<![CDATA[MoneyScience: Research]]>
http://www.moneyscience.com/pg/blog-directory/research?view=rss
FinancialResearchFocushttps://feedburner.google.comhttp://www.moneyscience.com/pg/blog/arXiv/read/805143/measurement-of-the-evolution-of-technology-a-new-perspective-arxiv180308698v1-qfinecSun, 25 Mar 2018 19:43:02 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/jDpd2E6-fMc/measurement-of-the-evolution-of-technology-a-new-perspective-arxiv180308698v1-qfinec
<![CDATA[Measurement of the evolution of technology: A new perspective. (arXiv:1803.08698v1 [q-fin.EC])]]>A fundamental problem in technological studies is how to measure the
evolution of technology. The literature has suggested several approaches to
measuring the level of technology (or state-of-the-art) and changes in
technology. However, the measurement of technological advances and
technological evolution is often a complex and elusive topic in science. The
study here starts by establishing a conceptual framework of technological
evolution based on the theory of technological parasitism, in broad analogy
with biology. Then, the measurement of the evolution of technology is modelled
in terms of morphological changes within complex systems considering the
interaction between a host technology and its subsystems of technology. The
coefficient of evolutionary growth of the model here indicates the grade and
type of the evolutionary route of a technology. This coefficient is quantified
in real instances using historical data of farm tractor, freight locomotive and
electricity generation technology in steam-powered plants and
internal-combustion plants. Overall, then, it seems that the approach here is
appropriate in grasping the typology of evolution of complex systems of
technology and in predicting which technologies are likeliest to evolve
rapidly.
]]>805143http://www.moneyscience.com/pg/blog/arXiv/read/805143/measurement-of-the-evolution-of-technology-a-new-perspective-arxiv180308698v1-qfinechttp://www.moneyscience.com/pg/blog/arXiv/read/805142/fast-swaption-pricing-in-gaussian-term-structure-models-arxiv180308803v1-qfinmfSun, 25 Mar 2018 19:41:58 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/cFpQalHz2ls/fast-swaption-pricing-in-gaussian-term-structure-models-arxiv180308803v1-qfinmf
<![CDATA[Fast swaption pricing in Gaussian term structure models. (arXiv:1803.08803v1 [q-fin.MF])]]>We propose a fast and accurate numerical method for pricing European
swaptions in multi-factor Gaussian term structure models. Our method can be
used to accelerate the calibration of such models to the volatility surface.
The pricing of an interest rate option in such a model involves evaluating a
multi-dimensional integral of the payoff of the claim on a domain where the
payoff is positive. In our method, we approximate the exercise boundary of the
state space by a hyperplane tangent to the maximum probability point on the
boundary and simplify the multi-dimensional integration into an analytical
form. The maximum probability point can be determined using the gradient
descent method. We demonstrate that our method is superior to previous methods
by comparing the results to the price obtained by numerical integration.
]]>805142http://www.moneyscience.com/pg/blog/arXiv/read/805142/fast-swaption-pricing-in-gaussian-term-structure-models-arxiv180308803v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/805141/a-structural-heathjarrowmorton-framework-for-consistent-intraday-spot-and-futures-electricity-prices-arxiv180308831v1-qfinmfSun, 25 Mar 2018 19:40:55 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/cPF_DygqgSM/a-structural-heathjarrowmorton-framework-for-consistent-intraday-spot-and-futures-electricity-prices-arxiv180308831v1-qfinmf
<![CDATA[A structural Heath-Jarrow-Morton framework for consistent intraday, spot, and futures electricity prices. (arXiv:1803.08831v1 [q-fin.MF])]]>In this paper we introduce a flexible HJM-type framework that allows for
consistent modelling of prices of intraday, spot, futures, and options on
futures. The link with the intraday market is in particular important, since
these markets gain increasing popularity. This framework is based on stochastic
processes with economic interpretations and consistent with the initial term
structure given in the form of a price forward curve. Furthermore, the
framework allows for day-ahead spot price models to be used in an HJM setting.
We include several explicit examples of classical spot price models but also
show how structural models (i.e. models based on supply and demand) and factor
models can be used.
