Catching-up and Leapfrogging in Key Manufacturing Sectors

Chapter 6 assesses China’s catching-up and leapfrogging in key manufacturing sectors compared with the Korean experience. It explains the varying records of market catch-up by referring to diverse aspects of technological and market regimes, such as modularity, degrees of embodied technical change, tacitness of knowledge, knowledge accessibility, and frequency of innovations. Easy access to foreign technologies from developed countries (mobile phones vs. semiconductors), high degree of modularity (mobile phones vs. automobiles and semiconductors), and frequent changes in the generations of technologies or short cycle times of technologies (mobile phones and telecommunications systems vs. automobiles) generally help latecomers catch up. More importantly, sectors with a high degree of tacit knowledge (e.g., automobiles) tend to show a slower speed of catch-up than the manufacturers of telecommunications equipment with a high degree of explicit knowledge. Whether markets feature segmentation (or the existence of low-end niche segments for Chinese latecomers) seems to play an important role in the market regimes. Chinese firms manage to achieve initial success from a low-end market in segmented market conditions (e.g., telecommunications equipment and mobile phones) or markets protected by the government (e.g., telecommunications equipment). Conversely, they face high entry barriers in markets with no such segmentation (e.g., memory chips), which is one of the reasons for their slow progress in the memory chip sector (see also Chapter 4). These cases also suggest that technological regimes are not the only paramount determining factor; the outcomes are affected by the roles of actors, including firms and governments.

Equity Risk and Return across Hidden Market Regimes

The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, its functioning regime allows us to reveal the logic of its development. The article employs the concept of financial turbulence to identify hidden market regimes. These are revealed through the ratio of the components, which describe single changes of correlated risks and volatility. The combinations of typical and atypical variates of correlational and magnitude components of financial turbulence allowed four hidden regimes to be revealed. These were arranged by the degree of financial turbulence, conceptually analyzed and assessed from the perspective of their duration. The empirical data demonstrated ETF day trading profits for S&P 500 sectors, covering the period of January 1998–August 2020, as well as day trade profits of the Russian blue chips within the period of October 2006–February 2021. The results show a significant difference in regard to the market performance and volatility, which depend on hidden regimes. Both sample data groups demonstrated similar contemporaneous and lagged effects, which allows the prediction of volatility jumps in the periods following atypical correlations.

Investigating the Dynamics of Exchange Traded Funds Across the Bear and Bull Markets: Evidence from Indian Equity ETFs

The present study attempts to examine the tracking ability of Indian equity exchange traded funds (ETFs) across the bearish and bullish market regimes. Also, ETFs’ sensitivity to their respective underlying indices across the two market conditions is examined so as to gain an insight into the differences in risk exposure under the two regimes using DBM. The results found that the tracking error (TE) of ETFs varies across the two market regimes with it higher during the bullish regime. At the same time, ETFs’ responsiveness to their underlying indices is found to be higher during the bearish market regime, which justifies the existence of lower TE during the bearish regime. NIFTYBEES, KOTAKNIFTY and BANKBEES emerged to be the top three performers in terms of tracking efficiency. Further, NIFTYBEES, BANKBEES and JUNIORBEES are reported to provide significantly positive excess returns during the bullish regime. As such, investors considering investment in equity ETFs can opt for the top performing funds where they also stand a chance to earn excess return (in few cases). Also, it is observed the beta coefficients of ETFs varied significantly from unity. It suggests that the ETFs and their respective underlying indices are not subject to similar systematic risk.

What to do when the robots come? Non-formal education in jobs affected by automation

PurposeOngoing automation processes may render a fair share of the existing jobs redundant or change their nature. This begs the question to what extent employees affected invest in training in order to strengthen their labour market position in times of uncertainty. Given the different national labour market regimes and institutions, there may be an important geographical dimension to the opportunities to cope with the challenges set by automation. The purpose of this study is to address both issues.Design/methodology/approachUsing data from the 2016 European labour Force Survey, the authors estimate with logit and multi-level regression analyses how the automation risk of a worker's job is associated with the propensity of following non-formal education/training. The authors allow this relationship to vary across European countries.FindingsThe results show that employees in jobs vulnerable to automation invest relatively little in training. Also, there are significant differences across Europe in both the provision of training in general and the effect of automation on training provision.Originality/valueWhile there is quite a lot of research on the structural labour market effects of automation, relatively little is known about the actions that employees take to deal with the uncertainty they are faced with. This article aims to contribute to our understanding of such mechanisms underlying the structural macro-level labour-market dynamics.

