2,899 research outputs found
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
Bayesian forecasting and scalable multivariate volatility analysis using simultaneous graphical dynamic models
The recently introduced class of simultaneous graphical dynamic linear models
(SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting
to higher-dimensional time series. This paper advances the methodology of
SGDLMs, developing and embedding a novel, adaptive method of simultaneous
predictor selection in forward filtering for on-line learning and forecasting.
The advances include developments in Bayesian computation for scalability, and
a case study in exploring the resulting potential for improved short-term
forecasting of large-scale volatility matrices. A case study concerns financial
forecasting and portfolio optimization with a 400-dimensional series of daily
stock prices. Analysis shows that the SGDLM forecasts volatilities and
co-volatilities well, making it ideally suited to contributing to quantitative
investment strategies to improve portfolio returns. We also identify
performance metrics linked to the sequential Bayesian filtering analysis that
turn out to define a leading indicator of increased financial market stresses,
comparable to but leading the standard St. Louis Fed Financial Stress Index
(STLFSI) measure. Parallel computation using GPU implementations substantially
advance the ability to fit and use these models.Comment: 28 pages, 9 figures, 7 table
State-Observation Sampling and the Econometrics of Learning Models
In nonlinear state-space models, sequential learning about the hidden state
can proceed by particle filtering when the density of the observation
conditional on the state is available analytically (e.g. Gordon et al., 1993).
This condition need not hold in complex environments, such as the
incomplete-information equilibrium models considered in financial economics. In
this paper, we make two contributions to the learning literature. First, we
introduce a new filtering method, the state-observation sampling (SOS) filter,
for general state-space models with intractable observation densities. Second,
we develop an indirect inference-based estimator for a large class of
incomplete-information economies. We demonstrate the good performance of these
techniques on an asset pricing model with investor learning applied to over 80
years of daily equity returns
Portfolio optimization with discrete proportional transaction costs under stochastic volatility
This paper is devoted to evaluating the optimal self-financing portfolio and the optimal trading frequency on a risky and risk-free asset to maximize the expected future utility of the terminal wealth in a stochastic volatility setting, when proportional transaction costs are incurred at each discrete trading time. The HARA utility function is used, allowing a simple approximation of the optimization problem, which is implementable forward in time. For each of various transaction cost rates, we find the optimal trading frequency, i.e. the one that attains the maximum of the expected utility at time zero. We study the relation between transaction cost rate and optimal trading frequency. The numerical method used is based on a stochastic volatility particle filtering algorithm, combined with a Monte-Carlo method
Linear and nonlinear filtering in mathematical finance: a review
Copyright @ The Authors 2010This paper presents a review of time series filtering and its applications in mathematical finance. A summary of results of recent empirical studies with market data are presented for yield curve modelling and stochastic volatility modelling. The paper also outlines different approaches to filtering of nonlinear time series
"Pricing Barrier and Average Options under Stochastic Volatility Environment"
This paper proposes a new approximation method of pricing barrier and average options under stochastic volatility environment by applying an asymptotic expansion approach. In particular, a high-order expansion scheme for general multi-dimensional diffusion processes is effectively applied. Moreover, the paper combines a static hedging method with the asymptotic expansion method for pricing barrier options. Finally, numerical examples show that the fourth or fifth-order asymptotic expansion scheme provides sufficiently accurate approximations under the ă-SABR and SABR models.
- …