Leopold Sögner
Current Research (updated Febuary 2023)
E-Mail: soegner@ihs.ac.at
ORCID-ID: https://orcid.org/0000-0001-5388-0601
“Stock-Oil Comovement: Fundamentals or
Financialization?”
With Alessandro Melone, Otto Randl,
and Josef Zechner
Abstract
The return
correlation between U.S. stocks and oil has shifted from negative to positive
since 2008. We use a return decomposition framework to demonstrate that the underlying
reason for this structural change is a shift in the correlation between cash
flow news for the two assets. Intuitively, as the U.S. turned from an oil
importer to a net exporter, the correlation between the cash flow news
associated with oil and the U.S. stock market turned positive. Our findings
help to understand the set of potential determinants of equity-commodity
correlations and the diversification benefits of investing in commodities.
Download from SSRN: https://ssrn.com/abstract=4205724
With Thomas
Gehrig and Arne Westerkamp
Abstract
We
extend the demand systems approach of Koijen and Yogo (2019) to more general classes of preferences.
Specifically, we analyse constant absolute and
constant relative risk aversion, provide conditions for the existence of
equilibrium, and evaluate equilibrium prices at US-data.
We find that
constant absolute risk aversion works particularly well at moderate
levels of risk aversion. In the case of relative risk aversion, optimal
interior portfolio solutions may not even exist.
In both
preference classes especially out-of-sample predictions are rather
volatile. In order to improve out-of-sample performance we augment the
optimal strategies by a shrinkage device. As a side product we
establish that the characteristics-based parametric portfolio approach
of Brandt, Santa Clara and Valkanov (RFS 2009)
can only be justified as optimal investments under exceedingly strong
assumptions. In empirical data the shrinkage approach outperforms the
parametric approach and the naive 1/N -strategy over quite a
wide range of levels of absolute and relative risk aversion.
Download from SSRN: https://ssrn.com/abstract=4292475
"Fully Modified Estimation of Spatially Correlated Cointegrated
Systems"
With Martin
Wagner
Abstract
We consider a system of spatially correlated
cointegrating relationships. Deterministic trend terms are also allowed for and
it is shown that the convergence rate of the spatial correlation parameter is
determined by the order of the trend polynomial. In addition to the correlation
induced by the spatial autoregressive formulation we also allow for cross-unit
correlation of the integrated regressors as well as the error terms.
Cointegration amongst the regressors is not allowed, as is standard in the
cointegrating regression literature. In order to obtain limiting distributions
that allow for standard asymptotic inference, the fully modified OLS estimation
principle is extended to the current situation. Finally, the methodology is
applied to a credit risk data set.
“Global-VAR Modeling with Mixed Frequency Data”
Abstract
This article considers a global vector auto-regressive model (G-VAR) developed
in Di Mauro et al. (2007) as well as Chudik and Pesaran (2016). In this set-up we allow for
common variables as well as foreign variables to influence the country
specific variables. The variables are considered to be integrated of order one.
We allow for $r$ cointegrating relationships. Since empirical data used in
G-VAR modeling are available at different frequencies we extend the approach
described in Chudik and Pesaran
(2016) to the mixed frequency case. We follow Seong
et al. (2013) and construct an expectation-maximization (EM) based parameter
estimation procedure. For the global model forecasting as well as generalized
impulse response analysis is performed.
“Optimal High-Risk Investment”
With Martin Meier and Gregor Kastner
Abstract
This article augments a Bruss (2002)-type investment model, where
n investor observes a finite sequence of
investment alternatives endowed with a quality characteristic. The information
available is the current and all prior quality characteristics. The
investor has to decide whether to invest in the same period the project shows
up. Finally, after the last investment alternative had shown up, only the
projects with the best and the second best quality
characteristic generate positive returns, while the payoffs of all other
projects are zero. These returns are either a-priori known or stochastic. Under
these assumptions this article derives the value functions and optimal investment
rules for risk-neutral or risk averse investors. A simulation study demonstrates
how optimal investment decisions are affected by the time horizon and by the
attitudes towards risk.
“Hunting for Superstars”
With Martin Meier
Abstract
An investor faces a sequence of high-risk investment opportunities. The investor
ranks the corresponding projects seen so far and must immediately decide
whether and how much to invest into the currently observed opportunity. Returns
are realized at the end of the investment horizon, where only a small number of
superstars, that is top ranked projects, generate positive returns. The payoffs
of all other opportunities are zero. Under these assumptions we derive the
value functions and optimal investment rules for risk-neutral or risk averse
investors. As well, we obtain a few results on the comparative statics of these
investment problems. A simulation study is performed for risk-neutral and
risk-averse investors.
