We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the... more We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the continuation value inherent in sequential decision making and we use this to analyze contestants risky choices. Given their choices and optimal strategy, we invert to find implied bounds on their levels of risk aversion. In risky decision making, previous empirical evidence has suggested that past outcomes affect future choices and that contestants have time-varying risk aversion. We demonstrate that the strategies of two players (Suzanne and Frank) from the European version of the game are consistent with constant risk aversion levels except for their last risk-seeking choice.
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may nev... more Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an investment portfolio's unobserved asset allocation weightings on a set of candidate assets when the only observed information is the time series of portfolio returns and the candidate asset returns. In this paper, we exhibit both sequential Monte Carlo numerical and conditionally Normal analytical approaches to solve for estimates of the unobserved asset weight time series. This methodology is motivated by the estimation of monthly asset class weights on the aggregate hedge fund industry from 1996 to 2012. Furthermore, we show how to implement the results as predictive investment weightings in order to construct hedge fund replicating portfolios.
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may nev... more Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an investment portfolio's unobserved asset allocation weightings on a set of candidate assets when the only observed information is the time series of portfolio returns and the candidate asset returns. In this paper, we exhibit both sequential Monte Carlo numerical and conditionally Normal analytical approaches to solve for estimates of the unobserved asset weight time series. This methodology is motivated by the estimation of monthly asset class weights on the aggregate hedge fund industry from 1996 to 2012. Furthermore, we show how to implement the results as predictive investment weightings in order to construct hedge fund replicating portfolios.
We derive optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the co... more We derive optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the continuation value inherent in sequential decision making and we use this to analyze contestants risky choices. Given their choices and optimal strategy, we invert to find implied bounds on their levels of risk aversion. In risky decision making, previous empirical evidence has suggested that past outcomes affect future choices and that contestants have time-varying risk aversion. We demonstrate that the strategies of two players (Suzanne and Frank) from the European version of the game are consistent with constant risk aversion levels except for their last risk-seeking choice.
Applied Stochastic Models in Business and Industry, 2013
We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the... more We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the continuation value inherent in sequential decision making and we use this to analyze contestants risky choices. Given their choices and optimal strategy, we invert to find implied bounds on their levels of risk aversion. In risky decision making, previous empirical evidence has suggested that past outcomes affect future choices and that contestants have time-varying risk aversion. We demonstrate that the strategies of two players (Suzanne and Frank) from the European version of the game are consistent with constant risk aversion levels except for their last risk-seeking choice.
We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the... more We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the continuation value inherent in sequential decision making and we use this to analyze contestants risky choices. Given their choices and optimal strategy, we invert to find implied bounds on their levels of risk aversion. In risky decision making, previous empirical evidence has suggested that past outcomes affect future choices and that contestants have time-varying risk aversion. We demonstrate that the strategies of two players (Suzanne and Frank) from the European version of the game are consistent with constant risk aversion levels except for their last risk-seeking choice.
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may nev... more Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an investment portfolio's unobserved asset allocation weightings on a set of candidate assets when the only observed information is the time series of portfolio returns and the candidate asset returns. In this paper, we exhibit both sequential Monte Carlo numerical and conditionally Normal analytical approaches to solve for estimates of the unobserved asset weight time series. This methodology is motivated by the estimation of monthly asset class weights on the aggregate hedge fund industry from 1996 to 2012. Furthermore, we show how to implement the results as predictive investment weightings in order to construct hedge fund replicating portfolios.
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may nev... more Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an investment portfolio's unobserved asset allocation weightings on a set of candidate assets when the only observed information is the time series of portfolio returns and the candidate asset returns. In this paper, we exhibit both sequential Monte Carlo numerical and conditionally Normal analytical approaches to solve for estimates of the unobserved asset weight time series. This methodology is motivated by the estimation of monthly asset class weights on the aggregate hedge fund industry from 1996 to 2012. Furthermore, we show how to implement the results as predictive investment weightings in order to construct hedge fund replicating portfolios.
We derive optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the co... more We derive optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the continuation value inherent in sequential decision making and we use this to analyze contestants risky choices. Given their choices and optimal strategy, we invert to find implied bounds on their levels of risk aversion. In risky decision making, previous empirical evidence has suggested that past outcomes affect future choices and that contestants have time-varying risk aversion. We demonstrate that the strategies of two players (Suzanne and Frank) from the European version of the game are consistent with constant risk aversion levels except for their last risk-seeking choice.
Applied Stochastic Models in Business and Industry, 2013
We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the... more We derive an optimal strategy in the popular Deal or No Deal game show. Q-learning quantifies the continuation value inherent in sequential decision making and we use this to analyze contestants risky choices. Given their choices and optimal strategy, we invert to find implied bounds on their levels of risk aversion. In risky decision making, previous empirical evidence has suggested that past outcomes affect future choices and that contestants have time-varying risk aversion. We demonstrate that the strategies of two players (Suzanne and Frank) from the European version of the game are consistent with constant risk aversion levels except for their last risk-seeking choice.
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Papers by Laszlo Korsos