Stochastic Theory and Control

Stochastic Theory and Control
Author: Bozenna Pasik-Duncan
Publisher: Springer Science & Business Media
Total Pages: 563
Release: 2002-07-24
Genre: Mathematics
ISBN: 3540437770

This volume contains almost all of the papers that were presented at the Workshop on Stochastic Theory and Control that was held at the Univ- sity of Kansas, 18–20 October 2001. This three-day event gathered a group of leading scholars in the ?eld of stochastic theory and control to discuss leading-edge topics of stochastic control, which include risk sensitive control, adaptive control, mathematics of ?nance, estimation, identi?cation, optimal control, nonlinear ?ltering, stochastic di?erential equations, stochastic p- tial di?erential equations, and stochastic theory and its applications. The workshop provided an opportunity for many stochastic control researchers to network and discuss cutting-edge technologies and applications, teaching and future directions of stochastic control. Furthermore, the workshop focused on promoting control theory, in particular stochastic control, and it promoted collaborative initiatives in stochastic theory and control and stochastic c- trol education. The lecture on “Adaptation of Real-Time Seizure Detection Algorithm” was videotaped by the PBS. Participants of the workshop have been involved in contributing to the documentary being ?lmed by PBS which highlights the extraordinary work on “Math, Medicine and the Mind: Discovering Tre- ments for Epilepsy” that examines the e?orts of the multidisciplinary team on which several of the participants of the workshop have been working for many years to solve one of the world’s most dramatic neurological conditions. Invited high school teachers of Math and Science were among the part- ipants of this professional meeting.

Macroeconometrics and Time Series Analysis

Macroeconometrics and Time Series Analysis
Author: Steven Durlauf
Publisher: Springer
Total Pages: 417
Release: 2016-04-30
Genre: Business & Economics
ISBN: 0230280838

Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.

Option Pricing with a Dividend General Equilibrium Model

Option Pricing with a Dividend General Equilibrium Model
Author: Kyriakos Chourdakis
Publisher:
Total Pages: 45
Release: 2002
Genre:
ISBN:

This paper derives a general equilibrium option-pricing model for a European call assuming that the economy is exogenously driven by a dividend process following Hamilton's (1989) Markov regime switching model. The derived formula is used to investigate if the European call option prices are consistently priced with the stock market prices. This is done by obtaining the implied risk aversion preferences, based on traded option prices data.

Option Pricing with Unobserved and Regime-Switching Volatility

Option Pricing with Unobserved and Regime-Switching Volatility
Author: Sean D. Campbell
Publisher:
Total Pages:
Release: 1999
Genre:
ISBN:

In this paper we use a regime-switching process to model the unobserved volatility of the underlying asset and derive a closed-form, risk-neutral option pricing formula. Specifically, our model implies the state price density (SPD) is a time-varying mixture of normals which can provide for time-varying excess kurtosis and skewness as agents learn about the state of volatility from realized returns. Furthermore, we show that our model generates the kinds of volatility quot;smilesquot; commonly found in option markets. We apply our two and three regime models to weekly Samp;P 500 option data and find our model fits the data better than other popular pricing models. Additionally, we find evidence that stock returns can be well-described by a markov switching framework with a very persistent low volatility regime followed by a less persistent moderate volatility regime and a highly non-persistent crash regime. Our estimation results don't suffer the so called quot;Peso Problemquot; as they come from option prices instead of the observed stock returns.

Option Pricing Under Regime Switching (analytical, PDE, and FFT Methods)

Option Pricing Under Regime Switching (analytical, PDE, and FFT Methods)
Author: Mohammad Yousef Akhavein Sohrabi
Publisher:
Total Pages: 83
Release: 2011
Genre:
ISBN:

Although globally used in option pricing, the Black-Scholes model has not been able to reflect the evolution of stocks in the real world. A regime-switching model which allows jumps in the underlying asset prices and the parameters of the corresponding stochastic process is more accurate. We evaluate the analytical solution for pricing of European options under a two-state regime switching model. Both the convergence of the analytical solution and the feature of implied volatility are investigated through numerical examples.

Options Pricing and Hedging in a Regime-Switching Volatility Model

Options Pricing and Hedging in a Regime-Switching Volatility Model
Author: Melissa Anne Mielkie
Publisher:
Total Pages: 320
Release: 2014
Genre:
ISBN:

Both deterministic and stochastic volatility models have been used to price and hedge options. Observation of real market data suggests that volatility, while stochastic, is well modelled as alternating between two states. Under this two-state regime-switching framework, we derive coupled pricing partial differential equations (PDEs) with the inclusion of a state-dependent market price of volatility risk (MPVR) term. Since there is no closed-form solution for this pricing problem, we apply and compare two approaches to solving the coupled PDEs, assuming constant Poisson intensities. First we solve the problem using numerical solution techniques, through the application of the Crank- Nicolson numerical scheme. We also obtain approximate solutions in terms of known Black- Scholes formulae by reformulating our problem and applying the Cauchy-Kowalevski PDE theorem. Both our pricing equations and our approximate solutions give way to the analysis of the impact of our state-dependent MPVR on theoretical option prices. Using financially intuitive constraints on our option prices and Deltas, we prove the necessity of a negative MPVR. An exploration of the regime-switching option prices and their implied volatilities is given, as well as numerical results and intuition supporting our mathematical proofs. Given our regime-switching framework, there are several different hedging strategies to investigate. We consider using an option to hedge against a potential regime shift. Some practical problems arise with this approach, which lead us to set up portfolios containing a basket of two hedging options. To be more precise, we consider the effects of an option going too far in- and out-of-the-money on our hedging strategy, and introduce limits on the magnitude of such hedging option positions. A complementary approach, where constant volatility is assumed and investor's risk preferences are taken into account, is also analysed. Analysis of empirical data supports the hypothesis that volatility levels are a effected by upcoming financial events. Finally, we present an extension of our regime-switching framework with deterministic Poisson intensities. In particular, we investigate the impact of time and stock varying Poisson intensities on option prices and their corresponding implied volatilities, using numerical solution techniques. A discussion of some event-driven hedging strategies is given.

Stochastic Optimization

Stochastic Optimization
Author: Stanislav Uryasev
Publisher: Springer Science & Business Media
Total Pages: 438
Release: 2013-03-09
Genre: Technology & Engineering
ISBN: 1475765940

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.

Modeling, Stochastic Control, Optimization, and Applications

Modeling, Stochastic Control, Optimization, and Applications
Author: George Yin
Publisher: Springer
Total Pages: 593
Release: 2019-07-16
Genre: Mathematics
ISBN: 3030254984

This volume collects papers, based on invited talks given at the IMA workshop in Modeling, Stochastic Control, Optimization, and Related Applications, held at the Institute for Mathematics and Its Applications, University of Minnesota, during May and June, 2018. There were four week-long workshops during the conference. They are (1) stochastic control, computation methods, and applications, (2) queueing theory and networked systems, (3) ecological and biological applications, and (4) finance and economics applications. For broader impacts, researchers from different fields covering both theoretically oriented and application intensive areas were invited to participate in the conference. It brought together researchers from multi-disciplinary communities in applied mathematics, applied probability, engineering, biology, ecology, and networked science, to review, and substantially update most recent progress. As an archive, this volume presents some of the highlights of the workshops, and collect papers covering a broad range of topics.