Download from SSRN: https://ssrn.com/abstract=4148313
With Borys Koval and Sylvia Frühwirth-Schnatter
Abstract
This article considers a stable vector autoregressive (VAR) model and
investigates return predictability in a Bayesian context. The VAR system
comprises asset returns and the dividend-price ratio
as proposed in Cochrane (2008), and allows pinning down the question of return
predictability to the value of one particular model parameter. We develop a new
shrinkage type prior for this parameter and compare our Bayesian approach to
ordinary least squares estimation and to the reduced-bias estimator proposed in
Amihud and Hurvich (2004).
A simulation study shows that the Bayesian approach dominates the reduced-bias
estimator in terms of observed size (false positive) and power (false
negative). We apply our methodology to annual CRSP value-weighted returns
running, respectively, from 1926 to 2004 and from 1953 to 2021. For the first
sample, the Bayesian approach supports the hypothesis of no return
predictability, while for the second data set weak evidence for predictability
is observed.
Download
from SSRN: https://ssrn.com/abstract=4288973
“REMUS: Generic Identifiability for the Cointegrated
Unit Root VAR”
With Philipp Gersing and Manfred Deistler
Abstract
The “REtrieval from MUltiperiod Systems”
(REMUS) approach
based on blocking developed in Anderson et al. (2016) is concerned with retrieving
an underlying high frequency model from mixed frequency observations. In this paper we
investigate parameter-identifiability in the Johansen (1995) vector error
correction model for mixed frequency data. We prove that from the second
moments of the blocked process after
taking
differences at lag N (N is
the slow sampling rate), the parameters of the high frequency system are
generically identified. We treat the stock and the flow case as well
as deterministic terms.
Download
from SSRN: https://ssrn.com/abstract=4068560
With Ines Fortin and Jaroslava Hlouskova
Abstract
We estimate new indices measuring financial and economic uncertainty in
the euro area, Germany, France, the United Kingdom and Austria, following the
approach of Jurado, Ludvigson and Ng (2015), which
measures uncertainty by the degree of predictability. We perform an impulse
response analysis in a vector error correction framework, where we focus on the
impact of both local and global uncertainty shocks on industrial
production, employment and the stock market. We find that global financial and
economic uncertainty have significant negative effects on local industrial
production, employment, and the stock market, while we find hardly any
influence of local uncertainty on these variables. In addition
we perform a forecasting analysis, where we assess the merits of uncertainty
indicators for forecasting industrial production, employment and the stock
market, using different performance measures. The results suggest that
financial uncertainty significantly improves the forecasts of the stock market in
terms of profit-based measures, while economic uncertainty gives, in general,
more insight when forecasting macroeconomic variables.
Download
from SSRN: https://ssrn.com/abstract=4349929
Recent Publications 2015-
“Deviations from Triangular Arbitrage Parity in
Foreign Exchange and Bitcoin Markets” with J. Reynolds and M. Wagner.
Central European Journal of Economic Modelling and
Econometrics, Vol. 13(2), pp. 105-146. DOI: 10.24425/cejeme.2021.137358
Abstract
This paper
applies recently developed procedures to monitor and date so-called “financial
market dislocations”, defined as periods in which substantial deviations from
arbitrage parities take place. In particular, we use a
cointegration perspective to focus on deviations from the triangular arbitrage
parity for exchange rate triplets. Due to increasing attention on and
importance of mispricing in the market for cryptocurrencies, we include the
cryptocurrency Bitcoin in addition to fiat currencies in our analysis. We do
not find evidence for substantial deviations from the triangular arbitrage
parity when only traditional fiat currencies are considered, but document
significant deviations from triangular arbitrage parities in the newer market
for Bitcoin. We tentatively confirm the importance of our results for portfolio
strategies by showing that a currency portfolio that trades based on our detected
break-points outperforms a simple buy-and-hold
strategy.
"Generalized Method of Moment based Parameter Estimation of Affine
Term Structure Models", with Jaroslava Hlouskova
With J. Hlouskova, Econometrics and Statistics, 2020,
Vol. 13, pp. 2-15. https://doi.org/10.1016/j.ecosta.2019.10.001
Abstract
This article studies dynamic panel data models in which the long run
outcome for a particular cross-section is affected by a weighted average of the
outcomes in the other cross-sections. We show that imposing such a structure
implies a model with several cointegrating relationships that, unlike in the
standard case, are nonlinear in the coefficients to be estimated.
Assuming that the weights are exogenously given, we extend the dynamic ordinary
least squares methodology and provide a dynamic two-stage least squares
estimator. We derive the large sample properties of our proposed estimator
under a set of low-level assumptions. Then our methodology is applied to US
financial market data, which consist of credit default swap spreads, as well as firm-specific and
industry data. We construct the economic space using a “closeness” measure for
firms based on input–output matrices. Our estimates show that this particular form of spatial correlation of credit default
swap spreads is substantial and highly significant.
