Industrial Engineering and Operations Research
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html
Upcoming EventsSeminar 217, Risk Management: A Term Structure Model for Dividends and Interest Rates, Jul 31
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118257&date=2018-07-31
Over the last decade, dividends have become a standalone asset class instead of a mere side product of an equity investment. We introduce a framework based on polynomial jump-diffusions to jointly price the term structures of dividends and interest rates. Prices for dividend futures, bonds, and the dividend paying stock are given in closed form. We present an efficient moment based approximation method for option pricing. In a calibration exercise we show that a parsimonious model specification has a good fit with Euribor interest rate swaps and swaptions, Euro Stoxx 50 index dividend futures and dividend futures options, and Euro Stoxx 50 index options.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118257&date=2018-07-31Seminar 217, Risk Management: Is motor insurance ratemaking going to change with telematics and semi-autonomous vehicles?, Aug 28
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118738&date=2018-08-28
Many automobile insurance companies offer the possibility to monitor driving habits and distance driven by means of telematics devices installed in the vehicles. This provides a novel source of data that can be analysed to calculate personalised tariffs. For instance, drivers who accumulate a lot of miles should be charged more for their insurance coverage than those who make little use of their car. However, it can also be argued that drivers with more miles have better driving skills than those who hardly use their vehicle, meaning that the price per mile should decrease with distance driven. The statistical analysis of a real data set by means of machine learning techniques shows the existence of a gaining experience effect for large values of distance travelled, so that longer driving should result in higher premium, but there should be a discount for drivers that accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety. Predictive models provide benchmarks of the impact of semi-autonomous vehicles on insurance rates.<br />
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This talk will cover the award winning paper on semiautonomous vehicle insurance presented in the International Congress of Actuaries in Berlin, June, 2018, which is under revision in Accident Analysis and Prevention and it will also include the contents of a paper entitled “The use of telematics devices to improve automobile insurance rates”, accepted in Risk Analysis.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118738&date=2018-08-28Seminar 217, Risk Management: On Optimal Options Book Execution Strategies with Market Impact, Sep 4
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118739&date=2018-09-04
We consider the optimal execution of a book of options when market impact is a driver of the option price. We aim at minimizing the mean-variance risk criterion for a given market impact function. First, we develop a framework to justify the choice of our market impact function. Our model is inspired from Leland’s option replication with transaction costs where the market impact is directly part of the implied volatility function. The option price is then expressed through a Black– Scholes-like PDE with a modified implied volatility directly dependent on the market impact. We set up a stochastic control framework and solve an Hamilton–Jacobi–Bellman equation using finite differences methods. The expected cost problem suggests that the optimal execution strategy is characterized by a convex increasing trading speed, in contrast to the equity case where the optimal execution strategy results in a rather constant trading speed. However, in such mean valuation framework, the underlying spot price does not seem to affect the agent’s decision. By taking the agent risk aversion into account through a mean-variance approach, the strategy becomes more sensitive to the underlying price evolution, urging the agent to trade faster at the beginning of the strategy.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118739&date=2018-09-04Seminar 217, Risk Management: Capacity constraints in earning, and asset prices before earnings announcements, Sep 11
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118096&date=2018-09-11
This paper proposes an asset pricing model with endogenous allocation of constrained learning capacity, that provides an explanation for abnormal returns before the scheduled release of information about firms, such as quarterly earnings announcements. In equilibrium investors endogenously focus their learning capacity and acquire information about stocks with upcoming announcements, resulting in excess price movements during this period. I show cross-sectional heterogeneity in stock returns and institutional investors' information demand before quarterly earnings announcements that are consistent with the model. The results suggest that limited information acquisition capacity, and investors' optimal allocation response can play a significant role in asset price movements before firms' scheduled announcements.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118096&date=2018-09-11Science Lecture - Artificial Intelligence and the long-term future of humanity, Sep 15
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=119492&date=2018-09-15
The news media in recent years have been full of dire warnings about the risk that AI poses to the human race, coming from well-known figures such as Stephen Hawking and Elon Musk. Should we be concerned? If so, what can we do about it? While some in the mainstream AI community dismiss these concerns, Professor Russell will argue instead that a fundamental reorientation of the field is required to avoid the existential risks that AI might otherwise create. Other risks, such as progressive enfeeblement, seem harder to address.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=119492&date=2018-09-15Seminar 217, Risk Management: Nonstandard Analysis and its Application to Markov Processes, Sep 18
