Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

A Time-Varying Network for Cryptocurrencies

Li Guo
Wolfgang Karl Härdle
Yubo Tao

Abstract:
Cryptocurrencies return cross-predictability and technological similarity yield
information on risk propagation and market segmentation. To investigate these
effects, we build a time-varying network for cryptocurrencies, based on the
evolution of return cross-predictability and technological similarities. We
develop a dynamic covariate-assisted spectral clustering method to consistently
estimate the latent community structure of cryptocurrencies network that
accounts for both sets of information. We demonstrate that investors can achieve
better risk diversification by investing in cryptocurrencies from different
communities. A cross-sectional portfolio that implements an inter-crypto
momentum trading strategy earns a 1.08% daily return. By dissecting the
portfolio returns on behavioral factors, we con rm that our results are not
driven by behavioral mechanisms.

Keywords:
Community detection, Dynamic stochastic blockmodel, Covariates, Co-clustering,
Network risk, Momentum

JEL Classification:
C00

A data-driven P-spline smoother and the P-Spline-GARCH models

Yuanhua Feng
Wolfgang Karl Härdle

Abstract:
Penalized spline smoothing of time series and its asymptotic properties are
studied. A data-driven algorithm for selecting the smoothing parameter is
developed. The proposal is applied to define a semiparametric extension of the
well-known Spline- GARCH, called a P-Spline-GARCH, based on the log-data
transformation of the squared returns. It is shown that now the errors process
is exponentially strong mixing with finite moments of all orders. Asymptotic
normality of the P-spline smoother in this context is proved. Practical
relevance of the proposal is illustrated by data examples and simulation. The
proposal is further applied to value at risk and expected shortfall.

Keywords:
P-spline smoother, smoothing parameter selection, P-Spline-GARCH, strong mixing,
value at risk, expected shortfall

JEL Classification:
C14, C51

Blockchain mechanism and distributional characteristics of cryptos

Min-Bin Lin
Kainat Khowaja
Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle

Abstract:
We investigate the relationship between underlying blockchain mechanism of
cryptocurrencies and its distributional characteristics. In addition to price,
we emphasise on using actual block size and block time as the operational
features of cryptos. We use distributional characteristics such as fourier power
spectrum, moments, quantiles, global we optimums, as well as the measures for
long term dependencies, risk and noise to summarise the information from crypto
time series. With the hypothesis that the blockchain structure explains the
distributional characteristics of cryptos, we use characteristic based spectral
clustering to cluster the selected cryptos into  five groups. We scrutinise
these clusters and  find that indeed, the clusters of cryptos share similar
mechanism such as origin of fork, difficulty adjustment frequency, and the
nature of block size. This paper provides crypto creators and users with a
better understanding toward the connection between the blockchain protocol
design and distributional characteristics of cryptos.

Keywords:
Cryptocurrency, price, blockchain mechanism, distributional characteristics,
clustering

JEL Classification:
C00

Green financial development improving energy efficiency and economic growth: a
study of CPEC area in COVID-19 era

Linyun Zhang
Feiming Huang
Lu Lu
Xinwen Ni

Abstract:
This study seeks to evaluate the effect of green financial development,
improving energy efficiency and economic growth on Covid-19 tenure. For this,
the CPEC area is recommended to look into. Present study revealed the energy
economic negative repercussions of Covid-19 impacts. It is assumed that, in
China and Pakistan, economic expansion, trade openness, financial development,
and urbanization coexist. To verify the postulated impacts of economic activity
on the environment, we do Johansen cointegration, error correction, and Granger
causality tests. We discovered that economic growth, energy consumption, trade
openness, financial development, and urbanization had a long-term relationship
to CO2 emissions in Pakistan. Urbanization is the only macroeconomic factor with
a detrimental effect on carbon emissions. As with China, no cointegration is
found across variables, but unidirectional causality from energy consumption and
economic growth to economic growth is established. Economic growth, energy
consumption, and trade openness also each have bidirectional causal effect on
financial development. According to statistical data, along with significant
projected economic development in CPEC countries, policymakers and regulators
are urged to strengthen environmental protection laws in China and Pakistan.

Keywords:
Green financial development, Energy Financing, Energy Efficiency, Economic
growth, Covid-19 crises, Capital formation

JEL Classification:
C00

Robustifying Markowitz

Wolfgang Karl Härdle
Yegor Klochkov
Alla Petukhina
Nikita Zhivotovskiy

Abstract:
Markowitz mean-variance portfolios with sample mean and covariance as input
parameters feature numerous issues in practice. They perform poorly out of
sample due to estimation error, they experience extreme weights together with
high sensitivity to change in input parameters. The heavy-tail characteristics
of  financial time series are in fact the cause for these erratic fluctuations
of weights that consequently create substantial transaction costs. In
robustifying the weights we present a toolbox for stabilizing costs and weights
for global minimum Markowitz portfolios. Utilizing a projected gradient descent
(PGD) technique, we avoid the estimation and inversion of the covariance
operator as a whole and concentrate on robust estimation of the gradient descent
increment. Using modern tools of robust statistics we construct a
computationally efficient estimator with almost Gaussian properties based on
median-of-means uniformly over weights. This robustified Markowitz approach is
confirmed by empirical studies on equity markets. We demonstrate that
robustified portfolios reach higher risk-adjusted performance and the lowest
turnover compared to shrinkage based and constrained portfolios.

Keywords:
.

JEL Classification:
C00

Understanding jumps in high frequency digital asset markets

Danial Saef
Odett Nagy
Sergej Sizov
Wolfgang Karl Härdle

Abstract:
While attention is a predictor for digital asset prices, and jumps in Bitcoin
prices are well-known, we know little about its alternatives. Studying high
frequency crypto data gives us the unique possibility to confirm that cross
market digital asset returns are driven by high frequency jumps clustered around
black swan events, resembling volatility and trading volume seasonalities.
Regressions show that intra-day jumps significantly influence end of day returns
in size and direction. This provides fundamental research for crypto option
pricing models. However, we need better econometric methods for capturing the
specific market microstructure of cryptos. All calculations are reproducible via
the quantlet.com technology.

Keywords:
jumps, market microstructure noise, high frequency data, cryptocurrencies, CRIX,
option pricing

JEL Classification:
C00

Advanced Statistical Learning on Short Term Load Process Forecasting

Junjie Hu
Brenda López Cabrera
Awdesch Melzer

Abstract:
Short Term Load Forecast (STLF) is necessary for effective scheduling, operation
optimization trading, and decision-making for electricity consumers. Modern and
efficient machine learning methods are recalled nowadays to manage complicated
structural big datasets, which are characterized by having a nonlinear temporal
dependence structure. We propose different statistical nonlinear models to
manage these challenges of hard type datasets and forecast 15-min frequency
electricity load up to 2-days ahead. We show that the Long-short Term Memory
(LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line
of a chemical production facility outperform several other predictive models in
terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test
with several metrics. The predictive information is fundamental for the risk
and production management of electricity consumers.

Keywords:
Short Term Load Forecast, Deep Neural Network, Hard Structure Load Process

JEL Classification:
C51, C52, C53, Q31, Q41