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We show that the conditional distribution of forecasted GDP growth depends on financial conditions in a panel of 11 advanced economies. Financial conditions have a larger effect on the lower fifth percentile of conditional growth—which we call growth-at-risk (GaR)—than the median. In addition, the term structure of GaR reflects that when initial financial conditions are loose, downside risks are lower in the near term but increase in later quarters. This intertemporal tradeoff for loose financial conditions is amplified when credit-to-GDP growth is rapid. Using granular instrumental variables, we also provide evidence that the relationship from loose financial conditions to future downside risks is causal. Our results suggest that models of macrofinancial linkages should incorporate the endogeneity of higher-order moments to systematically account for downside risks to growth in the medium run. (JEL E23, E27, E32, E44)

Medium Access Control (MAC) is the sub-layer of data link layer in the communication OSI model, whose basic function is to control the hardware that interacts with the transmission medium. In Wireless sensor network, the communication is done without any wired connectivity and the sensor nodes get energy through batteries. MAC layer protocol is classified as contention based and contention free. Over the years, these protocols have served efficiently in controlling the data transmission, yet they have some compromising and challenging issues, and data collision is one of them. Contention based protocols are reported to be more vulnerable to collisions, as they allow sharing of channels among transmitting nodes. Collisions result in message delay, data loss and retransmissions, resultantly demanding more energy. Researchers are trying their best to reduce the collisions during transmission. This research is aimed to evaluate the priority-based collision control in contention-based protocol. We present a new technique Energy efficient Collison mitigation MAC (ECM-MAC) protocol to handle the QoS and energy constraints for variety of applications. ECM-MAC uses different contention window and node priority mechanism to get the overall performance of the network. Nodes with most important data or with least residual energy will be allowed to transmit first, thereby minimizing the chances of collisions and maximizing the lifespan of the network. In case of high level of collision, the data traffic will be intelligently reduced to minimize the collisions. A contention-based MAC protocol is evaluated to compare the impacts of suggested mechanism with the standard protocol. It will be useful for designers to develop a better collision avoiding contention-based protocol. In wireless Sensor Networks (WSNs), a sensor node is typically comprising detection unit, transceiver unit (to send and receive the data packets), processing unit and an energy supply unit for energy provisioning. Enhancement in network lifetime is an important and major challenge in WSNs. Since the transceiver unit has more impact on the energy consumption of the node and its reduction results in significant improvement on network lifetime. In our work, we propose an efficient mechanism that prioritized the nodes to get the high performance in quality of service. MAC protocol is targeted to achieve this, as MAC layer has the authority access to the transmission channel. We prioritize the nodes with lower energy to increase the chances of that node to get access of the transmission channel before their power and battery ran out. Our proposed strategy outperforms other protocols in terms of content retrieval Time, retransmissions and energy efficiency.

Sensing and monitoring the physical conditions are basic purposes of Wireless Sensor Networks (WSN). Sensor nodes are the building blocks of WSN and they are battery operated. Battery charging or changing is impossible in harsh areas of deployment, so battery life needs to be maximized by improving the MAC protocol. If the Duty Cycle is selected wisely, it may reduce energy utilizations of a wireless sensor network, but it may impact other factors of WSN as well. Optimum duty cycle for a specific data traffic may not be optimal for other levels of data traffic and it may negatively affect the performance of a wireless sensor network. An adaptive duty cycle algorithm, VTA-SMAC (Variable Traffic-Adaptive Duty Cycle Sensor MAC) is proposed to reduce the problem of idle listening or collisions and to reduce energy consumption of traditional S-MAC protocol. VTA-SMAC algorithm was simulated in Network Simulator NS-2 and simulation results were evaluated to assess the impact of duty cycle variation on performance of S-MAC, particularly energy conservation with condition of varying data traffic. Impact of duty cycle variation in accordance with data traffic were also evaluated on various factors like latency, throughput, collisions, delay, jitter, and packet delivery ratio etc. Trade-off between energy consumption and latency was also studied. Simulation results show that VTA-SMAC improves energy conservation by 19.9 %, 14.4 % and 20.2 % at low, medium, and high traffic, respectively. Latency is also decreased by 10.2 %, 7.5 % and 18.9 % at low, medium, and high traffic, respectively.

Recovering from the COVID sell-off, inflation brekaeven increased sharply in the second half of 2020 while real yields declined materially. The analysis decomposes the inflation breakevens to strip out the role of inflation expectations and risk premia. Despite evidence of upward movement in five-year market-implied inflation expectations after the March sell-off, especially in the United States, such recovery appears to represent more of a reversion toward a preexisting downward trend that emerged after the global financial crisis.

This paper explores the determinants of profitability across large euro area banks using a novel approach based on conditional profitability distributions. Real GDP growth and the NPL ratio are shown to be the most reliable determinants of bank profitability. However, the estimated conditional distributions reveal that, while higher growth would raise profits on average, a large swath of banks would most likely continue to struggle even amid a strong economic recovery. Therefore, for some banks, a determined reduction in NPLs combined with cost efficiency improvements and customized changes to their business models appears to be the most promising strategy for durably raising profitability.

