<![CDATA[MoneyScience: Research]]>
http://www.moneyscience.com/pg/blog-directory/research?view=rss
FinancialResearchFocushttps://feedburner.google.comhttp://www.moneyscience.com/pg/blog/arXiv/read/803658/are-bitcoin-bubbles-predictable-combining-a-generalized-metcalfes-law-and-the-lppls-model-arxiv180305663v1-econemThu, 15 Mar 2018 19:51:47 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/O0oSLdt20kI/are-bitcoin-bubbles-predictable-combining-a-generalized-metcalfes-law-and-the-lppls-model-arxiv180305663v1-econem
<![CDATA[Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model. (arXiv:1803.05663v1 [econ.EM])]]>We develop a strong diagnostic for bubbles and crashes in bitcoin, by
analyzing the coincidence (and its absence) of fundamental and technical
indicators. Using a generalized Metcalfe's law based on network properties, a
fundamental value is quantified and shown to be heavily exceeded, on at least
four occasions, by bubbles that grow and burst. In these bubbles, we detect a
universal super-exponential unsustainable growth. We model this universal
pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which
parsimoniously captures diverse positive feedback phenomena, such as herding
and imitation. The LPPLS model is shown to provide an ex-ante warning of market
instabilities, quantifying a high crash hazard and probabilistic bracket of the
crash time consistent with the actual corrections; although, as always, the
precise time and trigger (which straw breaks the camel's back) being exogenous
and unpredictable. Looking forward, our analysis identifies a substantial but
not unprecedented overvaluation in the price of bitcoin, suggesting many months
of volatile sideways bitcoin prices ahead (from the time of writing, March
2018).
]]>803658http://www.moneyscience.com/pg/blog/arXiv/read/803658/are-bitcoin-bubbles-predictable-combining-a-generalized-metcalfes-law-and-the-lppls-model-arxiv180305663v1-econemhttp://www.moneyscience.com/pg/blog/arXiv/read/803657/optimal-liquiditybased-trading-tactics-arxiv180305690v1-qfintrThu, 15 Mar 2018 19:50:40 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/l9Xf1uHMel4/optimal-liquiditybased-trading-tactics-arxiv180305690v1-qfintr
<![CDATA[Optimal liquidity-based trading tactics. (arXiv:1803.05690v1 [q-fin.TR])]]>We consider an agent who needs to buy (or sell) a relatively small amount of
asset over some fixed short time interval. We work at the highest frequency
meaning that we wish to find the optimal tactic to execute our quantity using
limit orders, market orders and cancellations. To solve the agent's control
problem, we build an order book model and optimize an expected utility function
based on our price impact. We derive the equations satisfied by the optimal
strategy and solve them numerically. Moreover, we show that our optimal tactic
enables us to outperform significantly naive execution strategies.
]]>803657http://www.moneyscience.com/pg/blog/arXiv/read/803657/optimal-liquiditybased-trading-tactics-arxiv180305690v1-qfintrhttp://www.moneyscience.com/pg/blog/arXiv/read/803656/outperformance-and-tracking-dynamic-asset-allocation-for-active-and-passive-portfolio-management-arxiv180305819v1-qfinpmThu, 15 Mar 2018 19:49:36 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/ZGycB6ijT_I/outperformance-and-tracking-dynamic-asset-allocation-for-active-and-passive-portfolio-management-arxiv180305819v1-qfinpm
<![CDATA[Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management. (arXiv:1803.05819v1 [q-fin.PM])]]>Portfolio management problems are often divided into two types: active and
passive, where the objective is to outperform and track a preselected
benchmark, respectively. Here, we formulate and solve a dynamic asset
allocation problem that combines these two objectives in a unified framework.
We look to maximize the expected growth rate differential between the wealth of
the investor's portfolio and that of a performance benchmark while penalizing
risk-weighted deviations from a given tracking portfolio. Using stochastic
control techniques, we provide explicit closed-form expressions for the optimal
allocation and we show how the optimal strategy can be related to the growth
optimal portfolio. The admissible benchmarks encompass the class of
functionally generated portfolios (FGPs), which include the market portfolio,
as the only requirement is that they depend only on the prevailing asset
values. The passive component of the problem allows the investor to leverage
the relative arbitrage properties of certain FGPs and achieve outperformance in
a risk-adjusted sense without requiring the difficult task of estimating of
asset growth rates. Finally, some numerical experiments are presented to
illustrate the risk-reward profile of the optimal allocation.
