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    In a settlement agreement with the British Columbia Securities Commission (BCSC), HSBC Investment Funds (Canada) Inc. (HIFC) and HSBC Private Wealth Services (Canada) Inc. (HPWS) have agreed they charged excess fees in some client accounts.read more...

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    The MIAX Options Exchange is pleased to announce that beginning with the June 2016 billing period, MIAX is introducing the following new reports/transmissions:read more...

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    Following the approach of standard filtering theory, we analyse investor-valuation of firms, when these are modelled as geometric-Brownian state processes that are privately and partially observed, at random (Poisson) times, by agents. Tasked with disclosing forecast values, agents are able purposefully to withhold their observations; explicit filtering formulas are derived for downgrading the valuations in the absence of disclosures. The analysis is conducted for both a solitary firm and m co-dependent firms.

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    We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for time series data. Our concept is realized as the algorithm of empirical mode decomposition and intrinsic time scale decomposition and it is subsequently used for preliminary analysis on the real time series data.

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    We introduce a novel method to estimate the discount curve from market quotes based on the Moore-Penrose pseudoinverse such that 1) the market quotes are exactly replicated, 2) the curve has maximal smoothness, 3) no ad hoc interpolation is needed, and 4) no numerical root-finding algorithms are required. We provide a full theoretical framework as well as practical applications for both single-curve and multi-curve estimation.

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    The goal of the paper is to introduce a set of problems which we call mean field games of timing. We motivate the formulation by a dynamic model of bank run in a continuous-time setting. We briefly review the economic and game theoretic contributions at the root of our effort, and we develop a mathematical theory for continuous-time stochastic games where the strategic decisions of the players are merely choices of times at which they leave the game, and the interaction between the strategic players is of a mean field nature.

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    This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support our theoretical explanation through an analysis of large price fluctuations in the S&P 500 and the resulting effects on implied and realized volatility. These results indicate that asymmetry (a negative relationship) between shocks and volatility in the subsequent period indeed exists. Moreover, contrary to previous research, our empirical tests also suggest that implied volatility is not simply an upward biased predictor of future deviation compensating for the variance of the volatility but rather, due to investors systematic anchoring to losses and gains in their volatility forecasts, it is a co-integrated yet asymmetric over/under estimated financial instrument. We also provide results indicating that the medium-term implied volatility (measured by the VIX Index) is an unbiased though inefficient estimation of realized volatility, while in contrast, the short-term volatility (measured by the recently introduced VXST Index representing the 9-day implied volatility) is also unbiased and yet efficient.

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    When banks extend loans to each other, they generate a negative externality in the form of systemic risk. They create a network of interbank exposures by which they expose other banks to potential insolvency cascades. In this paper, we show how a regulator can use information about the financial network to devise a transaction-specific tax based on a network centrality measure that captures systemic importance. Since different transactions have different impact on creating systemic risk, they are taxed differently. We call this tax a Systemic Risk Tax (SRT). We show that this SRT induces a unique equilibrium matching of lenders and borrowers that is systemic-risk efficient, i.e. it minimizes systemic risk given a certain transaction volume. On the other hand, we show that without this SRT multiple equilibrium matchings can exist and are generally inefficient. This allows the regulator to effectively `rewire' the equilibrium interbank network so as to make it more resilient to insolvency cascades, without sacrificing transaction volume. Moreover, we show that a standard financial transaction tax (e.g. a Tobin-like tax) has no impact on reshaping the equilibrium financial network because it taxes all transactions indiscriminately. A Tobin-like tax is indeed shown to have a limited effect on reducing systemic risk while it decreases transaction volume.

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    We implement a market microstructure model including informed, uninformed and heuristic-driven investors, which latter behave in line with loss-aversion and mental accounting. We show that the probability of informed trading (PIN) varies significantly during 2008. In contrast, the probability of heuristic-driven trading (PH) remains constant both before and after the collapse of Lehman Brothers. Cross-sectional analysis yields that, unlike PIN, PH is not sensitive to size and volume effects. We show that heuristic-driven traders are universally present in all market segments and their presence is constant over time. Furthermore, we find that heuristic-driven investors and informed traders are disjoint sets.

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    Deliverable bonds and conversion factors of JGB Futures (5-year, 10-year & 20-year)read more...

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    A speech entitled “Liquidity Risk Management of Investment Funds" delivered by Ms Julia Leung on 14 June 2016 was posted on the SFC website today.

