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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Risk sharing with gradual financial integration: The Visegrad countries and the euro area
No full textNie dotycz
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dynamika cen towarów w skali czasowej
No full textIntroduction Commodity markets are distinct from other product markets due to the
existence of forward sales and futures contracts. Forward selling and the trading of a commodity derivative implies prices are subject to the influence of economic agents who are
not directly engaged in consumption or production of the commodity. As a result, even
when the forces of supply and demand are in equilibrium, prices may still move and vary
purely due to activities of agents operating in the futures markets. Inspired by this observation, the dissertation provides a new analysis of the role of futures markets trading on
the dynamics of commodity prices over different time scales.
Throughout the dissertation, the underlying economy can be conceived of as populated by a commodity producing firm with access to a stochastic production technology
that yields new output of the commodity every time period. The firm also has access to a
storage technology which it can use to hold inventory. The firm’s sales are made either in
a spot market for cash or can be sold ahead of production for future delivery using a forward contract specifying the price and date of delivery to the holder or buyer. When such
forward contracts are traded or exchanged in a centralized market, they become futures
contracts.
Stochastic production and consumption of the commodity implies that the firm faces
a risk of losses from volatile prices. The firm would therefore like to sell forward as much
of its output as it can. Those who buy the firm’s contract take on the risk of changing
prices (are subject to loss) and demand a risk–premium as compensation for taking over
the firm’s risk. In commodity markets, this risk premium is measured either as the basis,
the contemporaneous difference between the current spot price and the forward price or
the expected return, the difference between the expected future spot price and the forward price (Yang, 2013). These risk premiums depend on transaction costs incurred when
trading futures in a commodity exchange (Hasbrouck, 2009) and will have an effect on
the firms investments in physical production of the commodity – as it reflects the cost of
hedging.
Given that the risk premiums are a function of the transaction costs, quantifying the
size of these costs has become an important endeavor in understanding of price dynamics. The first paper takes on this task by developing parametric models that can be used to
measure liquidity costs using exchange traded futures transactions prices only. Simple dynamic linear regressions with switching are used in this task. The models treat underlying
price processes and liquidity costs as unobserved components in state space systems with
trade direction indicators of buyer and seller initiated transactions being the outcomes of
hidden Markov processes. Simulation studies show that the model provides accurate effective transaction cost estimates and beats the tick-rule method of signing trades using
prices.
1
Having developed a way to accurately measure the liquidity costs, focus turns to what
is driving price changes observed at a high frequency tick-by-tick level. The second paper
presents a new theory of history dependent price setting in limit order book market for
commodity futures. In traditional financial markets theory, the price discovery process
is a form of tâtonnement; informed agents trading against liquidity providers or market
markers slowly reveals private information which is incorporated into prices. The marketmarkers adjust their quotations to reflect the information revealed by the informed agents
transactions until a new equilibrium is attained. However, when trading contracts of physically delivered commodities, the transactions are directly informative of expected future
supply and demand since they reflect production and consumption intentions. Transactions therefore have price impact: a buyer initiated trade tends to push prices upwards
with the opposite effect following a seller initiated trade. The history dependent framework takes this hypothesis to the data and shows that agents trading in a limit order book
market for commodity futures adjust their prices in response to order flow – the sequence
of trade originator signs.
Over long time periods, commodity price time series exhibit boom–bust cycles that
may be accompanied by periods of either high or low volatility. One way to model time series subject to such boom and bust cycles is the hidden Markov or regime switching model
popularized in economics by Hamilton (1990). The standard regime switching model assumes that the growth and volatility phases of a time series coincide and that autoregressive lag lengths are similar across regimes. This assumption results into biases in estimates of unconditional variances across different regimes. To overcome these problems,
the third paper presents a new “Double Mixture Autoregressive” model for time series subject to potentially independent changes in level and volatility. This model allows for the
autocorrelation structure of the data generating process to vary across variance regimes.
