<![CDATA[MoneyScience: Research]]>
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FinancialResearchFocushttps://feedburner.google.comhttp://www.moneyscience.com/pg/blog/arXiv/read/795510/a-nonparametric-copula-approach-to-conditional-valueatrisk-arxiv171205527v1-statmeSun, 17 Dec 2017 19:40:31 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/AzhNewTolbA/a-nonparametric-copula-approach-to-conditional-valueatrisk-arxiv171205527v1-statme
<![CDATA[A nonparametric copula approach to conditional Value-at-Risk. (arXiv:1712.05527v1 [stat.ME])]]>Value-at-Risk and its conditional allegory, which takes into account the
available information about the economic environment, form the centrepiece of
the Basel framework for the evaluation of market risk in the banking sector. In
this paper, a new nonparametric framework for estimating this conditional
Value-at-Risk is presented. A nonparametric approach is particularly pertinent
as the traditionally used parametric distributions have been shown to be
insufficiently robust and flexible in most of the equity-return data sets
observed in practice. The method extracts the quantile of the conditional
distribution of interest, whose estimation is based on a novel estimator of the
density of the copula describing the dynamic dependence observed in the series
of returns. Real-world back-testing analyses demonstrate the potential of the
approach, whose performance may be superior to its industry counterparts.
]]>795510http://www.moneyscience.com/pg/blog/arXiv/read/795510/a-nonparametric-copula-approach-to-conditional-valueatrisk-arxiv171205527v1-statmehttp://www.moneyscience.com/pg/blog/arXiv/read/795509/risk-sensitive-portfolio-optimization-with-regimeswitching-arxiv171205676v1-qfinpmSun, 17 Dec 2017 19:39:28 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/nPlu4du66IA/risk-sensitive-portfolio-optimization-with-regimeswitching-arxiv171205676v1-qfinpm
<![CDATA[Risk Sensitive Portfolio Optimization with Regime-Switching. (arXiv:1712.05676v1 [q-fin.PM])]]>We study a risk sensitive portfolio optimization problem in a
regime-switching credit market with default contagion. The state space of the
Markovian regime-switching process is assumed to be a countably infinite set.
To characterize the value function of the risk sensitive stochastic control
problem, we investigate the corresponding recursive infinite-dimensional
nonlinear dynamical programming equations (DPEs) based on default states. We
propose to work in the following procedure: Applying the theory of the monotone
dynamical system, we first establish the existence and uniqueness of classical
solutions to the recursive DPEs by a truncation argument in the finite state
space. Moreover, the associated optimal feedback strategy is characterized by
developing a rigorous verification theorem. Building upon results in the first
stage, we construct a sequence of approximating risk sensitive control problems
with finite state space and prove that the resulting smooth value functions
will converge to the classical solution of the original system of DPEs. The
construction and approximation of the optimal feedback strategy for the
original problem are also thoroughly discussed using verification arguments.
]]>795509http://www.moneyscience.com/pg/blog/arXiv/read/795509/risk-sensitive-portfolio-optimization-with-regimeswitching-arxiv171205676v1-qfinpmhttp://www.moneyscience.com/pg/blog/JournalofFinance/read/795460/financial-literacy-and-portfolio-dynamicsSat, 16 Dec 2017 22:58:20 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/fQVv8ow_Sms/financial-literacy-and-portfolio-dynamics
<![CDATA[Financial Literacy and Portfolio Dynamics]]>

ABSTRACT

We match administrative panel data on portfolio choices with survey measures of financial literacy. When we control for portfolio risk, the most literate households experience 0.4% higher annual returns than the least literate households. Distinct portfolio dynamics are the key determinant of this difference. More literate households hold riskier positions when expected returns are higher, they more actively rebalance their portfolios and do so in a way that holds their risk exposure relatively constant over time, and they are more likely to buy assets that provide higher returns than the assets that they sell.

