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on Financial Markets |
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Issue of 2021‒07‒12
eight papers chosen by |
| By: | Karamfil Todorov |
| Abstract: | This paper studies exchange-traded funds’ (ETFs) price impact in the most ETF-dominated asset classes: volatility (VIX) and commodities. I propose a modelindependent approach to replicate the VIX futures contract. This allows me to isolate a non-fundamental component in VIX futures prices that is strongly related to the rebalancing of ETFs. To understand the source of that component, I decompose trading demand from ETFs into three parts: leverage rebalancing, calendar rebalancing, and flow rebalancing. Leverage rebalancing has the largest effects. It amplifies price changes and exposes ETF counterparties negatively to variance. |
| Keywords: | ETF, leverage, commoditization, VIX, futures |
| JEL: | G11 G13 G23 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:952&r= |
| By: | Lu, Wenna (Cardiff Metropolitan University); Copeland, Laurence (Cardiff Business School, Cardiff University); Xu, Yongdeng (Cardiff Business School) |
| Abstract: | Many recent papers have investigated the role played by volatility in determining the cross-section of currency returns. This paper employs two time-varying factor models: a threshold model and a Markov-switching model to price the excess returns from the currency carry trade. We show that the importance of volatility depends on whether the currency markets are unexpectedly volatile. Volatility innovations during relatively tranquil periods are largely unrewarded in the market, whereas during the volatile period, this risk, has a substantial impact on currency returns. The empirical results show that the two time-varying factor models fit the data better and generate a smaller pricing errors than the linear model, while the Markov-switching model outperforms the threshold factor models not only by generating lower pricing errors but also distinguishing two regimes endogenously and without any predetermined state variables. |
| Keywords: | carry trade; asset pricing; trading strategies; currency portfolios; Markov-switching model |
| JEL: | F3 G12 G15 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2021/16&r= |
| By: | Prehn, Sören |
| Abstract: | Nowadays it is widely accepted to estimate minimum variance hedge ratio regressions in first differences. There are both statistical and economic reasons for a first difference approach. However, no study has ever analyzed whether the first difference approach is also consistent with the theory of minimum variance hedging. In this paper we show, on the basis of a simulation study, that the first difference model with intercept does not provide hedge ratio estimates that are in line with the theory of minimum variance hedging. Only a linear regression model in levels provides theoretically consistent results. |
| Keywords: | Agricultural Finance, Research Methods/ Statistical Methods |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aesc21:312051&r= |
| By: | Melisso Boschi (Senate of the Republic of Italy); Stefano d'Addona (University of Rome 3); Aditya Goenka (University of Birmingham) |
| Abstract: | A class of asset pricing models with external habit formation imply a nonlinear relationship between the counter-cyclical equity premium and the surplus consumption over the business cycle. The effect of a shock to surplus consumption on the equity premium will be asymmetric in a boom versus a recession. We test this using a novel approach to the estimation of a time-varying VAR model of the U.S. postwar economy where parameters are conditional on Markov-switching regimes associated to the business cycle phases. We estimate the regime-dependent impulse response functions and show that the equity premium increases following a negative shock to the surplus consumption either in boom or in recession. The response in recession is significantly larger than in boom. These results provide evidence in favor of the external habit formation hypothesis. |
| Keywords: | Habit formation; Equity premium; Business cycle; Markov-switching models; Time-varying VAR; Regime-dependent impulse response functions |
| JEL: | E21 E32 E44 G11 G12 |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:bir:birmec:21-11&r= |
| By: | Andrew Paskaramoorthy; Tim Gebbie; Terence van Zyl |
| Abstract: | Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance. Here, we consider the performance of various shrinkage methods by their efficient frontiers under different distributional assumptions to study the impact of reasonable departures from Normality. Namely, we investigate the impact of first-order auto-correlation, second-order auto-correlation, skewness, and excess kurtosis. We show that the shrinkage methods tend to re-scale the sample efficient frontier, which can change based on the nature of local perturbations from Normality. This re-scaling implies that the standard approach of comparing decision rules for a fixed level of risk aversion is problematic, and more so in a dynamic market setting. Our results suggest that comparing efficient frontiers has serious implications which oppose the prevailing thinking in the literature. Namely, that sample estimators out-perform Stein type estimators of the mean, and that improving the prediction of the covariance has greater importance than improving that of the means. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.10491&r= |
| By: | Christopher L. Culp; Mihir Gandhi; Yoshio Nozawa; Pietro Veronesi |
| Abstract: | We propose implied spreads (IS) and normalized implied spreads (NIS) as simple measures to characterize option prices. IS is the credit spread of an option’s implied bond, the portfolio long a risk-free bond and short a put option. NIS normalizes IS by the risk-neutral default probability and reflects tail risk. IS and NIS are countercyclical and predict implied bond returns, while neither, like implied volatility, predicts put returns. These opposite predictability results are consistent with a stochastic volatility, stochastic jump intensity model, as put premia increase in volatility but decrease in jump intensity, while implied bond premia increase in both. |
| JEL: | G12 G13 |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28941&r= |
| By: | Marina-Eliza Spaliara; Serafeim Tsoukas; Paul Lavery |
| Abstract: | Using firm-level data from 16 euro-area countries over 2008-2014, we investigate how the growth and investment of bank-affiliated private equity-backed companies evolve after the European Banking Authority (EBA) increases capital requirements for their parent banks. We find that portfolio companies connected to affected banks reduce their investment, asset growth, and employment growth following the capital exercise. We further show that the effect is stronger for companies likely to face financial constraints. Finally, the findings indicate that the negative effect of the capital exercise is muted when the private equity sponsor is more experienced. |
| Keywords: | Private equity buyouts; bank capital requirements; financial constraints; company performance |
| JEL: | G32 G34 |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:gla:glaewp:2021_11&r= |
| By: | Marzagão, Thiago |
| Abstract: | How much insider trading happens in Brazil’s stock market? Previous research has used the model proposed by Easley et al. [1996] to estimate the probability of insider trading (PIN) for different stocks in Brazil. Those estimates have a number of problems: i) they are based on a factorization that biases the PIN downward, especially for high-activity stocks; ii) they fail to account for boundary solutions, which biases most PIN estimates upward (and a few of them downward); and iii) they are a decade old and therefore based on a very different market (for instance, the number of retail investors grew from 600 thousand in 2011 to 3.5 million in 2021). In this paper I address those three problems and estimate the probability of insider trading for 431 different stocks in the Brazilian stock market, for each quarter from October 2019 to March 2021. |
| Date: | 2021–06–18 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:fu9mg&r= |