]]>805141http://www.moneyscience.com/pg/blog/arXiv/read/805141/a-structural-heathjarrowmorton-framework-for-consistent-intraday-spot-and-futures-electricity-prices-arxiv180308831v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/804657/an-economic-bubble-model-and-its-first-passage-time-arxiv180308160v1-qfinmfThu, 22 Mar 2018 19:51:12 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/LQHjkI9NsII/an-economic-bubble-model-and-its-first-passage-time-arxiv180308160v1-qfinmf
<![CDATA[An Economic Bubble Model and Its First Passage Time. (arXiv:1803.08160v1 [q-fin.MF])]]>We introduce a new diffusion process Xt to describe asset prices within an
economic bubble cycle. The main feature of the process, which differs from
existing models, is the drift term where a mean-reversion is taken based on an
exponential decay of the scaled price. Our study shows the scaling factor on Xt
is crucial for modelling economic bubbles as it mitigates the dependence
structure between the price and parameters in the model. We prove both the
process and its first passage time are well-defined. An efficient calibration
scheme, together with the probability density function for the process are
given. Moreover, by employing the perturbation technique, we deduce the
closed-form density for the downward first passage time, which therefore can be
used in estimating the burst time of an economic bubble. The object of this
study is to understand the asset price dynamics when a financial bubble is
believed to form, and correspondingly provide estimates to the bubble crash
time. Calibration examples on the US dot-com bubble and the 2007 Chinese stock
market crash verify the effectiveness of the model itself. The example on
BitCoin prediction confirms that we can provide meaningful estimate on the
downward probability for asset prices.
]]>804657http://www.moneyscience.com/pg/blog/arXiv/read/804657/an-economic-bubble-model-and-its-first-passage-time-arxiv180308160v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/804656/optimal-price-management-in-retail-energy-markets-an-impulse-control-problem-with-asymptotic-estimates-arxiv180308166v1-mathocThu, 22 Mar 2018 19:50:07 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/kgsF98gt6uc/optimal-price-management-in-retail-energy-markets-an-impulse-control-problem-with-asymptotic-estimates-arxiv180308166v1-mathoc
<![CDATA[Optimal price management in retail energy markets: an impulse control problem with asymptotic estimates. (arXiv:1803.08166v1 [math.OC])]]>We consider a retailer who buys energy in the wholesale market and resells it
to final consumers. The retailer has to decide when to intervene to change the
price he asks to his customers, in order to maximize his income. We model the
problem as an infinite-horizon stochastic impulse control problem. We
characterize an optimal price strategy and provide analytical existence results
for the equations involved. We then investigate the dependence on the
intervention cost. In particular, we prove that the measure of the continuation
region is asymptotic to the fourth root of the cost. Finally, we provide some
numerical results and consider a suitable extension of the model.
]]>804656http://www.moneyscience.com/pg/blog/arXiv/read/804656/optimal-price-management-in-retail-energy-markets-an-impulse-control-problem-with-asymptotic-estimates-arxiv180308166v1-mathochttp://www.moneyscience.com/pg/blog/arXiv/read/804655/financial-contagion-in-a-generalized-stochastic-block-model-arxiv180308169v1-qfinrmThu, 22 Mar 2018 19:49:02 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/YmUnCHGG0Rg/financial-contagion-in-a-generalized-stochastic-block-model-arxiv180308169v1-qfinrm
<![CDATA[Financial Contagion in a Generalized Stochastic Block Model. (arXiv:1803.08169v1 [q-fin.RM])]]>We extend analytic large network results on default contagion in random
graphs to capture a pronounced Block Model structure. This includes as a
special case the Core-Periphery network structure, which plays a prominent role
in recent research on systemic risk. Further, in the existing literature on
systemic risk using random graph methods the problematic assumption that the
distribution of liabilities solely depends on the creditor type seems to
persist. Under this assumption a straightforward application of the law of
large numbers allows to turn edge related random elements into deterministic
vertex properties. Here we study a general setting in which the liabilities may
depend on both the creditor and the debtor where this argument breaks down and
a direct asymptotic analysis of the edge weighted random graph becomes
necessary. Among several other applications our results allow us to obtain
resilience conditions for the entire network (for example the global financial
network) based only on subnetwork conditions. Contrasting earlier research we
also give an example that demonstrates how reshuffling edge weights to form
blocks can in fact impact resilience even for otherwise very homogeneous
networks.
