Forward Rate Research Articles

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes' forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.

Interest Rate Markets & The Forward Rate - Discount Factor Relationship Explained

Interest Rate Markets & The Forward Rate - Discount Factor Relationship Explained

Bond Risk Premia: The Information in Really Long-Maturity Forward Rates

Long term forward rates contain information that greatly improves the precision with which expectations of future short rates can be distinguished from risk premia in the term structure. Indeed, in affine models, the slope of the term structure of risk premia for long maturities is very closely approximated by the sum of (i) the slope of the forward rate curve and (ii) a term that depends only of yield volatility and maturity (convexity), two quantities that can easily be estimated independently of the details of model specification. Key to extracting the risk premium information in long-term forward rates is capturing the dynamics of convexity, which requires a model with time-varying volatility. Using a four-factor ATSM, we find that risk premia on long term bonds are almost entirely driven by volatility. Short rate expectations in our model account for a much larger fraction of the volatility of yields than is typically reported, a result that we show is mainly the result of econometric bias in some estimates of Gaussian models. We also show - using Monte Carlo simulation - that including data on long-term yields in the estimation of term structure models greatly improves the precision of estimated values of short rate expectations, term premia and risk premia. Compared with benchmark estimates that use yield data up to 25 years, excluding data on yields longer than 10 years results in the standard errors of both estimated means and volatilities roughly tripling in size.

Modelling exchange rate movements in South Africa: an ARDL application

This study examined the monetary, unbiased forward rate hypothesis (UFRH), and random walk models of exchange rate in South Africa using the auto regressive distributed lag (ARDL) approach. The estimated results show that there is a long-run relationship between variables in each model, suggesting the examined models are able to predict the exchange rate. In the long run, this paper found a sticky-price monetary model with correct signs but restrictions on money supply are violated. However, both the UFRH restrictions and random walk model were upheld. The models were then used to predict forecasts of the out-of-sample period as compared to the random walk. We found that UFRH outperformed other models. In this context, forward rate is predominantly an unbiased estimator of the future spot exchange rate, suggesting that the South African exchange rate market is efficient.

Modelling exchange rate movements in South Africa: an ARDL application

This study examined the monetary, unbiased forward rate hypothesis (UFRH), and random walk models of exchange rate in South Africa using the auto regressive distributed lag (ARDL) approach. The estimated results show that there is a long-run relationship between variables in each model, suggesting the examined models are able to predict the exchange rate. In the long run, this paper found a sticky-price monetary model with correct signs but restrictions on money supply are violated. However, both the UFRH restrictions and random walk model were upheld. The models were then used to predict forecasts of the out-of-sample period as compared to the random walk. We found that UFRH outperformed other models. In this context, forward rate is predominantly an unbiased estimator of the future spot exchange rate, suggesting that the South African exchange rate market is efficient.

Optimization Driven-Based Secure Routing in MANET Using Atom Whale Optimization Algorithm

Mobile ad hoc networks (MANETs) routing is a very challenging task because of the dynamic nature of the network. The linking of provisional communication assures based on the infrastructure of MANET, however, there is no centralised monitoring process for making the routing in MANETs in terms of trust and security. Therefore, the stability routing is not considered, which may break easily in dynamic MANETs. Thus, this paper introduces a trust-based secure routing protocol using the proposed atom whale optimisation algorithm (AWOA), which is the trust-aware routing protocol. The developed atom whale optimisation is utilised to select the optimal route with respect to the trust factors, like average encounter rate (AER), and successful cooperation frequency (SCF), integrity factor, and the forwarding rate. Moreover, secure routing is performed between the nodes using the proposed AWOA. The AWOA is the integration of atom search optimisation (ASO), and whale optimisation algorithm (WOA) that inherits the faster global convergence. The fitness function is newly modelled considering mobility, and trust factors. The proposed AWOA outperformed other methods with a minimal end-to-end delay of 0.0083 sec, maximal packet delivery ratio (PDR) of 97.73%, and the maximal throughput of 85.05% respectively.

