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Series representation of the pricing formula for the European option driven by space-time fractional diffusion. (arXiv:1712.04990v1 [q-fin.MF])

December 14, 2017 by Quantitative Finance at arXiv   Comments (0)

In this paper, we show that the price of an European call option, whose
underlying asset price is driven by the space-time fractional diffusion, can be
expressed in terms of rapidly convergent double-series. The series formula can
be obtained from the Mellin-Barnes representation of the option price with help
of residue summation in $\mathbb{C}^2$. We also derive the series
representation for the associated risk-neutral factors, obtained by Esscher
transform of the space-time fractional Green...

The Mathematics of Market Timing. (arXiv:1712.05031v1 [q-fin.PM])

December 14, 2017 by Quantitative Finance at arXiv   Comments (0)

Market timing is an investment technique that tries to continuously switch
investment into assets forecast to have better returns. What is the likelihood
of having a successful market timing strategy? With an emphasis on modeling
simplicity, I calculate the feasible set of market timing portfolios using
index mutual fund data for perfectly timed (by hindsight) all or nothing
quarterly switching between two asset classes, US stocks and bonds over the
time period 1993--2017. The historical...

The consentaneous model of the financial markets exhibiting spurious nature of long-range memory. (arXiv:1712.05121v1 [q-fin.ST])

December 14, 2017 by Quantitative Finance at arXiv   Comments (0)

It is widely accepted that there is strong persistence in financial time
series. The origin of the observed persistence, or long-range memory, is still
an open problem as the observed phenomenon could be a spurious effect. Earlier
we have proposed the consentaneous model of the financial markets based on the
non-linear stochastic differential equations. The consentaneous model
successfully reproduces empirical probability and power spectral densities of
volatility. This approach is...

The evaluation of geometric Asian power options under time changed mixed fractional Brownian motion. (arXiv:1712.05254v1 [q-fin.PR])

December 14, 2017 by Quantitative Finance at arXiv   Comments (0)

The aim of this paper is to evaluate geometric Asian option by a mixed
fractional subdiffusive Black-Scholes model. We derive a pricing formula for
geometric Asian option when the underlying stock follows a time changed mixed
fractional Brownian motion. We then apply the results to price Asian power
options on the stocks that pay constant dividends when the payoff is a power
function. Finally, lower bound of Asian options and some special cases are
provided.

The Transformation of the Global Reinsurance Industry

December 14, 2017 by All About Alpha   Comments (0)

A new paper from Milliman, a consultant to the insurance and financial services industries, discusses the ongoing transformation in and of the global reinsurance industry that alternative investment has created. About 20 years ago, in the wake of Hurricane Andrew and the Northridge earthquake, catastrophe bonds caught on as aRead More

Slime mould: the fundamental mechanisms of cognition

December 14, 2017 by Complexity Digest   Comments (0)

The slime mould Physarum polycephalum has been used in developing unconventional computing devices for in which the slime mould played a role of a sensing, actuating, and computing device. These devices treated the slime mould rather as an active living substrate yet the slime mould is a self-consistent living creature which evolved for millions of years and occupied most part of the world, but in any case, that living entity did not own true cognition, just automated biochemical mechanisms. To...

Practical applications of reinforcement learning in industry

December 14, 2017 by The Practical Quant   Comments (0)

[A version of this post appears on the O'Reilly Radar.]An overview of commercial and industrial applications of reinforcement learning.The flurry of headlines surrounding AlphaGo Zero (the most recent version of DeepMind’s AI system for playing Go) means interest in reinforcement learning (RL) is bound to increase. Next to deep learning, RL is among the most followed topics in AI. For most companies, RL is something to investigate and evaluate but few organizations have identified use cases...

A Twist

December 14, 2017 by The Reformed Broker   Comments (0)

I'm less confident that the prices of coins will be central to the mass adoption of blockchain....

CAPS 2018: Complexity And Policy Studies

December 14, 2017 by Complexity Digest   Comments (0)

CAPS 2018 is the second International Conference on Complexity and Policy Studies. This is a cross-disciplinary conference for research in which the tools of Complex Systems are used to examine a wide range of policies and procedures that promote, emphasize, contribute to, improve, or otherwise positively affect society. This scope includes new definitions, measures, and methodologies for tracking, understanding, and predicting impacts and trends, data sets, analytical methods, actors and...

QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds. (arXiv:1712.04609v1 [q-fin.CP])

December 13, 2017 by Quantitative Finance at arXiv   Comments (0)

This paper presents a discrete-time option pricing model that is rooted in
Reinforcement Learning (RL), and more specifically in the famous Q-Learning
method of RL. We construct a risk-adjusted Markov Decision Process for a
discrete-time version of the classical Black-Scholes-Merton (BSM) model, where
the option price is an optimal Q-function. Pricing is done by learning to
dynamically optimize risk-adjusted returns for an option replicating portfolio,
as in the Markowitz portfolio theory....