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795460http://www.moneyscience.com/pg/blog/JournalofFinance/read/795460/financial-literacy-and-portfolio-dynamicshttp://www.moneyscience.com/pg/blog/JournalofFinance/read/795458/option-mispricing-around-nontrading-periodsSat, 16 Dec 2017 15:47:01 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/YM58n7uSIkM/option-mispricing-around-nontrading-periods
<![CDATA[Option Mispricing Around Nontrading Periods]]>

ABSTRACT

We find that option returns are significantly lower over nontrading periods, the vast majority of which are weekends. Our evidence suggests that nontrading returns cannot be explained by risk, but rather are the result of widespread and highly persistent option mispricing driven by the incorrect treatment of stock return variance during periods of market closure. The size of the effect implies that the broad spectrum of finance research involving option prices should account for nontrading effects. Our study further suggests how alternative industry practices could improve the efficiency of option markets in a meaningful way.

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795458http://www.moneyscience.com/pg/blog/JournalofFinance/read/795458/option-mispricing-around-nontrading-periodshttp://www.moneyscience.com/pg/blog/JournalofFinance/read/795080/shortselling-riskFri, 15 Dec 2017 05:28:45 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/b70QFgU1E54/shortselling-risk
<![CDATA[ShortâSelling Risk]]>

ABSTRACT

Short sellers face unique risks, such as the risk that stock loans become expensive and the risk that stock loans are recalled. We show that shortâselling risk affects prices among the crossâsection of stocks. Stocks with more shortâselling risk have lower returns, less price efficiency, and less short selling.

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795080http://www.moneyscience.com/pg/blog/JournalofFinance/read/795080/shortselling-riskhttp://www.moneyscience.com/pg/blog/JournalofFinance/read/795079/institutional-and-legal-context-in-natural-experiments-the-case-of-state-antitakeover-lawsFri, 15 Dec 2017 05:27:03 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/0pvgUQMSP20/institutional-and-legal-context-in-natural-experiments-the-case-of-state-antitakeover-laws
<![CDATA[Institutional and Legal Context in Natural Experiments: The Case of State Antitakeover Laws]]>

ABSTRACT

We argue and demonstrate empirically that a firm's institutional and legal context has firstâorder effects in tests that use state antitakeover laws for identification. A priori, the size and direction of a law's effect on a firm's takeover protection depends on (i) other state antitakeover laws, (ii) preexisting firmâlevel takeover defenses, and (iii) the legal regime as reflected by important court decisions. In addition, (iv) state antitakeover laws are not exogenous for many easily identifiable firms. We show that the inferences from nine prior studies related to nine different outcome variables change substantially when we include controls for these considerations.