Optimisation driven-based secure routing in MANET using atom whale optimisation algorithm

Mobile ad hoc networks (MANETs) routing is a very challenging task because of the dynamic nature of the network. The linking of provisional communication assures based on the infrastructure of MANET, however, there is no centralised monitoring process for making the routing in MANETs in terms of trust and security. Therefore, the stability routing is not considered, which may break easily in dynamic MANETs. Thus, this paper introduces a trust-based secure routing protocol using the proposed atom whale optimisation algorithm (AWOA), which is the trust-aware routing protocol. The developed atom whale optimisation is utilised to select the optimal route with respect to the trust factors, like average encounter rate (AER), and successful cooperation frequency (SCF), integrity factor, and the forwarding rate. Moreover, secure routing is performed between the nodes using the proposed AWOA. The AWOA is the integration of atom search optimisation (ASO), and whale optimisation algorithm (WOA) that inherits the faster global convergence. The fitness function is newly modelled considering mobility, and trust factors. The proposed AWOA outperformed other methods with a minimal end-to-end delay of 0.0083 sec, maximal packet delivery ratio (PDR) of 97.73%, and the maximal throughput of 85.05% respectively.

Short Rate Dynamics: A Fed Funds and SOFR perspective

The Secured Overnight Funding Rate (SOFR) is becoming the main Risk–Free Rate benchmark in US dollars, thus interest rate term structure models need to be updated to reflect the key features exhibited by the dynamics of SOFR and the forward rates implied by SOFR futures. Historically, interest rate term structure modelling has been based on rates of substantially longer time to maturity than overnight, but with SOFR the overnight rate now is the primary market observable. This means that the empirical idiosyncrasies of the overnight rate cannot be ignored when constructing interest rate models in a SOFR–based world. As a rate reflecting transactions in the Treasury overnight repurchase market, the dynamics of SOFR are closely linked to the dynamics Effective Federal Funds Rate (EFFR), which is the interest rate most directly impacted by US monetary policy target rate decisions. Therefore, these rates feature jumps at known times (Federal Open Market Committee meeting dates), and market expectations of these jumps are reflected in prices for futures written on these rates. On the other hand, forward rates implied by Fed Funds and SOFR futures continue to evolve diffusively. The model presented in this paper reflects the key empirical features of SOFR dynamics and is calibrated to futures prices. In particular, the model reconciles diffusive forward rate dynamics with piecewise constant paths of the target short rate.

Advanced Yield Curve Calibration, Mixed Interpolation Schemes & How to Incorporate Jumps and the Turn-of-Year Effect

Yield curves are used to imply the forward rates and discount factors from market tradable instruments and are required to discount future cash flows and evaluate the price of all financial contracts. Not all instruments can be included in the yield curve calibration or fitting process, hence we interpolate any gaps and missing forward rates. In this paper we discuss interpolation best practise and how to incorporate market jumps and turn of year (ToY) effects into yield curve calibration.

Routing Optimization Algorithm of Spatial DTN Network Based on Multiattribute Decision

Delay or stop (DTN) tolerance space network is considered to be a technology that can trade with a limited or restricted field communication area. In the space DTN network architecture, routing is very important. This article is based on the best‐in‐class multiattribute decision‐making DTN network routing optimization algorithm. The goal is to use the optimized DTN network routing algorithm multiattribute decision‐making algorithm and conduct a more in‐depth study on the field information mechanism multipath decision algorithm and network path. Aiming at the problems of long link delay and frequent link interruption of current deep‐space communication characteristics, and solving the impact on communication caused by system performance degradation. This paper adopts the multiattribute decision‐making model algorithm to construct simulation tests, introduces the DTN network architecture, and analyzes the four attributes of the multi‐attribute decision‐making link bandwidth, node data forwarding rate, link establishment delay, and network error rate. The experimental results show that, in the spatial information network, the high‐speed movement of nodes and the highly dynamic nature of the network make the deep‐space network topology unstable and long communication delay; the change of the link mode and switch connection in the network will cause the routing table to update. This routing update method requires the exchange of topology information between all networks; when the forwarding rates of adjacent nodes are 100%, 90%, 95%, and 70%, nodes can only perform routing independently when they meet. The decision attribute algorithm has obvious changes to the attributes of any node in the spatial DTN network. The most obvious is that the link establishment delay has reached the standard of 5 or more.

Indicator variables for inflation expectations in the Euro area

In this paper, we model the Euro area market-based inflation expectations extracted from the inflation-linked swaps, and study the macroeconomic information embedded in expected inflation. First, we estimate the Gaussian affine term structure model to decompose the forward ILS-implied inflation rate into inflation expectations and inflation risk premium at one-, two- and three-year horizons. Secondly, from a large panel of macroeconomic series we identify the most significant indicator variables for inflation expectations using the elastic net modification of the LASSO regression. Finally, we measure partial contributions of individual indicator variables to the changes in inflation expectations. Our findings reveal that across horizons considered inflation expectations are correlated to the measures of current inflation of the overall price level and price level of services, the unemployment rate, and the Euro exchange rate. The identified indicators provide a useful information about the evolution of inflation expectations with different intensities at different horizons.