Defining and measuring portfolio diversification

It is often said that diversification is the only ‘free lunch’ available to investors; meaning that a properly diversified portfolio reduces total risk without necessarily sacrificing expected return. However, achieving true diversification is easier said than done, especially when we do not fully know what we mean when we are talking about diversification. While the qualitative purpose of diversification is well known, a satisfactory quantitative definition of portfolio diversification remains elusive. In this research, we summarise a wide range of diversification measures, focusing our efforts on those most commonly used in practice. We categorise each measure based on which portfolio aspect it focuses on: cardinality, weights, returns, risk or higher moments. We then apply these measures to a range of South African equity indices, thus giving a diagnostic review of historical local equity diversification and, perhaps more importantly, providing a description of the investable opportunity set available tofund managers in this space. Finally, we introduce the idea of diversification profiles. These regimedependent profiles give a much richer description of portfolio diversification than their single-value counterparts and also allow one to manage diversification proactively based on one’s view of future market conditions. Keywords: Portfolio diversification; index concentration; weight-based diversification; risk-based diversification; correlation; covariance; market regimes

The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of their Determinants

This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit risk—rather than interest rate movements—contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.

On Volatility, Outliers, and Uncertainty

This dissertation is composed of three loosely related chapters, all of which are empirical.In Chapter 1, I examine whether expectations are formed in a systematically different manner during periods of low volatility versus periods of high volatility. I achieve this by measuring non-linearities in relationship between the SP 500 and the VIX across different market regimes. Three distinct market regimes are identified through a Markov Process, allowing for the capture of non-constant behavior in the relationship between contemporaneous price changes and future volatility expectations. The results indicate that the effect of the underlying asset on the supply and demand dynamics of its derivative is strongest during periods of low volatility and weakest during periods of high volatility. The decrease in magnitude of the SP 500 coefficient as the market switches from low volatility to high, suggests that information scarcity (low volatility) makes additional data (price changes) more impactful. Measures to limit market volatility may make market participant prone to expect changes in the state of the system. The purpose of Chapter 2 is to draw inference from the tail behavior of financial market price volatility in order to compare and contrast volatility expectations with volatility realizations. In doing so, I discuss the implications of slowly decaying tails as they relate to systems susceptible to unpredictable and consequential events. In such cases where fat tails are identified, typical values such as the average and variance, do not properly characterize the risk and unpredictability of the dynamic process under study. Prior research has identified asset prices and asset volatility as being drawn from a power law distribution. This paper aims to quantitatively confirm this characterization, specifically for market volatility. Further, this paper identifies whether or not volatility expectations exhibit similar power law characteristics. Goodness of fit and log likelihood tests indicate that most realized volatility series are plausibly drawn from a power- law distribution. However, none of the studied implied volatility series show evidence of power-law behavior, suggesting that risk premia may exist for lower levels of volatility but does not scale proportionally to the more extreme crisis events. That is, risk premia does not scale proportionally as values move farther into the tail. In Chapter 3, co-authored with Minh Pham, we investigate how economic uncertainty, specifically stock market uncertainty, correlates to individuals' life-satisfaction. Using expected price volatility (VIX) as our anticipatory indicator and life-satisfaction as our measure of utility, our hypothesis is built on the Anticipatory Utility framework, which suggests that people also derive utility from their beliefs. After accounting for associations with the unemployment rate and stock ownership, we find a positive relationship between the VIX and low self-reported life- satisfaction. This analysis captures the contemporaneous effects of future beliefs and indicates that economic sentiment about the future plays an important role in individuals' feelings about the present. This work was inspired by a desire to understand the economic crises that redirect and ultimately redefine our socioeconomic lives, as individuals and as nations. I began my economic studies during one of the most profound crises in recent history, the global financial crisis of the late 2000s. Here again in 2021, as my studies conclude, economies grapple with another, albeit different crisis. Both the Covid-19 pandemic and the subprime financial crisis highlight a salient fact; we never really know when, why, or from where such extreme events arrive. But they do, and do so more frequently than we like or predict. Each of the chapters presented in this dissertation seek to understand the ways in which we anticipate and interact with a characteristic marker of economic and financial crises, uncertainty.