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795079http://www.moneyscience.com/pg/blog/JournalofFinance/read/795079/institutional-and-legal-context-in-natural-experiments-the-case-of-state-antitakeover-lawshttp://www.moneyscience.com/pg/blog/arXiv/read/795076/series-representation-of-the-pricing-formula-for-the-european-option-driven-by-spacetime-fractional-diffusion-arxiv171204990v1-qfinmfThu, 14 Dec 2017 19:56:00 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/0WkTu2_z8bM/series-representation-of-the-pricing-formula-for-the-european-option-driven-by-spacetime-fractional-diffusion-arxiv171204990v1-qfinmf
<![CDATA[Series representation of the pricing formula for the European option driven by space-time fractional diffusion. (arXiv:1712.04990v1 [q-fin.MF])]]>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 functions.
]]>795076http://www.moneyscience.com/pg/blog/arXiv/read/795076/series-representation-of-the-pricing-formula-for-the-european-option-driven-by-spacetime-fractional-diffusion-arxiv171204990v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/795075/the-mathematics-of-market-timing-arxiv171205031v1-qfinpmThu, 14 Dec 2017 19:54:56 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/vNTWAWCJWXw/the-mathematics-of-market-timing-arxiv171205031v1-qfinpm
<![CDATA[The Mathematics of Market Timing. (arXiv:1712.05031v1 [q-fin.PM])]]>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 optimal timing path of switches is shown
to be indistinguishable from a random sequence. The key result is that the
probability distribution function of market timing returns is asymetric, that
the highest probability outcome for market timing is a below median return. Put
another way, simple math says market timing is more likely to lose than to
win---even before accounting for costs. The median of the market timing return
probability distribution can be directly calculated as a weighted average of
the returns of the model assets with the weights given by the fraction of time
each asset has a higher return than the other. For the time period of the data
the median return was close to, but not identical with, the return of a static
60:40 stock:bond portfolio. These results are illustrated through Monte Carlo
sampling of timing paths within the feasible set and by the observed return
paths of several market timing mutual funds.
]]>795075http://www.moneyscience.com/pg/blog/arXiv/read/795075/the-mathematics-of-market-timing-arxiv171205031v1-qfinpmhttp://www.moneyscience.com/pg/blog/arXiv/read/795074/the-consentaneous-model-of-the-financial-markets-exhibiting-spurious-nature-of-longrange-memory-arxiv171205121v1-qfinstThu, 14 Dec 2017 19:53:53 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/TJ5PNFqZKaY/the-consentaneous-model-of-the-financial-markets-exhibiting-spurious-nature-of-longrange-memory-arxiv171205121v1-qfinst
<![CDATA[The consentaneous model of the financial markets exhibiting spurious nature of long-range memory. (arXiv:1712.05121v1 [q-fin.ST])]]>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 qualitatively different from models built using
fractional Brownian motion. In this contribution we investigate burst and
inter-burst duration statistics of volatility in the financial markets
employing the consentaneous model. Our analysis provides an evidence that
empirical statistical properties of burst and inter-burst duration can be
explained by non-linear stochastic differential equations driving the
volatility in the financial markets. This serves as an strong argument that
long-range memory in finance can have spurious nature.
]]>795074http://www.moneyscience.com/pg/blog/arXiv/read/795074/the-consentaneous-model-of-the-financial-markets-exhibiting-spurious-nature-of-longrange-memory-arxiv171205121v1-qfinsthttp://www.moneyscience.com/pg/blog/arXiv/read/795073/the-evaluation-of-geometric-asian-power-options-under-time-changed-mixed-fractional-brownian-motion-arxiv171205254v1-qfinprThu, 14 Dec 2017 19:52:50 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/Y36A3Vo9s9Q/the-evaluation-of-geometric-asian-power-options-under-time-changed-mixed-fractional-brownian-motion-arxiv171205254v1-qfinpr
<![CDATA[The evaluation of geometric Asian power options under time changed mixed fractional Brownian motion. (arXiv:1712.05254v1 [q-fin.PR])]]>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.