By accounting for the change in the lag length of time series across the different volatility
periods, more precise estimates of the unconditional moments are obtained. The model
is applied to set of industrial commodity prices and is shown to accurately represent the
boom–bust cycles and volatility switches that characterize the time series.
The dissertation is divided into three related chapters/papers. The first chapter/paper,
“New Open to Old Close: Signs and Spreads in Daily Prices” presents state space models
that can be used to obtain accurate measures of transaction costs using daily summaries
of trading activity: open, close, max and min prices. The second chapter/paper, “Price
impact as reaction to order flow imbalance”, develops and successfully tests a theory of
history dependent price formation in a limit order book market of commodity futures.
Finally, the third chapter/paper, “A Double Mixture Autoregressive Model of Commodity
Prices”, presents a new type of non-linear econometric model that captures the boom–
bust cycles and volatility switches that characterize the long term behavior of commodity
price time series. I now give compact overviews of each chapter, followed by a brief conclusion of how the works are all related. (I) New Open to Old Close: Signs and Spreads in Daily Prices This chapter shows how to
estimate bid-ask spreads using observed transactions prices only. The main contribution
of this chapter is to provide a method that almost always guarantees positive estimates
of the transaction costs. Concretely, let pt = logPt be the log price of a commodity futures contract. The price follows: pt = mt + sqt
, where mt
is the unobserved efficient or
fundamental price process, s ≥ 0 is the bid-ask spread and qt = ±1 is a trade initiator indicator: qt = +1 if a transaction is buyer initiated, −1 if seller initiated. The fundamental
price follows the process mt = mt−1 +u
m
t where u
m
t
is a zero-mean disturbance uncorrelated with qt
. Bid and ask prices are: p
Bid
t = mt−1 + s and p
Ask
t = mt−1 − s which imply the
pre-trade mid–prices are given by midt =
1
2
(p
Bid
t + p
Ask
t
) = mt−1 and bid–ask spreads are
p
Bid
t − p
Ask
t = 2s.
Log returns can be written as: ∆pt = s∆qt +u
m
t
. If qt
is observed, we have: sbMLE =
Cov(∆pt
,∆qt)
Var(∆qt)
= s. But qt may not be observed or recorded in some datasets, e.g. open outcry markets. Assuming Prob(qt = +1) = Prob(qt = −1) =
1
2
, Roll (1984) estimated s by:
sbRoll =
p
−Cov(∆pt
,∆pt−1). One major shortcoming of this estimator is that if the sample autocovariance is positive, then sbRoll is undefined. This had led to an active research
area with alternatives to Roll’s estimator: Gibbs sampling approach of Hasbrouck (2002),
a time consuming and difficult to implement method; Non-parametric estimators of Abdi
and Ranaldo (2017), similar to Roll’s estimator: gives +ve autocovariances; and the empirical characteristic function of Chen, Linton and Yi (2017) which is useful but incomplete.
We propose an alternative parametric estimator that is: easy and fast to implement,
more informative: estimates mt
, s and qt
, and based on transaction prices only. We make
the following assumptions: (i). transaction prices are generated by pt = mt + st qt
, mt =
mt−1 +u
m
t
, qt = ±1, u
m
t ∼ N(0,σ
2
m) where {u
m
t
,qt }
∞
t=1
is a strictly stationary process; (ii).
st
is a random variable defined by st = s +u
s
t
, where u
s
t ∼ N
¡
0,σ
2
s
¢
with u
s
t ⊥ u
m
t
; (iii.)
the trade initiator indicator is the outcome of a first order Markov process defined by the
transition matrix: P =
h
pj k i
where pj k = Prob(qt = k|qt−1 = j), for j,k = 1, 2 and qt = ±1
are transition probabilities. The price process pt = mt + st qt can be written in state space
form as: yt ≡ ∆pt = mt − mt−1 + st qt − st−1qt−1 = At xt where xt =
³
mt
,mt−1,st
,st−1
´0
is
an unobserved state VAR(1) process: xt = φxt−1 + γs + ut
, and At =
h
1,−1,qt
,−qt−1
i
is a
measurement/observation matrix taking on 4 distinct values. At
is a first order Markov
process, inheriting properties of qt
. The error vector ut =
¡
u
m
t
, 0,u
s
t
, 0¢0
is i.i.d N(0,Σu)
where Σu is a (4×4) variance–covariance matrix with off–diagonal elements equal to zero
and diagonal (σ
2
m, 0,σ
2
s
, 0).
