TSFDC: A trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk‐adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC‐based trading strategy.     

Estimating daily volatility in financial markets utilizing intraday data

This study proposes a new approach to the estimation of daily realised volatility in financial markets from intraday data. Initially, an examination of intraday returns on S&P 500 Index Futures reveals that returns can be characterised by heteroscedasticity and time-varying autocorrelation. After reviewing a number of daily realised volatility estimators cited in the literature, it is concluded that these estimators are based upon a number of restrictive assumptions in regard to the data generating process for intraday returns. We use a weak set of assumptions about the data generating process for intraday returns, including transaction returns, given in den Haan and Levin [den Haan, W.J., Levin, A., 1996. Inferences from parametric and non-parametric covariance matrix estimation procedures, Working paper, NBER, 195.], which allows for heteroscedasticity and time-varying autocorrelation in intraday returns. These assumptions allow the VARHAC estimator to be employed in the estimation of daily realised volatility. An empirical analysis of the VARHAC daily volatility estimator employing intraday transaction returns concludes that this estimator performs well in comparison to other estimators cited in the literature.     

Forecasting implied volatility in foreign exchange markets: a functional time series approach

We utilise novel functional time series (FTS) techniques to characterise and forecast implied volatility in foreign exchange markets. In particular, we examine the daily implied volatility curves of FX options, namely; Euro/United States Dollar, Euro/British Pound, and Euro/Japanese Yen. The FTS model is shown to produce both realistic and plausible implied volatility shapes that closely match empirical data during the volatile 2006–2013 period. Furthermore, the FTS model significantly outperforms implied volatility forecasts produced by traditionally employed parametric models. The evaluation is performed under both in-sample and out-of-sample testing frameworks with our findings shown to be robust across various currencies, moneyness segments, contract maturities, forecasting horizons, and out-of-sample window lengths. The economic significance of the results is highlighted through the implementation of a simple trading strategy.     

The relation between implied and realised volatility in the Danish option and equity markets

We show that the conclusions to be drawn concerning the informational efficiency of illiquid options markets depend critically on whether one carefully recognises and appropriately deals with the econometrics of the errors‐in‐variables problem. This paper examines the information content of options on the Danish KFX share index. We consider the relation between the volatility implied in an option's price and the subsequently realised index return volatility. Since these options are traded infrequently and in low volumes, the errors‐in‐variables problem is potentially large. We address the problem directly using instrumental variables techniques. We find that when measurement errors are controlled for, call option prices even in this very illiquid market contain information about future realised volatility over and above the information contained in historical volatility.     

Causality in volatility and volatility spillover effects between US, Japan and four equity markets in the South China Growth Triangular

In examining co-movement across international stock markets, previous researchers usually pre-determine the direction of causation and neglect the Chinese equity markets. In this study, we examine the spillover effects of volatility among the two developed markets and four emerging markets in the South China Growth Triangular using Chueng and Ng's causality-in-variance test. Several findings deserve mention: (1) the Japanese stock market affects the US stock market and there is a feedback relationship between the Hong Kong and US stock market. (2) Markets of the SCGT are contemporaneously correlated with the return volatility of the US market. (3) Econometric models constructed according to the results of variance-in-causality tests have greater explanatory power than the conventional GARCH(1,1) model. (4) Using the return volatility of foreign exchange as a proxy for informational arrival can explain excess kurtosis of a stock return series, especially for the less open emerging market. (5) Geographic proximity and economic ties do not necessarily lead to a strong relationship in volatility across markets.     

Precious metals under the microscope: a high-frequency analysis

Taking advantage of a trades-and-quotes high-frequency database, we document the main stylized facts and dynamic properties of spot precious metals, i.e. gold, silver, palladium and platinum. We analyse the behaviours of spot prices, returns, volume and selected liquidity measures. We find clear evidence of periodic patterns matching the trading hours of the most active markets round-the-clock. The time series of spot returns have, thus, properties similar to those of traditional financial assets with fat tails, asymmetry, periodic behaviours in the conditional variances and volatility clustering. Gold (platinum) is the most (least) liquid and least (most) volatile asset. Commonality in liquidities of precious metals is very strong.     

Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk

In this research, we investigate the impact of stochastic volatility and interest rates on counterparty credit risk (CCR) for FX derivatives. To achieve this we analyse two real-life cases in which the market conditions are different, namely during the 2008 credit crisis where risks are high and a period after the crisis in 2014, where volatility levels are low. The Heston model is extended by adding two Hull–White components which are calibrated to fit the EURUSD volatility surfaces. We then present future exposure profiles and credit value adjustments (CVAs) for plain vanilla cross-currency swaps (CCYS), barrier and American options and compare the different results when Heston-Hull–White-Hull–White or Black–Scholes dynamics are assumed. It is observed that the stochastic volatility has a significant impact on all the derivatives. For CCYS, some of the impact can be reduced by allowing for time-dependent variance. We further confirmed that Barrier options exposure and CVA is highly sensitive to volatility dynamics and that American options’ risk dynamics are significantly affected by the uncertainty in the interest rates.     

Combining long memory and level shifts in modelling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.     

The Processing of Non-Anticipated Information in Financial Markets: Analyzing the Impact of Surprises in the Employment Report

This paper delineates the simultaneous impact of non-anticipated information on mean and variance of the intraday return process by including appropriate variables accounting for the news flow into both the mean and the variance function. This allows us to differentiate between the consistent price reaction to surprising news and the traders' uncertainty about the precise price impact of this information. Focussing on the US employment report, we find that headline information is almost instantaneously incorporated into T-bond futures prices. Nevertheless, large surprises, and `bad' news in particular, create considerable uncertainty. In contrast, if surprises in related headlines cross-validate each other, less room for differences of opinion is left and hence volatility is decreased.     

The risk-based approach under the new EU data protection regulation: a critical perspective

Abstract

The first broad reform of personal data protection legislation in the European Union entered into force in May 2018 (Regulation (EU) 2016/679, the General Data Protection Regulation). Remarkably, with this reform a risk-based approach has been introduced as the core data protection enforcement model, while data protection authorities see their regulatory role significantly weakened. The risk-based approach is to be implemented by the data controllers (i.e. the operators) via data protection impact assessments (evoking the established environmental impact assessment procedure) and notification of breaches, among other procedures. Hence the scope of both the concepts of risk and risk regulation spread beyond conventional domains, namely the environment, public health or safety, i.e. physical risks, to encompass risks to intangible values, i.e. individual rights and freedoms, presumably harder to assess and manage. Strikingly, the reform has been accompanied by a confident discourse by EU institutions, and their avowed belief in the reform’s ability to safeguard the fundamental right to data protection in the face of evolving data processing techniques, specifically, big data, the Internet of Things, and related algorithmic decision-making. However, one may wonder whether there isn’t cause for concern in view of the way the risk-based approach has been designed in the data protection legislation. In this article, the risk-based approach to data protection is analysed in the light of the reform’s underlying rationality. Comparison with the risk regulatory experience in environmental law, particularly the environmental impact assessment procedure, is drawn upon to assist us in pondering the shortcomings, as well as the opportunities of the novel risk-based approach.     

Does the Exchange Rate Respond to Monetary Policy in Mexico? Solving an Exchange Rate Puzzle in Emerging Markets

This paper argues that the null or weak response of emerging market currencies to domestic monetary policy documented in the literature is the result of wide event windows. An event study with intraday data for Mexico shows that an unanticipated tightening appreciates the currency and flattens the yield curve, consistent with the evidence for advanced economies. With daily event windows, however, only the yield curve responds to monetary policy. Noise in daily exchange rate returns explains the lack of response of the currency. Such noise gives rise to a bias that declines after controlling for potential omitted variables.     