]]>795073http://www.moneyscience.com/pg/blog/arXiv/read/795073/the-evaluation-of-geometric-asian-power-options-under-time-changed-mixed-fractional-brownian-motion-arxiv171205254v1-qfinprhttp://www.moneyscience.com/pg/blog/arXiv/read/794979/qlbs-qlearner-in-the-blackscholesmerton-worlds-arxiv171204609v1-qfincpWed, 13 Dec 2017 19:56:00 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/-aJ01lC4Bno/qlbs-qlearner-in-the-blackscholesmerton-worlds-arxiv171204609v1-qfincp
<![CDATA[QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds. (arXiv:1712.04609v1 [q-fin.CP])]]>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. Using Q-Learning and related methods,
once created in a parametric setting, the model is able to go model-free and
learn to price and hedge an option directly from data generated from a dynamic
replicating portfolio which is rebalanced at discrete times. If the world is
according to BSM, our risk-averse Q-Learner converges, given enough training
data, to the true BSM price and hedge ratio of the option in the continuous
time limit, even if hedges applied at the stage of data generation are
completely random (i.e. it can learn the BSM model itself, too!), because
Q-Learning is an off-policy algorithm. If the world is different from a BSM
world, the Q-Learner will find it out as well, because Q-Learning is a
model-free algorithm. For finite time steps, the Q-Learner is able to
efficiently calculate both the optimal hedge and optimal price for the option
directly from trading data, and without an explicit model of the world. This
suggests that RL may provide efficient data-driven and model-free methods for
optimal pricing and hedging of options, once we depart from the academic
continuous-time limit, and vice versa, option pricing methods developed in
Mathematical Finance may be viewed as special cases of model-based
Reinforcement Learning. Our model only needs basic linear algebra (plus Monte
Carlo simulation, if we work with synthetic data).
]]>794979http://www.moneyscience.com/pg/blog/arXiv/read/794979/qlbs-qlearner-in-the-blackscholesmerton-worlds-arxiv171204609v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/794978/inverse-reinforcement-learning-for-marketing-arxiv171204612v1-qfincpWed, 13 Dec 2017 19:54:55 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/FjO_9Y7F7KM/inverse-reinforcement-learning-for-marketing-arxiv171204612v1-qfincp
<![CDATA[Inverse Reinforcement Learning for Marketing. (arXiv:1712.04612v1 [q-fin.CP])]]>Learning customer preferences from an observed behaviour is an important
topic in the marketing literature. Structural models typically model
forward-looking customers or firms as utility-maximizing agents whose utility
is estimated using methods of Stochastic Optimal Control. We suggest an
alternative approach to study dynamic consumer demand, based on Inverse
Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL
that leads to a highly tractable model formulation that amounts to
low-dimensional convex optimization in the search for optimal model parameters.
Using simulations of consumer demand, we show that observational noise for
identical customers can be easily confused with an apparent consumer
heterogeneity.
]]>794978http://www.moneyscience.com/pg/blog/arXiv/read/794978/inverse-reinforcement-learning-for-marketing-arxiv171204612v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/794977/optimal-stochastic-desencoring-and-applications-to-calibration-of-market-models-arxiv171204844v1-qfincpWed, 13 Dec 2017 19:53:52 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/-d-IAYHIgmU/optimal-stochastic-desencoring-and-applications-to-calibration-of-market-models-arxiv171204844v1-qfincp
<![CDATA[Optimal Stochastic Desencoring and Applications to Calibration of Market Models. (arXiv:1712.04844v1 [q-fin.CP])]]>Typically flat filling, linear or polynomial interpolation methods to
generate missing historical data. We introduce a novel optimal method for
recreating data generated by a diffusion process. The results are then applied
to recreate historical data for stocks.
]]>794977http://www.moneyscience.com/pg/blog/arXiv/read/794977/optimal-stochastic-desencoring-and-applications-to-calibration-of-market-models-arxiv171204844v1-qfincphttp://www.moneyscience.com/pg/blog/arXiv/read/794976/stock-market-as-temporal-network-arxiv171204863v1-qfinstWed, 13 Dec 2017 19:52:47 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/TZUcUSGOnYM/stock-market-as-temporal-network-arxiv171204863v1-qfinst
<![CDATA[Stock market as temporal network. (arXiv:1712.04863v1 [q-fin.ST])]]>Financial networks have become extremely useful in characterizing the
structure of complex financial systems. Meanwhile, the time evolution property
of the stock markets can be described by temporal networks. We utilize the