To test the model’s ability to give reliable estimates of bid-ask spread and mid–prices,
we generate artificial data following Hasbrouck (2004). We assume that: (i) log-prices generated by the equation pt = mt + st qt
, with m0 = 100, σ
2
m = 0.012
, st = s = 0.01 each day,
(ii) trades per day are drawn from the set {15, 16,..., 25} for 100 days giving 1, 981 observations with a median of 20 trades per day. The model is able to reproduce these vales
in after maximum likelihood estimation, providing estimates as precise as the Gibbs sampling estimator of Hasbrouck (2002). Using qt = +1 if Prob[qt = +1|ψt−1] >
1
2
labels 76% of
3
trades correctly which beats the “tick rule” method used for signing trades in the absence
of quotes.
(II) Price Impact as Reaction Order Flow Imbalance Most modern financial markets
are organized around a limit order book (LOB): when a buy(sell) order is submitted, it
is matched against still unmatched sell(buy) orders, in which case a transaction occurs.
If not immediately matched, remains active in the book until a match against a future
incoming order or canceled. We postulate a theory of price dynamics in the LOB market of
commodity futures. We begin by assuming: (i) the buyer–seller initiator indicators qt = ±1
are Markovian with transition matrix P as in chapter 1; (ii) the spread st
is time varying
and, (iii) the LOB’s mid-price/fundamental value mt
is updated in a history dependent
manner.
Our hypothesis is that agents submitting orders to the LOB adjust their prices such that
the mid–price evolves according to the price update rule: mt+1 = mt + st+1(qt − qbt+1)+u
m
t+1
where qbt+1 = E
£
qt+1|qt
¤
is the prediction of the next trade sign given the sign of the last observed transaction and u
m
t+1 ∼ N
¡
0,σ
2
m
¢
is an innovation to the mid price reflecting public information unrelated to the sequence {qt }
∞
t=1
. Markovian trade signs imply the best
linear one-step forecast: qbt+1 = E
£
qt+1|qt
¤
= qt ×Prob(qt+1 = qt)− qt ×Prob(qt+1 6= qt) =
(1−2π)qt
, where π = Prob(qt+1 6= qt) = 1−(π1p11+π2p22), is the probability of a sign reversal. The expected price change is therefore: Et∆mt+1 = 2πsqt
. The three assumptions lead
to the following properties. (i) Martingale Prices: the transaction price process is a martingale, i.e.: E(pt+1) = pt
. (ii) Bid-Ask Spread: regret free price quotations require that the ask
and bid prices are respectively set such that: p
Ask
t = Et
£
pt+1|qt+1 = +1
¤
= mt +(1+2πqt)s
and p
Bid
t = Et
£
pt+1|qt+1 = −1
¤
= mt −(1−2πqt)s, which implies the bid–ask spread given
by: p
Ask
t − p
Bid
t = 2s. (iii) No Quasi-Arbitrage: the transaction price process pt does not
admit quasi-arbitrage or price-manipulation of Huberman and Stanzl (2004).