"An Exploratory Analysis on the Risk to be Offended on the Internet",
with Susanne Kirchner
Archives of
Data Science, Series A, 2018, Vol.. 3(1), pages 1-26,
https://doi.org/10.5445/KSP/1000083488/02
Abstract
Questionnaire data is used to identify socio-demographic
as well as the risk-awareness characteristics of users offended on the
Internet. The data comprises a representative sample of 3,000 individuals,
containing information on employment, education, age, the frequency of Internet
usage and security measures taken by the users. By means of a cluster analysis,
within the sub-sample of offended users, we identify a female group, where
employment and education are high, a male cluster with similar characteristics,
a group of urban users with low security awareness and a group of young users.
Regressions show that the frequency of using the Internet increases, while to
communicate only to people known in real life reduces the risk to be offended
on the Internet.
" Parameter Estimation and Inference with Spatial Lags and
Cointegration", with Jan Mutl
Econometric Reviews, 2019, Vol. 38}(6), pp.597-635,
https://doi.org/10.1080/07474938.2017.1382803
Abstract
We study dynamic panel data models where the long run outcome
for a particular cross-section is affected by a weighted average of the
outcomes in the other cross-sections. We show that imposing such a structure
implies several cointegrating relationships that are nonlinear in the
coefficients to be estimated. Assuming that the weights are exogenously given,
we extend the dynamic ordinary least squares methodology and provide a dynamic
two-stage least squares estimator. We derive the large sample properties of our
proposed estimator and investigate its small sample distribution in a
simulation study. Then our methodology is applied to US financial market data,
which consist of credit default swap spreads, firm specific and industry data.
A "closeness" measure for firms is based on input-output matrices.
Our estimates show that this particular form of
spatial correlation of credit default spreads is substantial and highly
significant
"A new strategy for Robbins’ problem of optimal stopping", with
Martin Meier
Journal of
Applied Probability, 2017, Vol.
54, pp. 331 - 336.
https://doi.org/10.1017/jpr.2016.103
Abstract
In this article we study the expected rank problem
under full information. Our approach uses the planar Poisson approach from Gnedin (2007) to derive the expected rank of a stopping
rule that is one of the simplest non-trivial examples combining rank dependent
rules with threshold rules. This rule attains an expected rank lower than
the best upper bounds obtained in the literature so far, in particular we
obtain an expected rank of 2.32614..
"Bayesian Learning, Shutdown and Convergence"
Mathematical
Social Sciences, 2015, 75, pp. 27 -
43.
https://doi.org/10.1016/j.mathsocsci.2015.01.004
Abstract
This article investigates a partial equilibrium
production model with dynamic information aggregation. Firms use observed
prices to estimate the unknown model parameter by applying Bayesian
learning. In the baseline setting, the demand structure is linear and the noise term is Gaussian. Then, prices and
quantities are supported by the real line and convergence of the limited
information to rational expectations quantities is obtained. Since a
production economy is considered, the economic constraint of non-negative
quantities is imposed. This non-negativity constraint and the assumption
that signals about demand are only received in periods where production takes
place destroy the ``optimistic'' convergence result observed in the
baseline model. With this constraint firms learning an unknown demand intercept
parameter exit with strictly positive probability, even when the true value of
this parameter would induce production in the full information setting. In a
second step, the linear demand structure is replaced by piece-wise linear
demand, such that prices become non-negative. Also in
this stetting the convergence result of the baseline
model does not hold.
"Weather and SAD Related Mood Effects on the Financial Market",
with Manfred Frühwirth
The Quarterly Review of Economics and Finance, 57, pp. 11-31.
https://doi.org/10.1016/j.qref.2015.02.003
Abstract
We investigate the relationship between weather or
seasonal affective disorder and the financial market, using a wide variety of
financial market data as well as several weather variables and a seasonal
affective disorder proxy. We distinguish between a model with a direct
effect of the weather and seasonal affective disorder on the financial market
and one with an indirect effect via a latent variable mood. Whereas only the
latter model is justified by psychological literature, the former model is
often used as an approximation. One major innovation of this paper is a
consistent econometric implementation of the indirect effects model. We
demonstrate that the approximation by direct effects yields inconsistent
estimates. Our study supports some weather related, but no seasonal
affective disorder related effects on the financial market. We show that,
instead of focusing on single market segments, an analysis of various financial
market segments is required. We also show that the analysis of individual stock
returns or bond spreads reveals additional information, compared to the
analysis of aggregate stock or bond indexes.
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