temporal network framework to characterize the time-evolving correlation-based
networks of stock markets. The market instability can be detected by the
evolution of the topology structure of the financial networks. We employ the
temporal centrality as a portfolio selection tool. Those portfolios, which are
composed of peripheral stocks with low temporal centrality scores, have
consistently better performance under different portfolio optimization schemes,
suggesting that the temporal centrality measure can be used as new portfolio
optimization and risk management tools. Our results reveal the importance of
the temporal attributes of the stock markets, which should be taken serious
consideration in real life applications.
]]>794976http://www.moneyscience.com/pg/blog/arXiv/read/794976/stock-market-as-temporal-network-arxiv171204863v1-qfinsthttp://www.moneyscience.com/pg/blog/arXiv/read/794944/the-calculus-of-democratization-and-development-arxiv171204117v1-econemTue, 12 Dec 2017 20:01:25 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/TWQv3YZCL8w/the-calculus-of-democratization-and-development-arxiv171204117v1-econem
<![CDATA[The Calculus of Democratization and Development. (arXiv:1712.04117v1 [econ.EM])]]>In accordance with "Democracy's Effect on Development: More Questions than
Answers", we seek to carry out a study in following the description in the
'Questions for Further Study.' To that end, we studied 33 countries in the
Sub-Saharan Africa region, who all went through an election which should signal
a "step-up" for their democracy, one in which previously homogenous regimes
transfer power to an opposition party that fairly won the election. After doing
so, liberal-democracy indicators and democracy indicators were evaluated in the
five years prior to and after the election took place, and over that ten-year
period, we examine the data for trends. If we see positive or negative trends
over this time horizon, we are able to conclude that it was the recent increase
in the quality of their democracy which led to it. Having investigated examples
of this in depth, there seem to be three main archetypes which drive the
results. Countries with positive results to their democracy from the election
have generally positive effects on their development, countries with more
"plateau" like results also did well, but countries for whom the descent to
authoritarianism was continued by this election found more negative results.
]]>794944http://www.moneyscience.com/pg/blog/arXiv/read/794944/the-calculus-of-democratization-and-development-arxiv171204117v1-econemhttp://www.moneyscience.com/pg/blog/arXiv/read/794943/fair-valuation-of-levytype-drawdowndrawup-contracts-with-general-insured-and-penalty-functions-arxiv171204418v1-qfinprTue, 12 Dec 2017 20:00:21 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/x2HqPZraNkk/fair-valuation-of-levytype-drawdowndrawup-contracts-with-general-insured-and-penalty-functions-arxiv171204418v1-qfinpr
<![CDATA[Fair valuation of L\'evy-type drawdown-drawup contracts with general insured and penalty functions. (arXiv:1712.04418v1 [q-fin.PR])]]>In this paper, we analyse some equity-linked contracts that are related to
drawdown and drawup events based on assets governed by a geometric spectrally
negative L\'evy process. Drawdown and drawup refer to the differences between
the historical maximum and minimum of the asset price and its current value,
respectively. We consider four contracts. In the first contract, a protection
buyer pays a premium with a constant intensity $p$ until the drawdown of fixed
size occurs. In return, he/she receives a certain insured amount at the
drawdown epoch, which depends on the drawdown level at that moment. Next, the
insurance contract may expire earlier if a certain fixed drawup event occurs
prior to the fixed drawdown. The last two contracts are extensions of the
previous ones but with an additional cancellable feature that allows the
investor to terminate the contracts earlier. In these cases, a fee for early
stopping depends on the drawdown level at the stopping epoch. In this work, we
focus on two problems: calculating the fair premium $p$ for basic contracts and
finding the optimal stopping rule for the polices with a cancellable feature.
To do this, we use a fluctuation theory of L\'evy processes and rely on a
theory of optimal stopping.
]]>794943http://www.moneyscience.com/pg/blog/arXiv/read/794943/fair-valuation-of-levytype-drawdowndrawup-contracts-with-general-insured-and-penalty-functions-arxiv171204418v1-qfinprhttp://www.moneyscience.com/pg/blog/arXiv/read/794867/enhancing-binomial-and-trinomial-equity-option-pricing-models-arxiv171203566v1-qfinmfMon, 11 Dec 2017 20:11:03 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/e4ds9K_k8XQ/enhancing-binomial-and-trinomial-equity-option-pricing-models-arxiv171203566v1-qfinmf