Indicator variables for inflation expectations in the euro area

In this paper, we model the Euro area market-based inflation expectations extracted from the inflation-linked swaps, and study the macroeconomic information embedded in expected inflation. First, we estimate the Gaussian affine term structure model to decompose the forward ILS-implied inflation rate into inflation expectations and inflation risk premium at one-, two- and three-year horizons. Secondly, from a large panel of macroeconomic series we identify the most significant indicator variables for inflation expectations using the elastic net modification of the LASSO regression. Finally, we measure partial contributions of individual indicator variables to the changes in inflation expectations. Our findings reveal that across horizons considered inflation expectations are correlated to the measures of current inflation of the overall price level and price level of services, the unemployment rate, and the Euro exchange rate. The identified indicators provide a useful information about the evolution of inflation expectations with different intensities at different horizons.

Discrepancy between Forward and Reverse Seepage Characteristics in a Single Rough Fracture

Under the uniform seepage boundary condition, the forward and reverse seepage flow rates will vary widely. In response to this phenomenon, this paper studies the mechanism of the difference in seepage characteristics between the forward and reverse directions from the fracture profile’s roughness, Darcy and non‐Darcy seepage, and distribution of eddy currents. First of all, wavelet transform was used to decompose and reconstruct the single rough fracture for 8 times, and then 9 fracture models with various roughness degrees were constructed. Then, Finite Volume Method was used to simulate the seepage in the original and approximate models of the Reynolds number varying from 0.001 to 1000. The results show that the nonlinear seepage is necessary for the difference between the forward and reverse seepage characteristics of the single rough fracture. The mechanism of the discrepancy between forward and reverse nonlinear seepages is the diverse shapes and distribution of the eddy current generated by the forward and reverse seepage. The secondary roughness provides space for the existence and development of the eddy current. The secondary roughness is the geometric basis of the discrepancy between the forward and reverse seepage characteristics.

Explanatory Elements of the Deformations of the Term Structure of Interest Rates

The interest rate curve has often been defined as a graphical representation of the yield offered by bonds of a single issuer according to their maturity, from shortest to longest. The best-known yield curve, which serve as a benchmark for the entire bond market in a given country, is that of the government bonds. Indeed, the shape of the interest rate curve on sovereign bonds provides information on investor’s expectations on the risk of default of the issuing state as well as on the level of inflation and future interest rates. As a result, it is a good indicator of the economic and financial health of the issuing country. In a stable economic environment marked by low inflation and sustainable public debt, bond yields increase with the maturity of securities. This is explained by the fact that the longer the maturity, the greater the risk of events occurring that could adversely affect the value of the bond security. Indeed, the further in time one goes, the greater the uncertainty about the issuer’s repayment capacity or about the level of interest rates. Under these conditions, investors require a risk premium to lend on long maturities compares to short maturities. However, as government loans are generally considered to be loans for which repayment is certain, the associated risk premiums are low or almost zero. On the longest maturities (20 to 50 years), the default risk as well as the interest rate or inflation risks can be considered to be broadly identical. This is the reason why the yield curves on government bonds of most "so-called" developed countries have an increasing and concave shape. This article highlights the explanatory factors of the structure of forward interest rates by proposing a multifactorial model of asset valuation that is at the same time exhaustive, simple, intelligible and realistic. The underlying objective is to propose techniques hedging against the risk of interest rates more effective than traditional techniques, especially since we live in an extremely sensitive and changing environment because of the consequences of covid-19 on economies. The contribution is part of the research movement aimed at improving the multifactorial models of the yield curve and to overcome the shortcomings of the techniques traditionally used.

The Association of Opening K-12 Schools and Colleges with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis

This paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States. Using data on foot traffic and K-12 school opening plans, we analyze how an increase in visits to schools and opening schools with different teaching methods (in-person, hybrid, and remote) is related to the 2-weeks forward growth rate of confirmed COVID-19 cases. Our debiased panel data regression analysis with a set of county dummies, interactions of state and week dummies, and other controls shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the positive association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These results have a causal interpretation in a structural model with unobserved county and time confounders. Sensitivity analysis shows that the baseline results are robust to timing assumptions and alternative specifications.

Abnormal Brain Bioenergetics in First-Episode Psychosis.

