Hong Kong's growth synchronization with China and the US: A trend and cycle analysis

This paper investigates the synchronization of Hong Kong's economic growth with mainland China and the US. We identify trends of economic growth based on the permanent income hypothesis. Specifically, we first confirm whether real consumption in Hong Kong and mainland China satisfies the permanent income hypothesis, at least in a weak form. We then identify the permanent and transitory components of income of each economy using a simple state-space model. We use structural vector autoregression models to analyze how permanent and transitory shocks originating from mainland China and the US affect the Hong Kong economy, and how such influences evolve over time. Our main findings suggest that transitory shocks from the US remain a major driving force behind Hong Kong's business cycle fluctuations. On the other hand, permanent shocks from mainland China have a larger impact on Hong Kong's trend growth.     

Examining dynamic currency linkages amongst South Asian economies: An empirical study

In this paper, we examine the currency market linkages of South Asian member countries using daily data from 6 January 2004 to 31st March 2016. Time invariant and varying Copula GARCH models show that South Asian countries, except for India and Nepal/Bhutan, have low levels of currency market linkages which can be ascribed to poor levels of intra-regional trade intensity and portfolio flows. We reconfirm the copula results through Diebold and Yilmaz methodology and document that currency market connectedness is very limited in the South Asian region. The trends of the fundamental determinants of currency co-movements for the South Asian member countries were compared with its neighbouring regional economic bloc in Asia which has a much longer history and a wider membership base i.e ASEAN + 6. From a comparative analysis, it was found that South Asia member states have to work on their governance parameters, improve on their trade linkages and trade tariffs and work towards greater degree of capital account convertibility with adequate safeguards to achieve higher levels of currency market linkages.     

Pure jump models for pricing and hedging VIX derivatives

Recent non-parametric statistical analysis of high-frequency VIX data (Todorov and Tauchen, 2011) reveals that VIX dynamics is a pure jump semimartingale with infinite jump activity and infinite variation. To our best knowledge, existing models in the literature for pricing and hedging VIX derivatives do not have these features. This paper fills this gap by developing a novel class of parsimonious pure jump models with such features for VIX based on the additive time change technique proposed in Li et al., 2016a, Li et al., 2016b. We time change the 3/2 diffusion by a class of additive subordinators with infinite activity, yielding pure jump Markov semimartingales with infinite activity and infinite variation. These processes have time and state dependent jumps that are mean reverting and are able to capture stylized features of VIX. Our models take the initial term structure of VIX futures as input and are analytically tractable for pricing VIX futures and European options via eigenfunction expansions. Through calibration exercises, we show that our model is able to achieve excellent fit for the VIX implied volatility surface which typically exhibits very steep skews. Comparison to two other models in terms of calibration reveals that our model performs better both in-sample and out-of-sample. We explain the ability of our model to fit the volatility surface by evaluating the matching of moments implied from market VIX option prices. To hedge VIX options, we develop a dynamic strategy which minimizes instantaneous jump risk at each rebalancing time while controlling transaction cost. Its effectiveness is demonstrated through a simulation study on hedging Bermudan style VIX options.     

Going for gold: Investigating the (non)sense of increased advertising around major sports events

Major sports events draw unsurpassed media attention. Companies are motivated to increase their advertising investments around these events to reach large audiences in a short period. Is such an advertising surge actually beneficial though, or should companies avoid advertising in these periods because of negative effects of competitive interference? This study investigates when consumer packaged goods companies should invest in advertising to increase sales: before, during, or after the event or outside these event periods. The author estimates short- and long-term own- and cross-advertising elasticities for 206 brands using four years of weekly data. Although considerable heterogeneity exists across brands, own-advertising effectiveness diminishes especially before and during major sports events, in both the short and the long run. In addition, brands benefit less from category-demand effects through competitors' advertising. Conversely, greater increases in advertising spending resulting in significant growth in share of voice around focused, single-sport events are a successful strategy to overcome this overall general negative trend.     

Steady state adjusting trends using a data-driven local polynomial regression

Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence.     

Optimal dividend strategies with time-inconsistent preferences

This paper studies the optimal dividend strategies of an insurance company when the manager has time-inconsistent preferences. We consider the problem for a naive manager and a sophisticated manager, and analytically derive the optimal dividend strategies when claim sizes follow an exponential distribution. Our results show that the manager with time-inconsistent preferences tends to pay out dividends earlier than her time-consistent counterpart and that the sophisticated manager is more inclined to pay out dividends than the naive manager. Furthermore, we extend these results to the case with claim sizes following a mixed exponential distribution, and provide a numerical analysis to reveal the sensitivity of the optimal dividend strategies to changes in the premium, claims and surplus volatility.     

Are impatient farmers more risk-averse? Evidence from a lab-in-the-field experiment in rural Uganda

Based on data from a field-experiment in rural Uganda, we show that impatient farmers are more risk-averse than patient farmers. We relied on a simplified version of the Convex Time Budget (CTB) method to elicit farmers’ time preferences and on an independent method for eliciting their risk-preferences. We report two important findings. First, we show that our simplified CTB method applied to farmers from Uganda replicates the key findings of Andreoni and Sprenger’s lab experiments that involved student subjects. Second, we establish the existence of a negative correlation between risk tolerance and impatience, based on two independent measures.     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

Understanding on-the-go consumption: A retail mix perspective

On-the-go (OTG) consumption is a growing phenomenon in the food and beverage industry. Drawing on the theory of consumption values, this study is conducted to acquire a better understanding of the influence that retail-mix elements have on OTG consumption outcomes. Specifically, this study examines the role of value for money, customer service, general assortments and healthy assortments in driving intention and satisfaction related to OTG consumption. The study also assesses the conditional value generated by the consumer's health orientation, impulsiveness and perceived time pressure. Based on a sample of 433 OTG consumers, and applying multi-group structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), value for money was identified as the key driver of OTG consumption outcomes. Impulsiveness and time pressure are shown to increase the effects of value for money perceptions on OTG consumption intention, while health orientation enhances the relationship between customer service and satisfaction. This study contributes to the literature on OTG consumption, explains the managerial implications for retailers, and offers recommendations to target OTG consumers better.     

Markov-switching dynamic factor models in real time

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.