]]>803656http://www.moneyscience.com/pg/blog/arXiv/read/803656/outperformance-and-tracking-dynamic-asset-allocation-for-active-and-passive-portfolio-management-arxiv180305819v1-qfinpmhttp://www.moneyscience.com/pg/blog/arXiv/read/803655/technical-uncertainty-in-real-options-with-learning-arxiv180305831v1-qfinmfThu, 15 Mar 2018 19:47:24 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/f_CVGjtErUs/technical-uncertainty-in-real-options-with-learning-arxiv180305831v1-qfinmf
<![CDATA[Technical Uncertainty in Real Options with Learning. (arXiv:1803.05831v1 [q-fin.MF])]]>We introduce a new approach to incorporate uncertainty into the decision to
invest in a commodity reserve. The investment is an irreversible one-off
capital expenditure, after which the investor receives a stream of cashflow
from extracting the commodity and selling it on the spot market. The investor
is exposed to price uncertainty and uncertainty in the amount of available
resources in the reserves (i.e. technical uncertainty). She does, however,
learn about the reserve levels through time, which is a key determinant in the
decision to invest. To model the reserve level uncertainty and how she learns
about the estimates of the commodity in the reserve, we adopt a continuous-time
Markov chain model to value the option to invest in the reserve and investigate
the value that learning has prior to investment.
]]>803655http://www.moneyscience.com/pg/blog/arXiv/read/803655/technical-uncertainty-in-real-options-with-learning-arxiv180305831v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/803654/practical-volume-computation-of-structured-convex-bodies-and-an-application-to-modeling-portfolio-dependencies-and-financial-crises-arxiv180305861v1-cscgThu, 15 Mar 2018 19:46:21 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/KliNQZvQDtc/practical-volume-computation-of-structured-convex-bodies-and-an-application-to-modeling-portfolio-dependencies-and-financial-crises-arxiv180305861v1-cscg
<![CDATA[Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises. (arXiv:1803.05861v1 [cs.CG])]]>We examine volume computation of general-dimensional polytopes and more
general convex bodies, defined as the intersection of a simplex by a family of
parallel hyperplanes, and another family of parallel hyperplanes or a family of
concentric ellipsoids. Such convex bodies appear in modeling and predicting
financial crises. The impact of crises on the economy (labor, income, etc.)
makes its detection of prime interest. Certain features of dependencies in the
markets clearly identify times of turmoil. We describe the relationship between
asset characteristics by means of a copula; each characteristic is either a
linear or quadratic form of the portfolio components, hence the copula can be
constructed by computing volumes of convex bodies. We design and implement
practical algorithms in the exact and approximate setting, we experimentally
juxtapose them and study the tradeoff of exactness and accuracy for speed. We
analyze the following methods in order of increasing generality: rejection
sampling relying on uniformly sampling the simplex, which is the fastest
approach, but inaccurate for small volumes; exact formulae based on the
computation of integrals of probability distribution functions; an optimized
Lawrence sign decomposition method, since the polytopes at hand are shown to be
simple; Markov chain Monte Carlo algorithms using random walks based on the
hit-and-run paradigm generalized to nonlinear convex bodies and relying on new
methods for computing a ball enclosed; the latter is experimentally extended to
non-convex bodies with very encouraging results. Our C++ software, based on
CGAL and Eigen and available on github, is shown to be very effective in up to
100 dimensions. Our results offer novel, effective means of computing portfolio
dependencies and an indicator of financial crises, which is shown to correctly
identify past crises.
