SSRN Author: Octavio Ruiz LacedelliOctavio Ruiz Lacedelli SSRN Content
https://www.ssrn.com/author=3088525
https://www.ssrn.com/rss/en-usMon, 24 Feb 2020 01:08:12 GMTeditor@ssrn.com (Editor)Mon, 24 Feb 2020 01:08:12 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Robo-advising: Learning Investors' Risk Preferences via Portfolio ChoicesWe introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the algorithm’s value function converges to the optimal value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.
https://www.ssrn.com/abstract=3228685
https://www.ssrn.com/1869117.htmlSun, 23 Feb 2020 17:35:26 GMTREVISION: Scenario Analysis for Derivatives Portfolios via Dynamic Factor ModelsA classic approach to financial risk management is the use of scenario analysis to stress test portfolios. In the case of an S&P 500 options portfolio, for example, a scenario analysis might report a P&L of −$1m in the event the S&P 500 falls 5% and its implied volatility surface increases by 3 percentage points. But how accurate is this reported value of −$1m? Such a number is typically computed under the (implicit) assumption that all other risk factors are set to zero. But this assumption is generally not justified as it ignores the often substantial statistical dependence among the risk factors. In particular, the expected values of the non-stressed factors conditional on the values of the stressed factors are generally non-zero. Moreover, even if the non-stressed factors were set to their conditional expected values rather than zero, the reported P&L might still be inaccurate due to convexity effects, particularly in the case of derivatives portfolios. A further ...
https://www.ssrn.com/abstract=3424127
https://www.ssrn.com/1844064.htmlFri, 22 Nov 2019 16:26:38 GMTREVISION: Robo-advising: Learning Investors' Risk Preferences via Portfolio ChoicesWe introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the algorithm’s value function converges to the optimal value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.
https://www.ssrn.com/abstract=3228685
https://www.ssrn.com/1842769.htmlMon, 18 Nov 2019 18:05:11 GMTREVISION: Scenario Analysis for Derivatives Portfolios via Dynamic Factor ModelsA classic approach to financial risk management is the use of scenario analysis to stress test portfolios. In the case of an S&P 500 options portfolio, for example, a scenario analysis might report a P&L of −$1m in the event the S&P 500 falls 5% and its implied volatility surface increases by 3 percentage points. But how accurate is this reported value of −$1m? Such a number is typically computed under the (implicit) assumption that all other risk factors are set to zero. But this assumption is generally not justified as it ignores the often substantial statistical dependence among the risk factors. In particular, the expected values of the non-stressed factors conditional on the values of the stressed factors are generally non-zero. Moreover, even if the non-stressed factors were set to their conditional expected values rather than zero, the reported P&L might still be inaccurate due to convexity effects, particularly in the case of derivatives portfolios. A further ...
https://www.ssrn.com/abstract=3424127
https://www.ssrn.com/1809488.htmlTue, 23 Jul 2019 14:35:16 GMT