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
"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
We present a stylized model of a venture capital firm
being active in a specific market, where all possible projects compete with each other. These projects have a payoff
structure such that only the best few alternatives generate a positive return.
The payoff depends on the relative ranking of a project’s quality in comparison
with all other projects. This discontinuous payoff structure is motivated by
the internet economy, where often classical price competition is not possible, ad the market share is chiefly determined by the relative
quality-ranking of the product, its absolute quality being of second order. We
assume that investment alternatives show up sequentially, and due to competition,
the investment decision cannot be postponed for too long. The information
available at any period is the current project’s and
all prior projects’ quality characteristics. Finally, after the last investment
alternative has shown up, those n projects with highest realized quality
characteristics generate positive gross-returns. We derive a recursive
formulation of the value function for risk-neutral and risk-averse investors.
Sufficient conditions under which the value function is non-increasing in the
number of periods are provided. A simulation study demonstrates how optimal
investment decisions are affected by the time horizon and by the attitudes
towards risk. The model presented is an extension of Bruss and Ferguson (2002).
Download from SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4546624
Recent Publications 2015-
``Extending the Demand System Approach to Asset
Pricing'',
with T. Gehrig and A. Westerkamp; Financial Markets
and Portfolio Management
[https://orcid.org/0000-0001-5388-0601}
Abstract
This article introduces a shrinkage procedure which
allows to improve upon the parametric portfolio approach introduced in Brandt
et al (Review of Financial Studies 22(9): 3411–3477, 2009) and more general
factor conditional frameworks. We analyze optimal investment decisions for
constant absolute and constant relative risk aversion. In both preference
classes, especially out-of-sample performance of the optimal strategies is
rather volatile. In order to reduce parameter and
model uncertainty, we augment the optimal strategies by a shrinkage device that
pulls the portfolio weights toward a predetermined policy portfolio. Our
theoretical approach thereby extends the demand systems approach of Koijen and Yogo (Journal of Political Economy,
127(4):1475–1515, 2019) to more general classes of preferences and provides
conditions for the existence of equilibrium. As a side product, we establish that
the characteristics-based parametric portfolio approach of Brandt et al.
(Review of Financial Studies 22(9): 3411–3477, 2009) can only be justified as
optimal investments under exceedingly strong assumptions. In empirical US data,
our 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.
``Inflation
Forecasting in Turbulent Times''
with Martin
Ertl, Ines Fortin, Jaroslava Hlouskova, Sebastian P. Koch, and Robert M. Kunst;
Empirica 2024. [https://doi.org/10.1007/s10663-024-09633-z]
Abstract
In the recent years many countries were hit by a
series of macroeconomic shocks, most notably as a consequence
of the COVID-19 pandemic and Russia’s invasion in Ukraine, raising
inflation rates to multi-decade highs and suspending well-ocumente
macroeconomic relationships. To capture these tail events, we propose a mixed-frequency
Bayesian vector autoregressive (BVAR) model with Student t-distributed innovations
or with stochastic volatility. Whereas inflation, industrial production, as
well as oil and gas prices are available at monthly frequencies, real gross domestic
product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency
setup using the forward-filtering–backward-sampling algorithm to generate
monthly real GDP growth rates. We forecast inflation in those euro area countries
that extensively import energy from Russia and therefore have been heavily exposed
to the recent oil and gas price shocks. To measure the forecast performance of
the mixed-frequency BVAR model, we compare our inflation forecasts with those
generated by a battery of competing inflation forecasting models. The proposed
BVAR models dominate the competition for all countries in terms of the log
predictive density score.
“Hunting for Superstars”
With Martin Meier, Mathematics and Financial
Economics, 2023, Vol.
17, pp.~335-371. [https://doi.org/10.1007/s11579-023-00337-9]
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.
With Ines Fortin and Jaroslava Hlouskova, Empirica, 2023, Vol.~50, pp.~481-521. [https://doi.org/10.1007/s10663-023-09570-3]
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.
“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.
Download Supplementary Materials