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On the Determination of the L\'evy Exponent in Asset Pricing Models. (arXiv:1811.07220v1 [q-fin.MF])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

We consider the problem of determining the L\'evy exponent in a L\'evy model
for asset prices given the price data of derivatives. The model, formulated
under the real-world measure $\mathbb P$, consists of a pricing kernel
$\{\pi_t\}_{t\geq0}$ together with one or more non-dividend-paying risky assets
driven by the same L\'evy process. If $\{S_t\}_{t\geq0}$ denotes the price
process of such an asset then $\{\pi_t S_t\}_{t\geq0}$ is a $\mathbb
P$-martingale. The L\'evy process $\{ \xi_t...

Portfolio Theory, Information Theory and Tsallis Statistics. (arXiv:1811.07237v1 [q-fin.ST])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

We developed a strategic of optimal portfolio based on information theory and
Tsallis statistics. The growth rate of a stock market is defined by using
q-deformed functions and we find that the wealth after n days with the optimal
portfolio is given by a q-exponential function. In this context, the asymptotic
optimality is investigated on causal portfolios, showing advantages of the
optimal portfolio over an arbitrary choice of causal portfolios. Finally, we
apply the formulation for the...

CVA and vulnerable options pricing by correlation expansions. (arXiv:1811.07294v1 [q-fin.CP])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

We consider the problem of computing the Credit Value Adjustment ({CVA}) of a
European option in presence of the Wrong Way Risk ({WWR}) in a default
intensity setting. Namely we model the asset price evolution as solution to a
linear equation that might depend on different stochastic factors and we
provide an approximate evaluation of the option's price, by exploiting a
correlation expansion approach, introduced in \cite{AS}. We compare the
numerical performance of such a method with that...

Optimal Iterative Threshold-Kernel Estimation of Jump Diffusion Processes. (arXiv:1811.07499v1 [math.ST])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

In this paper, we study a threshold-kernel estimation method for
jump-diffusion processes, which iteratively applies thresholding and kernel
methods in an approximately optimal way to achieve improved finite-sample
performance. As in Figueroa-L\'opez and Nisen (2013), we use the expected
number of jump misclassification as the objective function to optimally select
the threshold parameter of the jump detection scheme. We prove that the
objective function is quasi-convex and obtain a novel...

On the degree of incompleteness of an incomplete financial market. (arXiv:1811.07509v1 [q-fin.MF])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

In order to find a way of measuring the degree of incompleteness of an
incomplete financial market, the rank of the vector price process of the traded
assets and the dimension of the associated acceptance set are introduced. We
show that they are equal and state a variety of consequences.

Practical Deep Reinforcement Learning Approach for Stock Trading. (arXiv:1811.07522v1 [cs.LG])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

Stock trading strategy plays a crucial role in investment companies. However,
it is challenging to obtain optimal strategy in the complex and dynamic stock
market. We explore the potential of deep reinforcement learning to optimize
stock trading strategy and thus maximize investment return. 30 stocks are
selected as our trading stocks and their daily prices are used as the training
and trading market environment. We train a deep reinforcement learning agent
and obtain an adaptive trading...

The ETS challenges: a machine learning approach to the evaluation of simulated financial time series for improving generation processes. (arXiv:1811.07792v1 [q-fin.CP])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

This paper presents an evaluation framework that attempts to quantify the
"degree of realism" of simulated financial time series, whatever the simulation
method could be, with the aim of discover unknown characteristics that are not
being properly reproduced by such methods in order to improve them. For that
purpose, the evaluation framework is posed as a machine learning problem in
which some given time series examples have to be classified as simulated or
real financial time series. The...

Cryptoasset Factor Models. (arXiv:1811.07860v1 [q-fin.PM])

November 19, 2018 by Quantitative Finance at arXiv   Comments (0)

We propose factor models for the cross-section of daily cryptoasset returns
and provide source code for data downloads, computing risk factors and
backtesting them out-of-sample. In "cryptoassets" we include all
cryptocurrencies and a host of various other digital assets (coins and tokens)
for which exchange market data is available. Based on our empirical analysis,
we identify the leading factor that appears to strongly contribute into daily
cryptoasset returns. Our results suggest that...

Okay here’s what’s going on

November 19, 2018 by The Reformed Broker   Comments (0)

I don’t have any empiricism to attach to my remarks below. There’s no evidence for anything I’m about to say here, which is rare for this blog, but whatever I’m saying it because I think this is what’s going on… This week marks the ninth week of the correction that began in US stocks during......

Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps. (arXiv:1811.06606v1 [cs.AI])

November 18, 2018 by Quantitative Finance at arXiv   Comments (0)

In recent years, artificial intelligence (AI) decision-making and autonomous
systems became an integrated part of the economy, industry, and society. The
evolving economy of the human-AI ecosystem raising concerns regarding the risks
and values inherited in AI systems. This paper investigates the dynamics of
creation and exchange of values and points out gaps in perception of
cost-value, knowledge, space and time dimensions. It shows aspects of value
bias in human perception of achievements and...