Swing pricing allows a fund manager to transfer to redeeming or subscribing investors the costs associated with their trading activity, thus potentially discouraging large flows. This liquidity management tool, which is already used in major jurisdictions, may also help mitigate systemic risk. Here we develop and apply a methodology to investigate whether swing pricing does in fact help dampen flows out of funds, especially during periods of market stress. Drawing on evidence of first-mover advantage within a group of ‘swinging’ corporate bond funds, we provide policy considerations for enhancing the tool’s effectiveness as a systemic risk mitigant.

Interconnectedness among global systemically important banks (GSIBs) and global systemically important insurers (GSIIs) has important financial stability implications. This paper examines connectedness among United States, European and Asian GSIBs and GSIIs, using publicly-available daily equity returns and intra-day volatility data from October 2007 to August 2016. Results reveal strong regional clusters of return and volatility connectedness amongst GSIBs and GSIIs. Compared to Asia, selected GSIBs and GSIIs headquartered in the United States and Europe appear to be main sources of market-based connectedness. Total system connectedness—i.e., among all GSIBs and GSIIs—tends to rise during financial stress, which is corroborated by a balance sheet oriented systemic risk measure. Lastly, the paper demonstrates significant influence of economic policy uncertainty and U.S. long-term interest rates on total connectedness among systemically important institutions, and the important role of bank profitability and asset quality in driving bank-specific return connectedness.

Cloud computing is a big paradigm shift of computing mechanism. It provides high scalability and elasticity with a range of on-demand services. We can execute a variety of distributed applications on cloud's virtual machines (computing nodes). In a distributed application, virtual machine nodes need to communicate and coordinate with each other. This type of coordination requires that the inter-node latency should be minimal to improve the performance. But in the case of nodes belonging to different clusters of the same cloud or in a multi-cloud environment, there can be a problem of higher network latency. So it becomes more difficult to decide, which node(s) to choose for the distributed application execution, to keep inter-node latency at minimum. In this paper, we propose a solution for this problem. We propose a model for the grouping of nodes with respect to network latency. The application scheduling is done on the basis of network latency. This model is a part of our proposed Cloud Scheduler module, which helps the scheduler in scheduling decisions on the basis of different criteria. Network latency and resultant node grouping on the basis of this latency is one of those criteria. The main essence of the paper is that our proposed latency grouping algorithm not only has no additional network traffic overheads for algorithm computation but also works well with incomplete latency information and performs intelligent grouping on the basis of latency. This paper addresses an important problem in cloud computing, which is locating communicating virtual machines for minimum latency between them and group them with respect to inter-node latency.

In this paper, we attempt to analyse the relationship between house price dynamics and the business cycle. Employing a time-varying transition probability Markov switching framework, we provide empirical evidence that house price growth may prove a useful leading indicator for turning point detection. Focusing on three countries, the US, UK and Spain, we furthermore provide evidence that although potentially informative from an overall perspective in business cycle modelling, the significance of signals contained in house prices may not be symmetric across the identified high growth and low growth states. In addition, we suggest a possible range of values for house price deflation which may trigger a recession the following period.

In this paper, a method is introduced for approximating the likelihood for the unknown parameters of a state space model. The approximation converges to the true likelihood as the simulation size goes to infinity. In addition, the approximating likelihood is continuous as a function of the unknown parameters under rather general conditions. The approach advocated is fast and robust, and it avoids many of the pitfalls associated with current techniques based upon importance sampling. We assess the performance of the method by considering a linear state space model, comparing the results with the Kalman filter, which delivers the true likelihood. We also apply the method to a non-Gaussian state space model, the stochastic volatility model, finding that the approach is efficient and effective. Applications to continuous time finance models and latent panel data models are considered. Two different multivariate approaches are proposed. The neoclassical growth model is considered as an application.

This paper evaluates the robustness of UK bond term premia from affine term structure models. We show that this approach is able to match standard specification tests. In addition, term premia display countercyclical behaviour and are positively related to uncertainty about future inflation, consistent with previous findings for the United States. Premia are robust to correction for small sample bias and the inclusion of macro variables as unspanned factors. Including survey information about short rate expectations, which is a common way of improving identification of affine term structure models, however, results in inferior performance using UK data, as measured by standard specification tests and the economic plausibility of the estimated premia. Finally, we show that imposing the zero lower bound within a shadow rate term structure model does not have a large impact on estimates of long-maturity term premia.

Loss of market access (LMA) is a central element and an exacerbator of balance of payments and fiscal crises. This paper provides an operational definition of LMA, examines the predictive power of potential LMA leading indicators, attempts to determine the likely nature (temporary versus structural) of an LMA episode, and analyzes potential implications of such an assessment on the required degree of adjustment to restore market access. Finally, it highlights the possible application of the methodological framework for identifying emerging risks to market access.

Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold. First, we propose a new SV model, namely SV-GARCH, which bridges the gap between SV and GARCH models: it has the attractive feature of inheriting unconditional properties similar to the standard GARCH model but being conditionally heavier tailed. Second, we propose a likelihood-based inference technique for a large class of SV models relying on the recently introduced continuous particle filter. The approach is robust and simple to implement. The technique is applied to daily returns data for SandP 500 and Dow Jones stock price indices for various spans. © 2014 The Institute of Statistical Mathematics, Tokyo.

Cloud computing is becoming as a main choice computing paradigm for the enterprises. Use of cloud computing technologies is increasing by every passing day. It has great advantages of on-demand computing, scalability, proactive resource management, better application throughput, and enhanced security. Due to its openness and adaptability, it can be a good option for voluntary resource computing among non-profit organizations. In this paper, we present a model for cooperative cloud computing among research institutes and universities using Virtual Cloud concepts. The propose model of Cooperative Cloud Computing encompasses the concepts of cloud federation and volunteer computing and is based on our Virtual Cloud architecture. The cooperative cloud computing can be performed at a global scale among universities and research centers (institutes) located in different continents. In this model, institutes get benefited with a much higher computing power available through cloud federation.

Using a Bayesian structural vector autoregression (TVP-SVAR) with time-varying parameters and volatility we investigate monetary policy in the United States, in particular its interaction with the formation of inflation expectations and the linkages between monetary policy, inflation expectations and the behaviour of CPI inflation. We use Livingston Survey data for expected inflation, measured at a bi-annual frequency, actual inflation, unemployment and a nominal interest rate to estimate the VAR and show the significant changes that have occurred in the responses of these variables to monetary policy shocks or to shocks to expected and actual inflation. In so doing, we generalize the analysis undertaken by Leduc, Sill and Stark (2007) to allow for a more nuanced and detailed look at questions such as the impact of different chairmanship regimes at the Federal Reserve Board, the role of good policy versus good luck, and second round inflation effects. While some of the questions asked have a relatively long history, the methods used to undertake our investigations are very new, and the time-varying structure allows us to offer a more detailed picture. In using these methods we also undertake a substantial technical discussion to unearth the appropriateness of the TVP-SVAR models hitherto estimated in the literature, in particular the role of the choice of priors in determining the outcome of the estimations. As we discuss in the paper, this is an important issue which has remained rather hidden in the discussions surrounding the estimation of TVP-SVARs, yet may have a substantially important role to play in determining the results obtained.

The paper attempts to provide, for housing markets, evidence of 'shift-contagion' at the international level, i.e., regime shifts in the transmission of asset prices during crisis periods. The focus is in particular on UK and Spain. We use a Markov Switching FAVAR framework and regime-dependent impulse response functions. The 'Crisis' regime which we identify endogenously is shown to also correspond to an exogenously determined index of frequency of financial crises in OECD countries, which peaked in the early 1990s and in the more recent Subprime crisis. Furthermore, we find that the response of domestic house price to a shock to a common (global) house price factor during a 'Crisis' regime is relatively more amplified than in a 'Normal' (more tranquil) regime. Less compelling evidence is found for France.

Embedding maximum information in a stego-image with minimum change in its appearance has been a major concern in image-based steganography techniques. In this paper, we present a strategy of attaining maximum embedding capacity in an image in a way that maximum possible neighboring pixels are analyzed for their frequencies, to determine the amount of information to be added in each pixel. The technique provides a seamless insertion of data into the carrier image and reduces the error assessment and artifacts insertion required to a minimal. We justify our approach with the help of an experimental evaluation on a prototypic implementation of the proposed model.

Software review is an effective and best practice to verify the software quality. This paper presents an effective and efficient review process. It overcomes the obstacles facing the current review processes. Current review processes face these obstacles due to their rigid and time consuming nature. Proposed review process introduces twofold review activity, an effectual meeting procedure and course of actions for future improvements. It is an attempt to minimize the inflexible requirements of the review meetings. It aimed at reduction of synchronous review meeting by removing less critical defects before synchronous review meeting. It is evaluated through an experiment and comparative analysis, which has provided effective results.

Real time systems have a characteristic that they should be fault tolerant. In this paper, a fault tolerance mechanism for real time systems is proposed. First a model is discussed which is a modification of distributed recovery block and is based on distributed computing. Then a model is proposed which is based on distributed computing along with feed forward artificial neural network methodology. The proposed technique is based on execution of design diverse variants on replicated hardware, and assigning weights to the results produced by variants. Thus the proposed method encompasses both the forward and backward recovery mechanism, but main focus is on forward recovery.

A wide application of real time systems is safety critical systems. These systems should be highly reliable. Fault tolerance is one of the approaches to achieve reliability. In this paper, a fault tolerance model for real time systems is proposed. This model incorporates the concept of time stamped fault tolerance. This model is based on distributed computing along with feed forward artificial neural network methodology. The proposed technique is based on execution of design diverse variants on replicated hardware, and assigning weights to the results produced by variants. Thus the proposed method encompasses both the forward and backward recovery mechanism, but main focus is on forward recovery. The main essence of the proposed technique is the inclusion of time for decision mechanism