The three assumptions also lead to testable predictions: lag-1 unconditional impact of
Bouchaud, Kockelkoren and Potters (2006), defined as : R(1) :=
(mt+1 −mt)· qt
®
t
, where
the empirical average 〈·〉t
is taken over all transactions of any volume. For any k > 0,
we can define the lag-k response function: R(k) = E
£
(mt+k −mt)· qt
¤
≡ 〈(mt+k −mt)· qt〉
t
,
which measures the information content of the current trade on the mid-price k trades
into the future. Defining the symbols a = (π1 − π2)
2
, b = 4π1π2 and λ = 1 − p12 − p21
where π1 =
p21
p12+p21
and the lag-k anti-correlation function: C(k) = a(k − 1) − b
³
1 −
1−λ
k
1−λ
´
,
for k > 0, with C(1) = 0, we find the following one-to-one relationships between lag-1
and lag-k response functions: R(1) =
1
1+C(k)
· R(k)
for k = 1, 2,.... Stochastic volatility over
k trades is the average: 1
k
Pk
`=1
£
∆pt+`
¤2
. The price difference between any two trades is
∆pt+1 ≈ ∆mt+1 u 2πst+1qt and we can approximate volatility over k trades by the empirical average: 1
k
Pk
`=1
E
£
∆pt+`
¤2
≈
4π
2 ×
¡
s
2
t+1 +σ
2
s
¢®
k
.
We use data from the Tokyo Commodity Exchange (TOCOM) for two of the most liquid commodity futures contracts: Gold Standard (TOCOM Product Code: 11, Bloomberg:
JGA ) and Platinum Standard (TOCOM Product Code: 13, Bloomberg: JAA), with the delivery month of February 2020, over the day-time trading session, from 8:45 a.m. to 3:15
p.m. Japanese Standard Time on the 24th April 2019. Each contract has a minimum price
increment of JPY 1 per gram. We estimate the model described in Chapter 1 and compute the response functions: R(1) to R(k) and run the regressions R(1) = α + β
1
1+C(k)
· R(k)
for k = 2, 3, 4, 5 and find that statistically α = 0 and β = 1 with R
2 ≥ 60% in all cases. For
stochastic volatility that at least 85% is explained by the update rule.
(III) A Double Mixture Autoregressive Model of Commodity Price Many commodity
prices exhibit boom–bust type behavior: sustained periods of price increases are followed
by sudden sharp collapses. Since around the year 2000, booms have become longer while
busts have tended to be short but steep, suggesting a structural change in growth and persistence. We model these features of the data using a novel double mixture autoregression
with two independent hidden Markov chains. One chain models shifts in mean growth
rates that accounts for rising and falling prices, while a second chain tracks changes in
volatility and lag-structure. While the two chains are independent, the persistence of price
growth depends on the volatility state, which allows the lag-structure to vary across variance regimes.
Let yt = ∆log(Pt) represent a time series of the change in the log price of a commodity.
Let S
m
t
and S
υ
t
represent, respectively, indicators of a mean and variance regime. Here S
υ
t =
{0, 1} captures volatility changes characterizing many commodity price series while S
m
t =
{0, 1} represents shifts in the growth rate related to price boom–bust cycles. The regimes
S
m
t
and S
υ
t
are each the outcome of an independent first order Markov chain with transition matrices: P
m =
£
p
m
j|i
¤
and P
υ =
£
p
υ
j|i
¤
,i, j = {0, 1}, respectively. The two components
correspond to a restricted four regime model, with state St = S
m
t ×S
υ
t
and transition matrix:
P
m ⊗P
υ
. The state St defines a dynamic linear model: yt = µS
m
t
+
P`S
υ
t
)
l=1
φS
υ
t
,l
³
yt−l −µS
m
t−l
´
+
σS
υ
t
et
, et ∼ i.i.d N(0, 1), with time varying intercepts µS
m
t
and volatilities σS
υ
t
. The lag
length `(S
υ
t
) is potentially changing across variance regimes. This is the first novelty in
the paper.
In the original model of Hamilton (1989), there are no volatility changes and the state
σ can be thought of as a nuisance parameter, in the sense of Sartori (2003) or Elliott, Müller
and Watson (2015), which we are not interested in. In the present context, we are interested in modeling the boom–bust related shifts in mean growth rates while treating the
change in volatility as incidental shift parameters in the sense of Neyman and Scott (1948,
Example 1). This conceptual approach allows us to form a profile likelihood and filtering technique that can be used to estimate the model in two stages. Initially, location
related parameters are estimated while suppressing the underlying autoregressive structure. These parameters are then held fixed while the optimal lag-structure across variance
regimes is determined.