BackgroundConverging evidence indicates impaired brain energy metabolism in schizophrenia and other psychotic disorders. Creatine kinase (CK) is pivotal in providing adenosine triphosphate in the cell and maintaining its levels when energy demand is increased. However, the activity of CK has not been investigated in patients with first-episode schizophrenia spectrum disorders. MethodsUsing in vivo phosphorus magnetization transfer spectroscopy, we measured CK first-order forward rate constant (kf) in the frontal lobe, in patients with first-episode psychosis (FEP; n = 16) and healthy controls (n = 34), at rest. ResultsCK kf was significantly reduced in FEP compared to healthy controls. There were no differences in other energy metabolism-related measures, including phosphocreatine (PCr) or ATP, between groups. We also found increase in glycerol-3-phosphorylcholine, a putative membrane breakdown product, in patients. ConclusionsThe results of this study indicate that brain bioenergetic abnormalities are already present early in the course of schizophrenia spectrum disorders. Future research is needed to identify the relationship of reduced CK kf with psychotic symptoms and to test treatment alternatives targeting this pathway. Increased glycerol-3-phosphorylcholine is consistent with earlier studies in medication-naïve patients and later studies in first-episode schizophrenia, and suggest enhanced synaptic pruning.

AMFR-W Numerical Methods for Solving High-Dimensional SABR/LIBOR PDE Models

In this work, we mainly develop a new numerical methodology to solve a PDE model recently proposed in the literature for pricing interest rate derivatives. More precisely, we use high-order-in-time AMFR-W-methods, which belong to a class of W-methods based on approximate matrix factorization (AMF) and are especially suitable in the presence of mixed spatial derivatives. High-order convergence in time allows larger time steps, which, combined with the splitting of the involved operators, highly reduces the computational time for a given accuracy. Moreover, the consideration of a large number of underlying forward rates makes the PDE problem high dimensional in space, so the use of AMFR-W-methods with a sparse grid combination technique represents another innovative aspect, making AMFR-W more efficient than with full grids and opening the possibility of parallelization. Also, the consideration of new homogeneous Neumann boundary conditions provides another original feature to avoid the difficulties associated to the presence of boundary layers when using Dirichlet ones, especially in advection-dominated regimes. These Neumann boundary conditions motivate the introduction of a modified combination technique to overcome a decrease in the accuracy of the standard combination technique.

NYU Yield Curve Seminar - An Overview of Yield Curve Calibration & LIBOR Reform

In this PowerPoint presentation we give an overview of yield curves, show how they are modelled and calibrated and give a brief overview of LIBOR reform. Firstly we explain how to calibrate curves to imply forward rates & discount factors. Secondly, we outline the interpolation, optimization and solving process; showing how to calibrate curves in such a way to capture the necessary risk metrics required to compute analytical risk and rebuild curves for ultra-fast performance. Thirdly we give an outline of the LIBOR reform to ARR benchmarks and explain the new features of ARR yield curves. We conclude with a step-by-step tutorial using an Excel workbook to demonstrate how to calibrate a yield curve, price swaps and compute real-time bucketed risk analytically.

Identifying key node in multi-region opportunistic sensor network based on improved TOPSIS

The topology of multi-region opportunistic sensor networks is evolving, and it is difficult to identify the key nodes in the networks by traditional key node identification methods. In this paper, a novel method based on the improved TOPSIS method is proposed to identify the key node from the ferry node. The dynamic topology information is represented by the graph model which is modeled by the temporal reachable graph. Based on the temporal reachable graph, three attributes are constructed to identify the key node, which are average degree, betweenness centrality and message forwarding rate. The game theory with a combination weighting method is employed to combine the subjective weight and objective weight, so as to obtain the combined weight of each attribute. The TOPSIS method is improved by the combined weight. The key node is identified by the improved TOPSIS. The experiments in three simulation situations show that, compared with the TOPSIS method and MADM_TOPSIS method, the proposed method has better accuracy for the key node identification in the network.

Pergerakan kurs spot dan kurs forward pada future spot

The purpose of this research are to determine the movement of the spot rate on the future spot, the movement of the forward rate on the future spot, the movement of the spot rate and the forward rate on the future spot. This research uses two quarters, that is quarter I to quarter II of 2019. This research uses regression models for the forecasting method. The variables are the spot rate, forward rate, and future spot. Samples were obtained from BI. The regression model was tested using the classical assumption test. The results of the data analysis show that the spot rate has a significant movement on the future spot, this is evidenced by the regression coefficient value of -2.9938 and a probability value of 0.0042. The forward exchange rate also shows the results of significant movements on the future spot, as evidenced by the regression coefficient value of -2.9581 and a probability value of 0.0046. The spot rate and forward rate together have an effect on the future spot, as evidenced by the results of statistical analysis with an F-Statistical value of 5.5895 and an F-Statistical probability value of 0.0063.