]]>804655http://www.moneyscience.com/pg/blog/arXiv/read/804655/financial-contagion-in-a-generalized-stochastic-block-model-arxiv180308169v1-qfinrmhttp://www.moneyscience.com/pg/blog/arXiv/read/804654/mislearning-from-censored-data-gamblers-fallacy-in-a-search-problem-arxiv180308170v1-qfinecThu, 22 Mar 2018 19:47:58 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/0n8VKFLPRHU/mislearning-from-censored-data-gamblers-fallacy-in-a-search-problem-arxiv180308170v1-qfinec
<![CDATA[Mislearning from Censored Data: Gambler's Fallacy in a Search Problem. (arXiv:1803.08170v1 [q-fin.EC])]]>In the context of a sequential search problem, I explore large-generations
learning dynamics for agents who suffer from the "gambler's fallacy" - the
statistical bias of anticipating too much regression to the mean for
realizations of independent random events. Searchers are uncertain about search
pool qualities of different periods but infer these fundamentals from search
outcomes of the previous generation. Searchers' stopping decisions impose a
censoring effect on the data of their successors, as the values they would have
found in later periods had they kept searching remain unobserved. While
innocuous for rational agents, this censoring effect interacts with the
gambler's fallacy and creates a feedback loop between distorted stopping rules
and pessimistic beliefs about search pool qualities of later periods. In
general settings, the stopping rules used by different generations
monotonically converge to a steady-state rule that stops searching earlier than
optimal. In settings where true pool qualities increase over time - so there is
option value in rejecting above-average early draws - learning is monotonically
harmful and welfare strictly decreases across generations.
]]>804654http://www.moneyscience.com/pg/blog/arXiv/read/804654/mislearning-from-censored-data-gamblers-fallacy-in-a-search-problem-arxiv180308170v1-qfinechttp://www.moneyscience.com/pg/blog/arXiv/read/804653/smart-twap-trading-in-continuoustime-equilibria-arxiv180308336v1-qfinmfThu, 22 Mar 2018 19:46:55 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/omJ64N7scWc/smart-twap-trading-in-continuoustime-equilibria-arxiv180308336v1-qfinmf
<![CDATA[Smart TWAP Trading in Continuous-Time Equilibria. (arXiv:1803.08336v1 [q-fin.MF])]]>This paper presents a continuous-time equilibrium model of liquidity
provision in a market with multiple strategic investors with intraday trading
targets. We show analytically that there are infinitely many Nash equilibria.
We solve for the welfare-maximizing equilibrium and the competitive
equilibrium, and we illustrate that these equilibria are different. The model
is easily computed numerically, and we provide a number of numerical
illustrations.
]]>804653http://www.moneyscience.com/pg/blog/arXiv/read/804653/smart-twap-trading-in-continuoustime-equilibria-arxiv180308336v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/804652/large-largetrader-activity-weakens-the-long-memory-of-limit-order-markets-arxiv180308390v1-qfinstThu, 22 Mar 2018 19:45:52 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/9u5s0DcT2UE/large-largetrader-activity-weakens-the-long-memory-of-limit-order-markets-arxiv180308390v1-qfinst
<![CDATA[Large large-trader activity weakens the long memory of limit order markets. (arXiv:1803.08390v1 [q-fin.ST])]]>Using more than 6.7 billions of trades, we explore how the tick-by-tick
dynamics of limit order books depends on the aggregate actions of large
investment funds on a much larger (quarterly) timescale. In particular, we find
that the well-established long memory of market order signs is markedly weaker
when large investment funds trade either in a directional way and even weaker
when their aggregate participation ratio is large. Conversely, we investigate
to what respect a weaker memory of market order signs predicts that an asset is
being actively traded by large funds. Theoretical arguments suggest two simple
mechanisms that contribute to the observed effect: a larger number of active
meta-orders and a modification of the distribution of size of meta-orders.
Empirical evidence suggests that the number of active meta-orders is the most
important contributor to the loss of market order sign memory.