Pricing of guaranteed minimum withdrawal benefits in variable annuities under stochastic volatility,stochastic interest rates and stochastic mortality via the componentwise splitting method

This paper values guaranteed minimum withdrawal benefit (GMWB) riders embedded in variable annuities assuming that the underlying fund dynamics evolve under the influence of stochastic interest rates, stochastic volatility, stochastic mortality and equity risk. The valuation problem is formulated as a partial differential equation (PDE) which is solved numerically by employing the operator splitting method. Sensitivity analysis of the fair guarantee fee is performed with respect to various model parameters. We find that (i) the fair insurance fee charged by the product provider is an increasing function of the withdrawal rate; (ii) the GMWB price is higher when stochastic interest rates and volatility are incorporated in the model, compared to the case of static interest rates and volatility; (iii) the GMWB price behaves non-monotonically with changing volatility of variance parameter; (iv) the fair fee increases with increasing volatility of interest rates parameter, and increasing correlation between the underlying fund and the interest rates; (v) the fair fee increases when the speed of mean-reversion of stochastic volatility or the average long-term volatility increases; (vi) the GMWB fee decreases when the speed of mean-reversion of stochastic interest rates or the average long-term interest rates increase. We investigate both static and dynamic (optimal) policyholder's withdrawal behaviours; we present the optimal withdrawal schedule as a function of the withdrawal account and the investment account for varying volatility and interest rates. When incorporating stochastic mortality, we find that its impact on the fair guarantee fee is rather small. Our results demonstrate the importance of correct quantification of risks embedded in GMWBs and provide guidance to product providers on optimal hedging of various risks associated with the contract.     

Explaining the (non-) adoption of advanced data analytics in auditing: A process theory

Audit firms are increasingly engaging with advanced data analytics to improve the efficiency and effectiveness of external audits through the automation of audit work and obtaining a better understanding of the client’s business risk and thus their own audit risk. This paper examines the process by which audit firms adopt advanced data analytics, which has been left unaddressed by previous research. We derive a process theory from expert interviews which describes the activities within the process and the organizational units involved. It further describes how the adoption process is affected by technological, organizational and environmental contextual factors. Our work contributes to the extent body of research on technology adoption in auditing by using a previously unused theoretical perspective, and contextualizing known factors of technology adoption. The findings presented in this paper emphasize the importance of technological capabilities of audit firms for the adoption of advanced data analytics; technological capabilities within audit teams can be leveraged to support both the ideation of possible use cases for advanced data analytics, as well as the diffusion of solutions into practice.     

智慧税警协作下的涉税犯罪治理研究

我国涉税犯罪持续高发,依靠传统侦控方式发现和查处的难度不断加大,因而有必要对其新变化及特点进行梳理与总结.分析近年来的犯罪数据及典型案例可知,涉税犯罪的分工组织严密、职业化色彩明显,采用非接触方式从事犯罪活动日益增多,涉税犯罪案件中多种犯罪行为相互交织.涉税犯罪治理面临案件发现受理难度较大、传统侦控难以应对职业化犯罪、违法犯罪的法律规制不足等问题.涉税犯罪治理应从模式和方法上寻求突破,搭建多部门协同共建共治的格局,以智慧税警协作为主线,运用数据导侦及信息研判,实现涉税犯罪的智能防控和精准治理.     

我国商业银行的效率现状及生产率变动分析

本文运用数据包络分析方法和Malmquist指数对我国28家三类商业银行2000～2004年的技术效率、纯技术效率和规模效率以及全要素生产率变动进行了测算。结果发现,三类商业性银行的效率存在差异:国有商业银行的规模效率最低,并且呈规模报酬递减;股份制商业银行效率略高于国有商业银行;城市商业银行效率由相对最低转为相对最高。总体上看,银行业全要素生产率呈上升趋势,技术效率呈下降趋势,规模效率无明显变化。本文的主要结论是:规模是影响国有商业银行和股份制商业银行效率差异的主要因素,银行全要素生产率的变动受信息技术发展的推动以及宏观环境的影响。