Market risk and Bitcoin returns

Energy price volatility affects decisions to purchase energy using capital: Motor vehicles

Regime specific spillovers across US sectors and the role of oil price volatility

Forecasting stock market in high and low volatility periods: a modified multifractal volatility approach

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.

This paper investigates the switching effect of COVID-19 pandemic and economic policy uncertainty on commodity prices. We employ Markov regime-switching dynamic model to explore price regime dynamics of eight widely traded commodities namely oil, natural gas, corn, soybeans, silver, gold, copper, and steel. We fit two Markov switching regimes to allow parameters to respond to both low and high volatilities. The empirical evidence shows oil, natural gas, corn, soybean, silver, gold, copper, and steel returns adjust to shocks in COVID-19 outcomes and economic policy uncertainty at varying degrees––in both low volatility and high volatility regimes. In contrast, oil and natural gas do not respond to changes in COVID-19 deaths in both regimes. The findings show most commodities are responsive to historical price in terms of demand and supply in both volatility regimes. Our findings further show a high probability that commodity prices will remain in low volatility regime than in high volatility regime––owing to COVID-19-attributed market uncertainties. These findings are useful to both investors and policymakers––as precious metals and agricultural commodities show less negative response to exogenous variables. Thus, investors and portfolio managers could use precious metals, viz. Gold for short-term cover against systematic risks in the market during the period of global pandemic.

This study proposes a novel approach that incorporates rolling‐window estimation and a quantile causality test. Using this approach, Google Trends and Bitcoin price data are used to empirically investigate the time‐varying quantile causality between investor attention and Bitcoin returns. The results show that the parameters of the causality tests are unstable during the sample period. The results also show strong evidence of quantile‐ and time‐varying causality between investor attention and Bitcoin returns. Specifically, our results show that causality appears only in high volatility periods within the time domain, and causality presents various patterns across quantiles within the quantile domain.

Intelligent forecasting in bitcoin markets

Investor Herds and Regime-Switching: Evidence from Gulf Arab Stock Markets

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.

The cryptocurrencies are digital currencies that were initially designated to replace the old ones. However, they act as investment assets and many treat them like stocks. The market for cryptocurrencies counts more than 1600 types and the Bitcoin is the first and foremost of all of them. In one year the price of Bitcoin grew staggering 2000 percent. Other currencies have not seen this type of rising. This study investigates the period of over 4 years of data for 5 cryptocurrencies, the three years before and the last year of hyper-growth of the Bitcoin. We used GARCH model to see if two periods of data may offer some incites for patterns. The findings of this paper show that the Bitcoin in the period of high volatility is more diverged from its counterparts. According to results in the period of high volatility, the factors that influence the price formation of cryptocurrencies may not be the same for all of them. Using the price of the Bitcoin of one day earlier and the price of altcoins today we find spillover effect. Spillover effect is less prominent in the second period of high volatility. It may indicate of relatively independent nature of altcoins during the periods of high volatility.

In the current market, municipal bonds offer attractive after-tax yields combined with high credit quality and low price volatility, but investors' heavy use of munis in short-term tactical allocations (such as crossover trades) has added volatility to the market in recent years. Given the probability of yield-curve flattening and a return to a more normal short-term/long-term yield spread, investors should consider a barbell strategy in which the portfolio is divided approximately equally between long-term and short-term maturities. Investors also should consider the implications of the alternative minimum tax, which will affect an increasing percentage of taxpayers if the tax code is not repealed or modified.

Economic volatility, banks’ risk accumulation and systemic risk

Potential implications of the financial transaction tax for the market and investors on the Warsaw Stock Exchange. Purpose: The main aim of the chapter was to present the idea and the current state of work on the financial transaction tax (FTT) at the national and EU level. Moreover, in the empirical part, it was decided to estimate the burden of this tax on investors on the Warsaw Stock Exchange in order to assess the legitimacy of introducing FTT from the point of view of the domestic capital market. Design/methodology/approach: The research method used in this chapter is the review of the literature and legal acts concerning the financial transaction tax (FTT). In the empirical part, the tax burden on investors on the Warsaw Stock Exchange (WSE) was estimated. Additionally, based on data collected from the WSE and the tax rate proposed in the 2015 Polish FTT project, a simulation of the state budget revenues from this tax was carried out. Findings: The amount of the state budget revenues from the financial transaction tax on the Warsaw Stock Exchange estimated in the chapter is between PLN 416.1 and 654.4 million. The estimates were made taking into account the stock exchange turnover in 2015–2020 and assuming the tax burden only on individual investors trading instruments on their own behalf. The introduction of the tax with the rate proposed in the 2015 FTT project was assessed negatively, taking into account the further development of the Polish capital market and the competitiveness of GPW S.A. compared to other European exchanges. In the event of a possible implementation of the FTT in the future, it is the amount of the tax rate that is important. FTT should be introduced with a lower rate than the proposed one, and bearing in mind the time—tax should be proposed in the period of high volume and low volatility on the WSE. Thereafter, the tax rate could be increased if turnover and volatility were not distorted.

Long range financial data as typified by the daily returns of the Standard and Poor's index exhibit common features such as heavy tails, long range memory of the absolute values and clustering of periods of high and low volatility. These and other features are often referred to as stylized facts and parametric models for such data are required to reproduce them in some sense. Typically this is done by simulating some data sets under the model and demonstrating that the simulations also exhibits the stylized facts. Nevertheless when the parameters of such models are to be estimated recourse is very often taken to likelihood either in the form of maximum likelihood or Bayes. In this paper we expound a method of determining parameter values which depends solely on the ability of the model to reproduce the relevant features of the data set. We introduce a new measure of the volatility of the volatility and show how it can be combined with the distribution of the returns and the autocorrelation of the absolute returns to determine parameter values. We also give a parametric model for such data and show that it can reproduce the required features.

Modelling bursts and chaos regularization in credit risk with a deterministic nonlinear model