We apply the model to three industrial commodities price time series: Crude Oil, Aluminium and Rubber. We find that in each case, the model captures boom and bust cycles, with data from more recent periods exhibiting higher volatility, longer price rallies
5
and steeper collapses. In order to show the models relevance for other applications in
macroeconomics such the identification by heteroskedasticity method of Lütkepohl and
Velinov (2016), we aggregate monthly frequency data to quarterly. This temporal aggregation allows us to show that the methods can be used for instance in a structural vector
autoregression that includes data only available at the quarterly frequency such as GDP.
This avoids using the more complex mixed frequency type models such as those advanced
by Christensen, Posch and Van Der Wel (2016).
Conclusion The analysis covers a variety of commodities at different observation frequencies or time scales. The first uses commodity price series at a daily frequency for periods of up to one year, approximately 252 days of market open to close futures prices from a
commodity exchange. The second paper uses high-frequency trade-by-trade or tick-level
data from a continuous trading session in a single day. Finally the third paper uses long
time series spanning decades. It is important to look at data from the microscopic(ticklevel) to the macroscopic(decades) time scale in order to obtain a holistic view of the behavior of prices. Over very short time periods, the tick size is the smallest movement over
any two prices and price changes can be viewed as random walks over a grid, with jumps
occurring at arbitrary times (Curato and Lillo, 2014). This calls for the modeling of the
microstructural features of the data such as the bid–ask spread; usually equal to half a
tick for highly liquid assets, and the sequence of buy–sell orders which may predict the
direction of short term price movements. At a coarser time-scale of months, quarters or
years, the microstructural issues can be dispensed with and a more traditional time series
approach used to describe price dynamics. While I separate the analysis based on the observation time scales, multi frequency models of price dynamics are possible, albeit with a
more complex structure to capture the volatility components at play over every time scale
(Calvet and Fisher, 2001, 2004)
Dynamika cen towarów w skali czasowej
No full textLink archiwalny https://depotuw.ceon.pl/handle/item/367
Income Inequality and Growth: Calibration and Simulation for the Kenyan Economy
No full textWe investigate the notable decline in wealth and income inequality in Kenya over the 10-year period between 2005 and 2015. Using a calibrated continuous time heterogeneous agent model, we attribute up to 92% of the variation in top wealth inequality to a persistent but slow increase in the return to capital, a low risk free rate, and rising “effective” income tax rates. Our study suggests that a macroeconomic environment characterized by low risk-free interest rates anchored by low debt-to fiscal revenue ratios are key to reducing both wealth and income inequality
A Poor Man’s Portfolio?
Full text linkOver the 10-year period between 2005 and 2015, Kenya experienced a significant decrease in both wealth and income inequality. This decline in inequality has been characterized by a fall in both the income and wealth shares of the POLICY BRIEF A Poor Man’s Portfolio? Gilbert Mbara July 2023 / No.GPIR-PB-CC003 richest members of the population, as well as modest gains for the poorest. This is contrary to what has been observed in many advanced countries where inequality has been on the rise. To understand why the Kenyan experience has been different, we investigate macroeconomic variables linked to top-end income and wealth inequality
Inégalité des Revenus et Croissance : Ajustement et Simulation pour l'Économie Kenyane
Full text linkNous étudions le déclin considérable des inégalités de richesse et de revenu au Kenya au cours de la période de 10 ans entre 2005 et 2015. En utilisant un modèle d'agents hétérogènes ajusté en temps continu, nous attribuons jusqu'à 92% de la variation de l'inégalité de la richesse au sommet à une augmentation persistante mais lente du rendement du capital, à un faible taux sans risque et à une augmentation des taux d'imposition sur le revenu « effectifs ». Notre étude suggère qu'un environnement macroéconomique caractérisé par de faibles taux d'intérêt sans risque, ancré par de faibles ratios dette/recettes fiscales, est important pour réduire les inégalités de richesse et de revenu
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