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118740&date=2018-09-18
Nonstandard analysis, a powerful machinery derived from mathematical logic, has had many applications in probability theory as well as stochastic processes. Nonstandard analysis allows construction of a single object - a hyperfinite probability space - which satisfies all the first order logical properties of a finite probability space, but which can be simultaneously viewed as a measure-theoretical probability space via the Loeb construction. As a consequence, the hyperfinite/measure duality has proven to be particularly in porting discrete results into their continuous settings.<br />
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In this talk, for every general-state-space continuous-time Markov process satisfying appropriate conditions, we construct a hyperfinite Markov process to represent it. Hyperfinite Markov processes have all the first order logical properties of a finite Markov process. We establish ergodicity of a large class of general-state-space continuous-time Markov processes via studying their hyperfinite counterpart. We also establish the asymptotical equivalence between mixing times, hitting times and average mixing times for discrete-time general-state-space Markov processes satisfying moderate condition. Finally, we show that our result is applicable to a large class of Gibbs samplers and a large class of Metropolis-Hasting algorithms.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118740&date=2018-09-18Seminar 217, Risk Management: A Deep Learning Investigation of One-Month Momentum, Sep 25
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118741&date=2018-09-25
The one-month return reversal in equity prices was first documented by Jedadeesh (1990), who found that there was a highly significant negative serial correlation in the monthly return series of stocks. This is in contrast to the positive serial correlation of the annual stock returns. Explanations for this effect differ, but the general consensus has been that the trailing one-month return includes a component of overreaction by investors. Since 1990, the one-month return reversal effect has decayed substantially, which has led others to refine it. Asness, Frazzini, Gormsen, and Pedersen (2017) refine this idea by adjusting MAX5 (the average of the five highest daily returns over the trailing month) for trailing volatility. They define a measure SMAX (scaled MAX5), which is the MAX5 divided by the trailing month daily return volatility. SMAX is designed to capture lottery demand in excess of volatility. They show that SMAX has an even stronger one-month return reversal than trailing month return.<br />
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In this talk, I first replicate the results of Jedadeesh and Asness as benchmark models. I confirm that SMAX outperforms simple return reversal over the test period 1993-2017. However, the effectiveness of SMAX declines substantially over the test period. Using an enhanced combination of return statistics, I improve upon SMAX. I further improve upon SMAX by applying Neural Networks to trailing daily active returns. Note that all of these signals decay substantially in effectiveness over the common test period 1998-2017.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118741&date=2018-09-25Seminar 217, Risk Management: Predicting Portfolio Return Volatility at Median Horizons, Oct 2
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118742&date=2018-10-02
Commercially available factor models provide good predictions of short-horizon (e.g. one day or one week) portfolio volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility. These predictions are of significant value to certain short-term investors, such as hedge funds. However, they provide limited guidance to long-term investors, such as Defined Benefit pension plans, individual owners of Defined Contribution pension plans, and insurance companies. Because return volatility is variable and mean-reverting, the square root rule for extrapolating short-term volatility predictions to medium-horizon (one year to five years) risk predictions systematically overstates (understates) medium-horizon risk when short-term volatility is high (low). In this paper, we propose a computationally feasible method for extrapolating to medium-horizon risk predictions in one-factor models that substantially outperforms the square root rule.http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118742&date=2018-10-02Seminar 217, Risk Management: Topic Forthcoming, Oct 9
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118749&date=2018-10-09
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118749&date=2018-10-09Seminar 217, Risk Management: Topic Forthcoming, Oct 16
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118748&date=2018-10-16
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118748&date=2018-10-16Seminar 217, Risk Management: Topic Forthcoming, Oct 23
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118743&date=2018-10-23
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118743&date=2018-10-23Seminar 217, Risk Management: Topic Forthcoming, Nov 13
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118744&date=2018-11-13
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118744&date=2018-11-13Seminar 217, Risk Management: Topic Forthcoming, Nov 27
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118746&date=2018-11-27
http://events.berkeley.edu/index.php/calendar/sn/IEOR.html?event_ID=118746&date=2018-11-27