]]>803654http://www.moneyscience.com/pg/blog/arXiv/read/803654/practical-volume-computation-of-structured-convex-bodies-and-an-application-to-modeling-portfolio-dependencies-and-financial-crises-arxiv180305861v1-cscghttp://www.moneyscience.com/pg/blog/MathematicalFinance/read/803524/issue-informationWed, 14 Mar 2018 23:11:35 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/aKKmeOHvxzU/issue-information
<![CDATA[Issue Information]]>]]>803524http://www.moneyscience.com/pg/blog/MathematicalFinance/read/803524/issue-informationhttp://www.moneyscience.com/pg/blog/arXiv/read/803517/an-endogenous-mechanism-of-business-cycles-arxiv180305002v1-qfingnWed, 14 Mar 2018 19:44:39 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/awM5T_12A-g/an-endogenous-mechanism-of-business-cycles-arxiv180305002v1-qfingn
<![CDATA[An Endogenous Mechanism of Business Cycles. (arXiv:1803.05002v1 [q-fin.GN])]]>This paper suggests that business cycles may be a manifestation of coupled
economy-market dynamics and describes a mechanism that can generate economic
fluctuations consistent with observed business cycles. To this end, we seek to
incorporate into the macroeconomic framework a dynamic stock market model based
on opinion interactions (Gusev et al., 2015). We derive this model from
microfoundations, provide its empirical verification including market return
prediction (backtested and live-track), demonstrate that it contains the
efficient market as a particular regime and establish a link through which
economic models can be attached for the study of economy-market interaction. To
examine the key effects, we link this model with a simple economic model
(Blanchard, 1981). The coupled system generates nontrivial endogenous dynamics,
which exhibit deterministic and stochastic features, producing quasiperiodic
fluctuations (business cycles). We also inspect this system's behavior in the
phase space. The economy and the market are shown to coevolve dynamically along
the path governed by a stochastically-forced dynamical system with two stable
equilibria, one where the economy expands and the other where it contracts,
resulting in business cycles identified as the coherence resonance phenomenon.
Thus, the incorporation of market dynamics into the macroeconomic framework, as
presented here, allows the derivation of realistic behaviors in a tractable
setting, and so could enhance models applied for policy analysis.
]]>803517http://www.moneyscience.com/pg/blog/arXiv/read/803517/an-endogenous-mechanism-of-business-cycles-arxiv180305002v1-qfingnhttp://www.moneyscience.com/pg/blog/arXiv/read/803516/stock-price-prediction-using-principle-components-arxiv180305075v1-qfinmfWed, 14 Mar 2018 19:43:35 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/7PrHzl9KwgM/stock-price-prediction-using-principle-components-arxiv180305075v1-qfinmf
<![CDATA[Stock Price Prediction using Principle Components. (arXiv:1803.05075v1 [q-fin.MF])]]>The literature provides strong evidence that stock prices can be predicted
from past price data. Principal component analysis (PCA) is a widely used
mathematical technique for dimensionality reduction and analysis of data by
identifying a small number of principal components to explain the variation
found in a data set. In this paper, we describe a general method for stock
price prediction using covariance information, in terms of a dimension
reduction operation based on principle component analysis. Projecting the noisy
observation onto a principle subspace leads to a well-conditioned problem. We
illustrate our method on daily stock price values for five companies in
different industries. We investigate the results based on mean squared error
and directional change statistic of prediction, as measures of performance, and
volatility of prediction as a measure of risk.
]]>803516http://www.moneyscience.com/pg/blog/arXiv/read/803516/stock-price-prediction-using-principle-components-arxiv180305075v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/803515/stochastic-dynamic-utilities-and-intertemporal-preferences-arxiv180305244v1-mathprWed, 14 Mar 2018 19:42:31 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/C0X32JjLOkM/stochastic-dynamic-utilities-and-intertemporal-preferences-arxiv180305244v1-mathpr
<![CDATA[Stochastic Dynamic Utilities and Inter-Temporal Preferences. (arXiv:1803.05244v1 [math.PR])]]>We propose an axiomatic approach which economically underpins the
representation of dynamic preferences in terms of a stochastic utility
function, sensitive to the information available to the decision maker. Our
construction is recursive and based on inter-temporal preference relations,
whose characterization is inpired by the original intuition given by Debreu's
State Dependent Utilities (1960).
