 Jul 2020 08:30:27 GMTREVISION: Rules-of-thumb for the Impact of Parameter Uncertainty on Investor OutcomesUnder a simple setup we investigate the impact of parameter uncertainty on risk-adjusted returns that correspond to expected exponential utilities. In particular, we present rules-of-thumb for the impact of parameter uncertainty on the risk-adjusted returns of non-learning and learning investment strategies. These enable investors to easily approximate how much additional wealth they would need now to compensate for parameter uncertainty. Our results suggest that acting myopically has minimal adverse consequences in the presence of learning, in contrast to the situation in the absence of learning where there can be a large negative impact.
https://privwww.ssrn.com/abstract=3610358
https://privwww.ssrn.com/1914490.htmlMon, 29 Jun 2020 09:59:12 GMTREVISION: A real-world stochastic model for projecting nominal interest rates in a Monte Carlo settingWe present a real-world stochastic projection of nominal interest rate curves intended to integrate with Monte Carlo economic scenario generators. The model projects three time-varying parameters arising from a singular value decomposition of transformed historic interest rates, rather than modelling tenor points directly. A key feature is that we devise an empirically-motivated, rather than an assumed convenient form, transform function to apply to interest rates before performing the decomposition. This choice reflects an observed relationship between the volatility and level of interest rates. The resulting time-varying parameters, which we call time components, are projected as mean-reverting arithmetic random walks with mean reversion strength calibrated to historic behaviour. The drift of the model is adjusted so that the mean average of discount factors across simulations is in line with initial market-implied expectations. This adjustment preserves the required relationship ...
https://privwww.ssrn.com/abstract=3577777
https://privwww.ssrn.com/1896121.htmlTue, 12 May 2020 09:12:26 GMTREVISION: A Practical Stochastic Model for Projecting Nominal Interest Rates in a Monte Carlo SettingWe present a practical stochastic projection of nominal interest rate curves intended to integrate with Monte Carlo economic scenario generators. The model projects three time-varying parameters arising from a singular value decomposition of transformed historic interest rates, rather than modelling tenor points directly. A key feature is that we devise an empirically-motivated, rather than an assumed convenient form, transform function to apply to interest rates before performing the decomposition. This choice reflects an observed relationship between the volatility and level of interest rates. The resulting time-varying parameters, which we call time components, are projected as mean-reverting arithmetic random walks with mean reversion strength calibrated to historic behaviour. The drift of the model is adjusted so that the mean average of discount factors across simulations is in line with initial market-implied expectations. This adjustment preserves the required relationship ...
https://privwww.ssrn.com/abstract=3577777
https://privwww.ssrn.com/1892647.htmlMon, 04 May 2020 08:42:47 GMTREVISION: A Practical Stochastic Model for Projecting Nominal Interest Rates in a Monte Carlo SettingWe present a practical stochastic projection of nominal interest rate curves intended to integrate with Monte Carlo economic scenario generators. The model projects three time-varying parameters arising from a singular value decomposition of transformed historic interest rates, rather than modelling tenor points directly. A key feature is that we devise an empirically-motivated, rather than an assumed convenient form, transform function to apply to interest rates before performing the decomposition. This choice reflects an observed relationship between the volatility and level of interest rates. The resulting time-varying parameters, which we call time components, are projected as mean-reverting arithmetic random walks with mean reversion strength calibrated to historic behaviour. The drift of the model is adjusted so that the mean average of discount factors across simulations is in line with initial market-implied expectations. This adjustment preserves the required relationship ...
https://privwww.ssrn.com/abstract=3577777
https://privwww.ssrn.com/1891354.htmlWed, 29 Apr 2020 13:17:21 GMT