<![CDATA[Enhancing Binomial and Trinomial Equity Option Pricing Models. (arXiv:1712.03566v1 [q-fin.MF])]]>We extend the classical Cox-Ross-Rubinstein binomial model in two ways. We
first develop a binomial model with time-dependent parameters that equate all
moments of the pricing tree increments with the corresponding moments of the
increments of the limiting It\^o price process. Second, we introduce a new
trinomial model in the natural (historical) world, again fitting all moments of
the pricing tree increments to the corresponding geometric Brownian motion. We
introduce the risk-neutral trinomial tree and derive a hedging strategy based
on an additional perpetual derivative used as a second asset for hedging in any
node of the trinomial pricing tree.
]]>794867http://www.moneyscience.com/pg/blog/arXiv/read/794867/enhancing-binomial-and-trinomial-equity-option-pricing-models-arxiv171203566v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/794866/revisiting-the-determinacy-on-new-keynesian-models-arxiv171203681v1-qfinecMon, 11 Dec 2017 20:09:58 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/TQ_VXBlBUpo/revisiting-the-determinacy-on-new-keynesian-models-arxiv171203681v1-qfinec
<![CDATA[Revisiting the determinacy on New Keynesian Models. (arXiv:1712.03681v1 [q-fin.EC])]]>The goal of this paper is to shed light on the determinacy question that
arises in New Keynesian models as result of a combination of several monetary
policy rules; in these models, we provide conditions to guarantee existence and
uniqueness of equilibrium by means of results that are obtained from
theoretical analysis. In particular, we show that Taylor--like rules in
interest rate setting are not the only way to reach determinacy of the rational
expectations equilibrium in the New Keynesian setting. The key technical tool
that we use for that purposes is the so--called Budan--Fourier Theorem, that we
review along the paper.
]]>794866http://www.moneyscience.com/pg/blog/arXiv/read/794866/revisiting-the-determinacy-on-new-keynesian-models-arxiv171203681v1-qfinechttp://www.moneyscience.com/pg/blog/arXiv/read/794787/remarks-on-bayesian-control-charts-arxiv171202860v1-statapSun, 10 Dec 2017 19:42:57 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/5MahYg8DQdU/remarks-on-bayesian-control-charts-arxiv171202860v1-statap
<![CDATA[Remarks on Bayesian Control Charts. (arXiv:1712.02860v1 [stat.AP])]]>There is a considerable amount of ongoing research on the use of Bayesian
control charts for detecting a shift from a good quality distribution to a bad
quality distribution in univariate and multivariate processes. Monitoring
continuous-time multivariate processes by using Bayesian control charts is
studied in Makis (2008) [Makis, V. (2008). Multivariate Bayesian control chart.
Operations Research, 56(2), 487-496, DOI: 10.1287/opre.1070.0495]. Makis (2008)
and some other authors widely claimed that Bayesian control charts were
economically optimal, compared to non-Bayesian control charts. This paper first
shows that the Bayesian control charts considered by Makis (2008) are not
always better than non-Bayesian control charts. Secondly, it demonstrates that
the algorithm presented in Makis (2008) to determine the optimal control limits
of Bayesian control charts fails to find the true optimal values.
]]>794787http://www.moneyscience.com/pg/blog/arXiv/read/794787/remarks-on-bayesian-control-charts-arxiv171202860v1-stataphttp://www.moneyscience.com/pg/blog/arXiv/read/794786/mixed-models-as-an-alternative-to-farima-arxiv171203044v1-qfinmfSun, 10 Dec 2017 19:41:53 -0600
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/UBBSEyPqwPQ/mixed-models-as-an-alternative-to-farima-arxiv171203044v1-qfinmf
<![CDATA[Mixed Models as an Alternative to Farima. (arXiv:1712.03044v1 [q-fin.MF])]]>We construct a new process using a fractional Brownian motion and a
fractional Ornstein-Uhlenbeck process of the Second Kind as building blocks. We
consider the increments of the new process in discrete time and, as a result,
we obtain a more parsimonious process with similar autocovariance structure to
that of a FARIMA. In practice, variance of the new increment process is a
closed-form expression easier to compute than that of FARIMA.
]]>794786http://www.moneyscience.com/pg/blog/arXiv/read/794786/mixed-models-as-an-alternative-to-farima-arxiv171203044v1-qfinmf