]]>803515http://www.moneyscience.com/pg/blog/arXiv/read/803515/stochastic-dynamic-utilities-and-intertemporal-preferences-arxiv180305244v1-mathprhttp://www.moneyscience.com/pg/blog/arXiv/read/803400/pathwise-moderate-deviations-for-option-pricing-arxiv180304483v1-qfinmfTue, 13 Mar 2018 20:04:17 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/Ej0BXeh9ckM/pathwise-moderate-deviations-for-option-pricing-arxiv180304483v1-qfinmf
<![CDATA[Pathwise moderate deviations for option pricing. (arXiv:1803.04483v1 [q-fin.MF])]]>We provide a unifying treatment of pathwise moderate deviations for models
commonly used in financial applications, and for related integrated
functionals. Suitable scaling allows us to transfer these results into
small-time, large-time and tail asymptotics for diffusions, as well as for
option prices and realised variances. In passing, we highlight some intuitive
relationships between moderate deviations rate functions and their large
deviations counterparts; these turn out to be useful for numerical purposes, as
large deviations rate functions are often difficult to compute.
]]>803400http://www.moneyscience.com/pg/blog/arXiv/read/803400/pathwise-moderate-deviations-for-option-pricing-arxiv180304483v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/803399/minimising-the-expectation-value-of-the-procurement-cost-in-electricity-markets-based-on-the-prediction-error-of-energy-consumption-arxiv180304532v1-qfinecTue, 13 Mar 2018 20:03:14 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/_1eZNh-kITw/minimising-the-expectation-value-of-the-procurement-cost-in-electricity-markets-based-on-the-prediction-error-of-energy-consumption-arxiv180304532v1-qfinec
<![CDATA[Minimising the expectation value of the procurement cost in electricity markets based on the prediction error of energy consumption. (arXiv:1803.04532v1 [q-fin.EC])]]>In this paper, we formulate a method for minimising the expectation value of
the procurement cost of electricity in two popular spot markets: {\it
day-ahead} and {\it intra-day}, under the assumption that expectation value of
unit prices and the distributions of prediction errors for the electricity
demand traded in two markets are known. The expectation value of the total
electricity cost is minimised over two parameters that change the amounts of
electricity. Two parameters depend only on the expected unit prices of
electricity and the distributions of prediction errors for the electricity
demand traded in two markets. That is, even if we do not know the predictions
for the electricity demand, we can determine the values of two parameters that
minimise the expectation value of the procurement cost of electricity in two
popular spot markets. We demonstrate numerically that the estimate of two
parameters often results in a small variance of the total electricity cost, and
illustrate the usefulness of the proposed procurement method through the
analysis of actual data.
]]>803399http://www.moneyscience.com/pg/blog/arXiv/read/803399/minimising-the-expectation-value-of-the-procurement-cost-in-electricity-markets-based-on-the-prediction-error-of-energy-consumption-arxiv180304532v1-qfinechttp://www.moneyscience.com/pg/blog/arXiv/read/803398/categorizing-variants-of-goodharts-law-arxiv180304585v1-csaiTue, 13 Mar 2018 20:02:10 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/C_qwIQ3HmX0/categorizing-variants-of-goodharts-law-arxiv180304585v1-csai
<![CDATA[Categorizing Variants of Goodhart's Law. (arXiv:1803.04585v1 [cs.AI])]]>There are several distinct failure modes for overoptimization of systems on
the basis of metrics. This occurs when a metric which can be used to improve a
system is used to an extent that further optimization is ineffective or
harmful, and is sometimes termed Goodhart's Law. This class of failure is often
poorly understood, partly because terminology for discussing them is ambiguous,
and partly because discussion using this ambiguous terminology ignores
distinctions between different failure modes of this general type. This paper
expands on an earlier discussion by Garrabrant, which notes there are "(at
least) four different mechanisms" that relate to Goodhart's Law. This paper is
intended to explore these mechanisms further, and specify more clearly how they
occur. This discussion should be helpful in better understanding these types of
failures in economic regulation, in public policy, in machine learning, and in
Artificial Intelligence alignment. The importance of Goodhart effects depends
on the amount of power directed towards optimizing the proxy, and so the
increased optimization power offered by artificial intelligence makes it
especially critical for that field.