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    ASIC has today released its assessment report on the listing standards of the Australian Securities Exchange Limited (ASX), which concludes that up to this point in time ASX has met its statutory obligations.read more...

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    From an analysis of the time series of volatility using recent high frequency data, Gatheral, Jaisson and Rosenbaum previously showed that log-volatility behaves essentially as a fractional Brownian motion with Hurst exponent H of order 0.1, at any reasonable time scale. The resulting Rough Fractional Stochastic Volatility (RFSV) model is remarkably consistent with financial time series data. We now show how the RFSV model can be used to price claims on both the underlying and integrated volatility. We analyze in detail a simple case of this model, the rBergomi model. In particular, we find that the rBergomi model fits the SPX volatility markedly better than conventional Markovian stochastic volatility models, and with fewer parameters. Finally, we show that actual SPX variance swap curves seem to be consistent with model forecasts, with particular dramatic examples from the weekend of the collapse of Lehman Brothers and the Flash Crash.

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  • 06/14/16--02:18: CESC Index Report For May
  • Highlightsread more...

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    Accuity, the leading global provider of risk and compliance, payments and know-your-customer (KYC) solutions, now incorporating FircoSoft, the leading global provider of watch list filtering solutions, announced today that it has been recognized as a category leader in the Watchlist Monitoring Solutions RiskTech Quadrant® of Chartis’ 2016 Financial Crime Risk Management report. Accuity acquired FircoSoft in 2014 and together they specialise in providing data, technology and services to assist in the prevention of money laundering, terrorism financing, and financial transaction tampering. This report reflects the integration of Accuity data and FircoSoft software.read more...

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    The Financial Services Regulatory Authority (FSRA) of Abu Dhabi Global Market (ADGM) and the Cayman Islands Monetary Authority (CIMA) signed a Memorandum of Understanding (MoU) putting in place a framework for mutual assistance and exchange of information to foster high standards of regulatory practice and compliance in both jurisdictions.  The agreement was signed on 9 June 2016 by Cindy Scotland, Managing Director of CIMA, and Richard Teng, CEO of FSRA, ADGM.read more...

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    CME Group Executive Chairman and President Terry Duffy will appear before a hearing of the U.S. House Agriculture Committee's Subcommittee on Commodity Exchanges, Energy & Credit today to testify about the global implementation of G-20 Clearing and Trading Execution Requirements and their impact on derivatives markets.read more...

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    We introduce a network valuation model (hereafter NEVA) for the ex-ante valuation of claims among financial institutions connected in a network of liabilities. Similar to previous work, the new framework allows to endogenously determine the recovery rate on all claims upon the default of some institutions. In addition, it also allows to account for ex-ante uncertainty on the asset values, in particular the one arising when the valuation is carried out at some time before the maturity of the claims. The framework encompasses as special cases both the ex-post approaches of Eisenberg and Noe and its previous extensions, as well as the ex-ante approaches, in the sense that each of these models can be recovered exactly for special values of the parameters. We characterize the existence and uniqueness of the solutions of the valuation problem under general conditions on how the value of each claim depends on the equity of the counterparty. Further, we define an algorithm to carry out the network valuation and we provide sufficient conditions for convergence to the maximal solution.

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    We study the problem of a trader who wants to maximize the expected reward from liquidating a given stock position. We model the stock price dynamics as a geometric pure jump process with local characteristics driven by an unobservable finite-state Markov chain and the liquidation rate. This reflects uncertainty about the state of the market and feedback effects from trading. We use stochastic filtering to reduce the optimization problem under partial information to an equivalent one under complete information. This leads to a control problem for piecewise deterministic Markov processes (in short PDMP). We apply control theory for PDMPs to our problem. In particular, we derive the optimality equation for the value function and we characterize the value function as unique viscosity solution of the associated dynamic programming equation. The paper concludes with a detailed analysis of specific examples. We present numerical results illustrating the impact of partial information and feedback effects on the value function and on the optimal liquidation rate.

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    The concept of RRI has emerged as a new framework to be used by the European Commission for research projects. It now lies at the core of the Horizon 2020 programme and is designed to replace current assessment practices focused on ethical review. The book will analyse the shift from ethical review to RRI: what remains of the former, what has been gained? Secondly, it makes a critical presentation of existing ethical reviews from the perspectivesread more...

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