]]>804652http://www.moneyscience.com/pg/blog/arXiv/read/804652/large-largetrader-activity-weakens-the-long-memory-of-limit-order-markets-arxiv180308390v1-qfinsthttp://www.moneyscience.com/pg/blog/arXiv/read/804651/scaling-properties-of-extreme-price-fluctuations-in-bitcoin-markets-arxiv180308405v1-qfinstThu, 22 Mar 2018 19:44:47 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/XGnzg7BsHe8/scaling-properties-of-extreme-price-fluctuations-in-bitcoin-markets-arxiv180308405v1-qfinst
<![CDATA[Scaling properties of extreme price fluctuations in Bitcoin markets. (arXiv:1803.08405v1 [q-fin.ST])]]>Detection of power-law behavior and studies of scaling exponents uncover the
characteristics of complexity in many real world phenomena. The complexity of
financial markets has always presented challenging issues and provided
interesting findings, such as the inverse cubic law in the tails of stock price
fluctuation distributions. Motivated by the rise of novel digital assets based
on blockchain technology, we study the distributions of cryptocurrency price
fluctuations. We consider Bitcoin returns over various time intervals and from
multiple digital exchanges, in order to investigate the existence of universal
scaling behavior in the tails, and ascertain whether the scaling exponent
supports the presence of a finite second moment. We provide empirical evidence
on slowly decaying tails in the distributions of returns over multiple time
intervals and different exchanges, corresponding to a power-law. We estimate
the scaling exponent and find an asymptotic power-law behavior with 2 <
{\alpha} < 2.5 suggesting that Bitcoin returns, in addition to being more
volatile, also exhibit heavier tails than stocks, which are known to be around
3. Our results also imply the existence of a finite second moment, thus
providing a fundamental basis for the usage of standard financial theories and
covariance-based techniques in risk management and portfolio optimization
scenarios.
]]>804651http://www.moneyscience.com/pg/blog/arXiv/read/804651/scaling-properties-of-extreme-price-fluctuations-in-bitcoin-markets-arxiv180308405v1-qfinsthttp://www.moneyscience.com/pg/blog/arXiv/read/804486/on-the-basel-liquidity-formula-for-elliptical-distributions-arxiv180307590v1-qfinrmWed, 21 Mar 2018 19:45:26 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/euXsnU5gxyQ/on-the-basel-liquidity-formula-for-elliptical-distributions-arxiv180307590v1-qfinrm
<![CDATA[On the Basel Liquidity Formula for Elliptical Distributions. (arXiv:1803.07590v1 [q-fin.RM])]]>A justification of the Basel liquidity formula for risk capital in the
trading book is given under the assumption that market risk-factor changes form
a Gaussian white noise process over 10-day time steps and changes to P&L are
linear in the risk-factor changes. A generalization of the formula is derived
under the more general assumption that risk-factor changes are multivariate
elliptical. It is shown that the Basel formula tends to be conservative when
the elliptical distributions are from the heavier-tailed generalized hyperbolic
family. As a by-product of the analysis a Fourier approach to calculating
expected shortfall for general symmetric loss distributions is developed.
]]>804486http://www.moneyscience.com/pg/blog/arXiv/read/804486/on-the-basel-liquidity-formula-for-elliptical-distributions-arxiv180307590v1-qfinrmhttp://www.moneyscience.com/pg/blog/arXiv/read/804485/asymptotic-optimal-portfolio-in-fast-meanreverting-stochastic-environments-arxiv180307720v1-qfinmfWed, 21 Mar 2018 19:44:21 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/k9veptyiO3k/asymptotic-optimal-portfolio-in-fast-meanreverting-stochastic-environments-arxiv180307720v1-qfinmf
<![CDATA[Asymptotic Optimal Portfolio in Fast Mean-reverting Stochastic Environments. (arXiv:1803.07720v1 [q-fin.MF])]]>This paper studies the portfolio optimization problem when the investor's
utility is general and the return and volatility of the risky asset are fast
mean-reverting, which are important to capture the fast-time scale in the
modeling of stock price volatility. Motivated by the heuristic derivation in
[J.-P. Fouque, R. Sircar and T. Zariphopoulou, \emph{Mathematical Finance},
2016], we propose a zeroth order strategy, and show its asymptotic optimality
within a specific (smaller) family of admissible strategies under proper
assumptions. This optimality result is achieved by establishing a first order
approximation of the problem value associated to this proposed strategy using
singular perturbation method, and estimating the risk-tolerance functions. The
results are natural extensions of our previous work on portfolio optimization
in a slowly varying stochastic environment [J.-P. Fouque and R. Hu, \emph{SIAM
Journal on Control and Optimization}, 2017], and together they form a whole
picture of analyzing portfolio optimization in both fast and slow environments.