]]>803398http://www.moneyscience.com/pg/blog/arXiv/read/803398/categorizing-variants-of-goodharts-law-arxiv180304585v1-csaihttp://www.moneyscience.com/pg/blog/arXiv/read/803397/a-generalization-of-the-robust-positive-expectation-theorem-for-stock-trading-via-feedback-control-arxiv180304591v1-qfinstTue, 13 Mar 2018 20:01:06 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/fOsoV5UTn_Y/a-generalization-of-the-robust-positive-expectation-theorem-for-stock-trading-via-feedback-control-arxiv180304591v1-qfinst
<![CDATA[A Generalization of the Robust Positive Expectation Theorem for Stock Trading via Feedback Control. (arXiv:1803.04591v1 [q-fin.ST])]]>The starting point of this paper is the so-called Robust Positive Expectation
(RPE) Theorem, a result which appears in literature in the context of
Simultaneous Long-Short stock trading. This theorem states that using a
combination of two specially-constructed linear feedback trading controllers,
one long and one short, the expected value of the resulting gain-loss function
is guaranteed to be robustly positive with respect to a large class of
stochastic processes for the stock price. The main result of this paper is a
generalization of this theorem. Whereas previous work applies to a single
stock, in this paper, we consider a pair of stocks. To this end, we make two
assumptions on their expected returns. The first assumption involves price
correlation between the two stocks and the second involves a bounded non-zero
momentum condition. With known uncertainty bounds on the parameters associated
with these assumptions, our new version of the RPE Theorem provides necessary
and sufficient conditions on the positive feedback parameter K of the
controller under which robust positive expectation is assured. We also
demonstrate that our result generalizes the one existing for the single-stock
case. Finally, it is noted that our results also can be interpreted in the
context of pairs trading.
]]>803397http://www.moneyscience.com/pg/blog/arXiv/read/803397/a-generalization-of-the-robust-positive-expectation-theorem-for-stock-trading-via-feedback-control-arxiv180304591v1-qfinsthttp://www.moneyscience.com/pg/blog/arXiv/read/803396/theoretical-and-empirical-analysis-of-trading-activity-arxiv180304892v1-qfintrTue, 13 Mar 2018 20:00:03 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/VH5dCjs6YVI/theoretical-and-empirical-analysis-of-trading-activity-arxiv180304892v1-qfintr
<![CDATA[Theoretical and empirical analysis of trading activity. (arXiv:1803.04892v1 [q-fin.TR])]]>Understanding the structure of financial markets deals with suitably
determining the functional relation between financial variables. In this
respect, important variables are the trading activity, defined here as the
number of trades $N$, and traded volume $V$ in the asset, its price $P$, the
squared volatility $\sigma^2$, the corresponding bid-ask spread $S$ and the
cost of trading $C$. Different reasonings result in simple proportionality
relations ("scaling laws") between these variables. A basic proportionality is
established between the trading activity and the squared volatility, i.e., $N
\sim \sigma^2$. More sophisticated relations are the so called 3/2-law $N^{3/2}
\sim \sigma P V /C$ and the intriguing scaling $N \sim (\sigma P/S)^2$. We
prove that these "scaling laws" are the only possible relations for considered
sets of variables by means of a well-known argument from physics: dimensional
analysis. Moreover, we provide empirical evidence based on data from the NASDAQ
stock exchange showing that the sophisticated relations hold with a certain
degree of universality. Finally, we discuss the time scaling of the volatility
$\sigma$, which turns out to be more subtle than one might naively expect.
