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Automation Impacts on China's Polarized Job Market. (arXiv:1908.05518v1 [econ.GN])

August 15, 2019 by Quantitative Finance at arXiv   Comments (0)

When facing threats from automation, a worker residing in a large Chinese
city might not be as lucky as a worker in a large U.S. city, depending on the
type of large city in which one resides. Empirical studies found that large
U.S. cities exhibit resilience to automation impacts because of the increased
occupational and skill specialization. However, in this study, we observe
polarized responses in large Chinese cities to automation impacts. The
polarization might be attributed to the...

Mean-variance hedging of unit linked life insurance contracts in a jump-diffusion model. (arXiv:1908.05534v1 [q-fin.PM])

August 15, 2019 by Quantitative Finance at arXiv   Comments (0)

We consider a time-consistent mean-variance portfolio selection problem of an
insurer and allow for the incorporation of basis (mortality) risk. The optimal
solution is identified with a Nash subgame perfect equilibrium. We characterize
an optimal strategy as solution of a system of partial integro-differential
equations (PIDEs), a so called extended Hamilton-Jacobi-Bellman (HJB) system.
We prove that the equilibrium is necessarily a solution of the extended HJB
system. Under certain conditions...

Optimal exercise of American options under stock pinning. (arXiv:1903.11686v2 [q-fin.CP] UPDATED)

August 15, 2019 by Quantitative Finance at arXiv   Comments (0)

We address the problem of optimally exercising American options based on the
assumption that the underlying stock's price follows a Brownian bridge whose
final value coincides with the strike price. In order to do so, we solve the
discounted optimal stopping problem endowed with the gain function $G(x) = (S -
x)^+$ and a Brownian bridge whose final value equals $S$. These settings came
up as a first approach of optimally exercising an option within the so-called
"stock pinning" scenario. The...

Murdoch has seen enough

August 15, 2019 by The Reformed Broker   Comments (0)

Donald Trump upsets the boss... The post Murdoch has seen enough appeared first on The Reformed Broker.

Labeling, transforming, and structuring training data sets for machine learning

August 15, 2019 by The Practical Quant   Comments (0)

The O'Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel.In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled...

“Economic Buffoonery”

August 15, 2019 by The Reformed Broker   Comments (0)

There are no actual economists in Trump’s inner circle. ... The post “Economic Buffoonery” appeared first on The Reformed Broker.

Forecasting U.S. Textile Comparative Advantage Using Autoregressive Integrated Moving Average Models and Time Series Outlier Analysis. (arXiv:1908.04852v1 [econ.GN])

August 14, 2019 by Quantitative Finance at arXiv   Comments (0)

To establish an updated understanding of the U.S. textile and apparel (TAP)
industrys competitive position within the global textile environment, trade
data from UN-COMTRADE (1996-2016) was used to calculate the Normalized Revealed
Comparative Advantage (NRCA) index for 169 TAP categories at the four-digit
Harmonized Schedule (HS) code level. Univariate time series using
Autoregressive Integrated Moving Average (ARIMA) models forecast short-term
future performance of Revealed categories with...

Is being `Robust' beneficial?: A perspective from the Indian market. (arXiv:1908.05002v1 [q-fin.PM])

August 14, 2019 by Quantitative Finance at arXiv   Comments (0)

The problem of data uncertainty has motivated the incorporation of robust
optimization in various arenas, beyond the Markowitz portfolio optimization.
This work presents the extension of the robust optimization framework for the
minimization of downside risk measures, such as Value-at-Risk (VaR) and
Conditional Value-at-Risk (CVaR). We perform an empirical study of VaR and CVaR
frameworks, with respect to their robust counterparts, namely, Worst-Case VaR
and Worst-Case CVaR, using the market...

Can robust optimization offer improved portfolio performance?: An empirical study of Indian market. (arXiv:1908.04962v1 [q-fin.PM])

August 14, 2019 by Quantitative Finance at arXiv   Comments (0)

The emergence of robust optimization has been driven primarily by the
necessity to address the demerits of the Markowitz model. There has been a
noteworthy debate regarding consideration of robust approaches as superior or
at par with the Markowitz model, in terms of portfolio performance. In order to
address this skepticism, we perform empirical analysis of three robust
optimization models, namely the ones based on box, ellipsoidal and separable
uncertainty sets. We conclude that robust...

Accurate Finite Difference Scheme with Hermite Interpolation for Pricing American Put Options Using a Regime Switching Model. (arXiv:1908.04900v1 [q-fin.CP])

August 14, 2019 by Quantitative Finance at arXiv   Comments (0)

We consider a system of coupled free boundary problems for pricing American
put options with regime switching. To solve this system, we first fix the
optimal exercise boundary for each regime resulting in multi-variable fixed
domains. We further eliminate the first order derivatives associated with the
regime switching model by taking derivatives to obtain a system of coupled
partial differential equations which we called the asset-delta-gamma-speed
option equations. The fourth-order compact...