]]>804485http://www.moneyscience.com/pg/blog/arXiv/read/804485/asymptotic-optimal-portfolio-in-fast-meanreverting-stochastic-environments-arxiv180307720v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/804484/pricing-credit-default-swap-subject-to-counterparty-risk-and-collateralization-arxiv180307843v1-qfincpWed, 21 Mar 2018 19:43:17 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/2hggXVxvklg/pricing-credit-default-swap-subject-to-counterparty-risk-and-collateralization-arxiv180307843v1-qfincp
<![CDATA[Pricing Credit Default Swap Subject to Counterparty Risk and Collateralization. (arXiv:1803.07843v1 [q-fin.CP])]]>This article presents a new model for valuing a credit default swap (CDS)
contract that is affected by multiple credit risks of the buyer, seller and
reference entity. We show that default dependency has a significant impact on
asset pricing. In fact, correlated default risk is one of the most pervasive
threats in financial markets. We also show that a fully collateralized CDS is
not equivalent to a risk-free one. In other words, full collateralization
cannot eliminate counterparty risk completely in the CDS market.
]]>804484http://www.moneyscience.com/pg/blog/arXiv/read/804484/pricing-credit-default-swap-subject-to-counterparty-risk-and-collateralization-arxiv180307843v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/804483/a-path-integral-based-model-for-stocks-and-order-dynamics-arxiv180307904v1-qfincpWed, 21 Mar 2018 19:42:12 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/Y1N-_fgaxF0/a-path-integral-based-model-for-stocks-and-order-dynamics-arxiv180307904v1-qfincp
<![CDATA[A path integral based model for stocks and order dynamics. (arXiv:1803.07904v1 [q-fin.CP])]]>We introduce a model for the short-term dynamics of financial assets based on
an application to finance of quantum gauge theory, developing ideas of Ilinski.
We present a numerical algorithm for the computation of the probability
distribution of prices and compare the results with APPLE stocks prices and the
S&P500 index.
]]>804483http://www.moneyscience.com/pg/blog/arXiv/read/804483/a-path-integral-based-model-for-stocks-and-order-dynamics-arxiv180307904v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/804307/fear-universality-and-doubt-in-asset-price-movements-arxiv180307138v1-qfinmfTue, 20 Mar 2018 19:45:26 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/nEaF3Zv_w5c/fear-universality-and-doubt-in-asset-price-movements-arxiv180307138v1-qfinmf
<![CDATA[Fear Universality and Doubt in Asset price movements. (arXiv:1803.07138v1 [q-fin.MF])]]>We take a look the changes of different asset prices over variable periods,
using both traditional and spectral methods, and discover universality
phenomena which hold (in some cases) across asset classes.
]]>804307http://www.moneyscience.com/pg/blog/arXiv/read/804307/fear-universality-and-doubt-in-asset-price-movements-arxiv180307138v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/804306/exploring-the-predictability-of-rangebased-volatility-estimators-using-rnns-arxiv180307152v1-qfincpTue, 20 Mar 2018 19:44:22 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/Uk9TNYLTo8Y/exploring-the-predictability-of-rangebased-volatility-estimators-using-rnns-arxiv180307152v1-qfincp
<![CDATA[Exploring the predictability of range-based volatility estimators using RNNs. (arXiv:1803.07152v1 [q-fin.CP])]]>We investigate the predictability of several range-based stock volatility
estimators, and compare them to the standard close-to-close estimator which is
most commonly acknowledged as the volatility. The patterns of volatility
changes are analyzed using LSTM recurrent neural networks, which are a state of
the art method of sequence learning. We implement the analysis on all current
constituents of the Dow Jones Industrial Average index, and report averaged
evaluation results. We find that changes in the values of range-based
estimators are more predictable than that of the estimator using daily closing
values only.
]]>804306http://www.moneyscience.com/pg/blog/arXiv/read/804306/exploring-the-predictability-of-rangebased-volatility-estimators-using-rnns-arxiv180307152v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/804305/mixing-lsmc-and-pde-methods-to-price-bermudan-options-arxiv180307216v1-qfincpTue, 20 Mar 2018 19:43:19 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/K-X5MlTx6fc/mixing-lsmc-and-pde-methods-to-price-bermudan-options-arxiv180307216v1-qfincp
<![CDATA[Mixing LSMC and PDE Methods to Price Bermudan Options. (arXiv:1803.07216v1 [q-fin.CP])]]>We develop a mixed least squares Monte Carlo-partial differential equation
(LSMC-PDE) method for pricing Bermudan style options on assets whose volatility
is stochastic. The algorithm is formulated for an arbitrary number of assets
and driving processes and we prove the algorithm converges probabilistically.