]]>803396http://www.moneyscience.com/pg/blog/arXiv/read/803396/theoretical-and-empirical-analysis-of-trading-activity-arxiv180304892v1-qfintrhttp://www.moneyscience.com/pg/blog/arXiv/read/803395/a-scoredriven-conditional-correlation-model-for-noisy-and-asynchronous-data-an-application-to-highfrequency-covariance-dynamics-arxiv180304894v1-qfintrTue, 13 Mar 2018 19:58:58 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/JwIRCyB-bKY/a-scoredriven-conditional-correlation-model-for-noisy-and-asynchronous-data-an-application-to-highfrequency-covariance-dynamics-arxiv180304894v1-qfintr
<![CDATA[A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics. (arXiv:1803.04894v1 [q-fin.TR])]]>We propose a new multivariate conditional correlation model able to deal with
data featuring both observational noise and asynchronicity. When modelling
high-frequency multivariate financial time-series, the presence of both
problems and the requirement for positive-definite estimates makes the
estimation and forecast of the intraday dynamics of conditional covariance
matrices particularly difficult. Our approach tackles all these challenging
tasks within a new Gaussian state-space model with score-driven time-varying
parameters that can be estimated using standard maximum likelihood methods.
Similarly to DCC models, large dimensionality is handled by separating the
estimation of correlations from individual volatilities. As an interesting
outcome of this approach, intra-day patterns are recovered without the need of
any cross-sectional averaging, allowing, for instance, to estimate the
real-time response of the market covariances to macro-news announcements.
]]>803395http://www.moneyscience.com/pg/blog/arXiv/read/803395/a-scoredriven-conditional-correlation-model-for-noisy-and-asynchronous-data-an-application-to-highfrequency-covariance-dynamics-arxiv180304894v1-qfintrhttp://www.moneyscience.com/pg/blog/arXiv/read/803394/dual-moments-and-risk-attitudes-arxiv161203347v2-qfinrm-updatedTue, 13 Mar 2018 19:57:30 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/SBW3GPyUt30/dual-moments-and-risk-attitudes-arxiv161203347v2-qfinrm-updated
<![CDATA[Dual Moments and Risk Attitudes. (arXiv:1612.03347v2 [q-fin.RM] UPDATED)]]>In decision under risk, the primal moments of mean and variance play a
central role to define the local index of absolute risk aversion. In this
paper, we show that in canonical non-EU models dual moments have to be used
instead of, or on par with, their primal counterparts to obtain an equivalent
index of absolute risk aversion.
]]>803394http://www.moneyscience.com/pg/blog/arXiv/read/803394/dual-moments-and-risk-attitudes-arxiv161203347v2-qfinrm-updatedhttp://www.moneyscience.com/pg/blog/arXiv/read/803213/realitycheck-for-econophysics-likelihoodbased-fitting-of-physicsinspired-market-models-to-empirical-data-arxiv180303861v1-csceMon, 12 Mar 2018 20:19:01 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/ORr63z9WydE/realitycheck-for-econophysics-likelihoodbased-fitting-of-physicsinspired-market-models-to-empirical-data-arxiv180303861v1-csce
<![CDATA[Reality-check for Econophysics: Likelihood-based fitting of physics-inspired market models to empirical data. (arXiv:1803.03861v1 [cs.CE])]]>The statistical description and modeling of volatility plays a prominent role
in econometrics, risk management and finance. GARCH and stochastic volatility
models have been extensively studied and are routinely fitted to market data,
albeit providing a phenomenological description only.
read more...

]]>803213http://www.moneyscience.com/pg/blog/arXiv/read/803213/realitycheck-for-econophysics-likelihoodbased-fitting-of-physicsinspired-market-models-to-empirical-data-arxiv180303861v1-cscehttp://www.moneyscience.com/pg/blog/arXiv/read/803212/calibration-of-local-volatility-model-with-stochastic-interest-rates-by-efficient-numerical-pde-method-arxiv180303941v1-qfinmfMon, 12 Mar 2018 20:17:56 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/oSSbDUn6XeA/calibration-of-local-volatility-model-with-stochastic-interest-rates-by-efficient-numerical-pde-method-arxiv180303941v1-qfinmf
<![CDATA[Calibration of Local Volatility Model with Stochastic Interest Rates by Efficient Numerical PDE Method. (arXiv:1803.03941v1 [q-fin.MF])]]>Long maturity options or a wide class of hybrid products are evaluated using
a local volatility type modelling for the asset price S(t) with a stochastic
interest rate r(t). The calibration of the local volatility function is usually
time-consuming because of the multi-dimensional nature of the problem. In this
paper, we develop a calibration technique based on a partial differential
equation (PDE) approach which allows an efficient implementation. The essential
idea is based on solving the derived forward equation satisfied by P(t; S;
r)Z(t; S; r), where P(t; S; r) represents the risk neutral probability density
of (S(t); r(t)) and Z(t; S; r) the projection of the stochastic discounting
factor in the state variables (S(t); r(t)). The solution provides effective and
sufficient information for the calibration and pricing. The PDE solver is
constructed by using ADI (Alternative Direction Implicit) method based on an
extension of the Peaceman-Rachford scheme. Furthermore, an efficient algorithm
to compute all the corrective terms in the local volatility function due to the
stochastic interest rates is proposed by using the PDE solutions and grid
points. Different numerical experiments are examined and compared to
demonstrate the results of our theoretical analysis.
]]>803212http://www.moneyscience.com/pg/blog/arXiv/read/803212/calibration-of-local-volatility-model-with-stochastic-interest-rates-by-efficient-numerical-pde-method-arxiv180303941v1-qfinmfhttp://www.moneyscience.com/pg/blog/arXiv/read/803211/matching-distributions-recovery-of-implied-physical-densities-from-option-prices-arxiv180303996v1-qfinprMon, 12 Mar 2018 20:16:52 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/uSYxm5eYWjg/matching-distributions-recovery-of-implied-physical-densities-from-option-prices-arxiv180303996v1-qfinpr
<![CDATA[Matching distributions: Recovery of implied physical densities from option prices. (arXiv:1803.03996v1 [q-fin.PR])]]>We introduce a non-parametric method to recover physical probability
distributions of asset returns based on their European option prices and some
other sparse parametric information. Thus the main problem is similar to the
one considered foir instance in the Recovery Theorem by Ross (2015), except
that here we consider a non-dynamical setting. The recovery of the distribution
is complete, instead of estimating merely a finite number of its parameters,
such as implied volatility, skew or kurtosis. The technique is based on a
reverse application of recently introduced Distribution Matching by the author
and is related to the ideas in Distribution Pricing by Dybvig (1988) as well as
comonotonicity.
]]>803211http://www.moneyscience.com/pg/blog/arXiv/read/803211/matching-distributions-recovery-of-implied-physical-densities-from-option-prices-arxiv180303996v1-qfinprhttp://www.moneyscience.com/pg/blog/arXiv/read/803210/algorithmic-trading-with-partial-information-a-mean-field-game-approach-arxiv180304094v1-qfinmfMon, 12 Mar 2018 20:15:48 -0500
http://feedproxy.google.com/~r/FinancialResearchFocus/~3/o79BPksciWc/algorithmic-trading-with-partial-information-a-mean-field-game-approach-arxiv180304094v1-qfinmf
<![CDATA[Algorithmic Trading with Partial Information: A Mean Field Game Approach. (arXiv:1803.04094v1 [q-fin.MF])]]>Financial markets are often driven by latent factors which traders cannot
observe. Here, we address an algorithmic trading problem with collections of
heterogeneous agents who aim to perform statistical arbitrage, where all agents
filter the latent states of the world, and their trading actions have permanent
and temporary price impact. This leads to a large stochastic game with
heterogeneous agents. We solve the stochastic game by investigating its
mean-field game (MFG) limit, with sub-populations of heterogenous agents, and,
using a convex analysis approach, we show that the solution is characterized by
a vector-valued forward-backward stochastic differential equation (FBSDE). We
demonstrate that the FBSDE admits a unique solution, obtain it in closed-form,
and characterize the optimal behaviour of the agents in the MFG equilibrium.
Moreover, we prove the MFG equilibrium provides an $\epsilon$-Nash equilibrium
for the finite player game. We conclude by illustrating the behaviour of agents
using the optimal MFG strategy through simulated examples.
]]>803210http://www.moneyscience.com/pg/blog/arXiv/read/803210/algorithmic-trading-with-partial-information-a-mean-field-game-approach-arxiv180304094v1-qfinmf