We also discuss two methods to greatly improve the algorithm's computational
complexity. Our numerical examples focus on the single ($2d$) and
multi-dimensional ($4d$) Heston model and we compare our hybrid algorithm with
classical LSMC approaches. In both cases, we demonstrate that the hybrid
algorithm has significantly lower variance than traditional LSMC. Moreover, for
the $2d$ example, where it is possible to visualize, we demonstrate that the
optimal exercise strategy from the hybrid algorithm is significantly more
accurate compared to the one from the full LSMC when using a finite difference
approach as a reference.
]]>804305http://www.moneyscience.com/pg/blog/arXiv/read/804305/mixing-lsmc-and-pde-methods-to-price-bermudan-options-arxiv180307216v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/804304/sparse-reduced-rank-regression-with-nonconvex-regularization-arxiv180307247v1-statmlTue, 20 Mar 2018 19:42:15 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/9LiifgLZFsk/sparse-reduced-rank-regression-with-nonconvex-regularization-arxiv180307247v1-statml
<![CDATA[Sparse Reduced Rank Regression With Nonconvex Regularization. (arXiv:1803.07247v1 [stat.ML])]]>In this paper, the estimation problem for sparse reduced rank regression
(SRRR) model is considered. The SRRR model is widely used for dimension
reduction and variable selection with applications in signal processing,
econometrics, etc. The problem is formulated to minimize the least squares loss
with a sparsity-inducing penalty considering an orthogonality constraint.
Convex sparsity-inducing functions have been used for SRRR in literature. In
this work, a nonconvex function is proposed for better sparsity inducing. An
efficient algorithm is developed based on the alternating minimization (or
projection) method to solve the nonconvex optimization problem. Numerical
simulations show that the proposed algorithm is much more efficient compared to
the benchmark methods and the nonconvex function can result in a better
estimation accuracy.
]]>804304http://www.moneyscience.com/pg/blog/arXiv/read/804304/sparse-reduced-rank-regression-with-nonconvex-regularization-arxiv180307247v1-statmlhttp://www.moneyscience.com/pg/blog/arXiv/read/804133/mean-reverting-portfolios-via-penalized-oulikelihood-estimation-arxiv180306460v1-qfinpmMon, 19 Mar 2018 20:02:18 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/dYAeA2qTXUU/mean-reverting-portfolios-via-penalized-oulikelihood-estimation-arxiv180306460v1-qfinpm
<![CDATA[Mean Reverting Portfolios via Penalized OU-Likelihood Estimation. (arXiv:1803.06460v1 [q-fin.PM])]]>We study an optimization-based approach to con- struct a mean-reverting
portfolio of assets. Our objectives are threefold: (1) design a portfolio that
is well-represented by an Ornstein-Uhlenbeck process with parameters estimated
by maximum likelihood, (2) select portfolios with desirable characteristics of
high mean reversion and low variance, and (3) select a parsimonious portfolio,
i.e. find a small subset of a larger universe of assets that can be used for
long and short positions. We present the full problem formulation, a
specialized algorithm that exploits partial minimization, and numerical
examples using both simulated and empirical price data.
]]>804133http://www.moneyscience.com/pg/blog/arXiv/read/804133/mean-reverting-portfolios-via-penalized-oulikelihood-estimation-arxiv180306460v1-qfinpmhttp://www.moneyscience.com/pg/blog/arXiv/read/804132/modeling-stock-markets-through-the-reconstruction-of-market-processes-arxiv180306653v1-qfinstMon, 19 Mar 2018 20:01:15 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/tOThrfSYhNI/modeling-stock-markets-through-the-reconstruction-of-market-processes-arxiv180306653v1-qfinst
<![CDATA[Modeling stock markets through the reconstruction of market processes. (arXiv:1803.06653v1 [q-fin.ST])]]>There are two possible ways of interpreting the seemingly stochastic nature
of financial markets: the Efficient Market Hypothesis (EMH) and a set of
stylized facts that drive the behavior of the markets. We show evidence for
some of the stylized facts such as memory-like phenomena in price volatility in
the short term, a power-law behavior and non-linear dependencies on the
returns.
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